Defining your future is a strategy challenge, not a technology one

The session kicked off by talking about what it means to ‘define your future’. Joe stated that we’re fundamentally looking at data strategy – I.E. how an organisation should look to unlock value from data, automation, and AI. While technologies are more powerful and accessible than ever, value doesn’t emerge by chance.

It’s not enough to simply ‘get started’ with tools, because impact is rarely delivered without a clear plan. That’s where strategy comes in. It provides direction, prioritisation, and credibility, three elements at the core of moving any organisation forward. So wherever yours is on its maturity journey, value should be the primary driver.

“The context around defining your future is that we live in an incredibly exciting era in the field of data automation and AI. 

“We’ve never had more technologies available to us. We’ve never had more skills and awareness of this. There’s never been more opportunity. 

“But that comes with a double-edged sword – there’s also disruption. We know that our business models need to change and we need to bring our people along on that change curve. 

“So when I say ‘define your future’ I’m really talking about strategy – and how we drive value from data and AI.”

Joe Horgan, Principal Consultant – Data Strategy and Digital Transformation at Oakland Everything Data

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Value can’t be broadly defined – it’s context specific 

There’s no one size fits all or universal definition of value in data and AI. What matters depends on an organisation’s purpose, industry, and strategic goals. 

For instance, financial regulators focus on risk, harm prevention, service quality, and scale, whereas financial organisations prioritise fraud, risk, and customer insight. Universities emphasise student experience and research impact, which differs from the focus on asset optimisation, predictive maintenance, and digital twins in utilities.

How to make meaningful change for data and AI success

Rather than merely ‘aligning’ data and AI initiatives to business strategy, organisations should aim to actively drive strategy through data and AI. Increasingly, these capabilities are at the foundation of wider business activities, like digital transformation, customer experience, and operating models. But you can’t get these off the ground without two important elements: organisational outcomes, which require clarity and planning, and leadership and investment reality.

“Having a joined up plan to unlock value is one thing. Being able to communicate that outwards is crucial if you want to get the agenda moving at scale.”

Joe Horgan

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Remember: A complete data and AI strategy is more than a vision slide

The panellists went on to highlight one of the most common recurring failures in data and AI roadmapping: mistaking a high-level vision for a full strategy.

Organisations must account for several elements to define the future, including:

1. Vision – where the organisation wants to go and why, i.e. the value you think it’ll bring.
2. Case for change – the gaps and constraints the organisation has, which show why investment is needed.
3. Use cases – what are the tangible problems you can solve?
4. Strategy and roadmap – how you’re going to make this change happen, what will be delivered and when.
5. Target operating model – translating the strategy into reliability via people, processes, and technology.

Without this full answer, you risk losing momentum and confidence – fast.

Challenges are normal and to be expected

No organisation has ever started with perfect data or platforms – so don’t pressure yours to, either.

“I’ve never met an organisation who says ‘we are absolutely happy with every aspect of our data’. (And I’ve met literally hundreds!)” Joe Horgan

Joe spoke of a range of typical challenges encountered on the road to AI and data success, from data silos and fragmented systems to platform or infrastructure limitations, cost concerns (especially around cloud and GenAI), and data quality, security, and trust issues. 

The advice? It’s absolutely fine to have challenges. Just remember they aren’t failures, but the very reason to define a strategy. A clear baseline enables realistic expectations and justifies investment.

Strategic pitfalls to avoid for data and AI success

However, there are mistakes to be mindful of – ones that we’ve seen derail the success of a strategy, time and time again.

1. Unclear value – we’re talking vague benefits with no measurable outcomes rather than what metrics will move and what use cases we can solve, for example.
2. Fragmented strategies – ones that are disconnected from other initiatives involved in digital, operating models, or user experiences.
3. Partial answers – presenting a vision without a plan/substance behind it, like having a business case without a narrative.
4. ‘Jam tomorrow’ roadmaps – where technical leaders evangelise a strategy that loses sight of what the wider organisation is interested in.
5. Technology bias – strategies that read like shopping lists rather than transformation plans that account for people, processes, and use-cases.

If you take anything away from this webinar round-up, it should be this: successful strategies address people, process, culture, and experience, not just tools.

“Putting a comprehensive and compelling strategy together isn’t easy. That’s one of the reasons people turn to Oakland Everything Data for help.” Joe Horgan

Five principles for AI and data success

Joe moved on to translate what this looks like in real life and explain our approach to defining a data and AI future. It’s based on five principles:

1. Value first, value fast. Anchor everything in value and deliver early ‘reasons to believe’.
2. Build capabilities, not just solutions. Remember that sustainable value comes from people, process, and technology working together. If you want your strategy to stick and add value for the long run, think about the foundations and capabilities you’re building in the organisation.
3. Integrated transformation. We don’t give soloed answers and data and AI automation – they must connect with, and fit into, wider digital, customer, and organisational change.
4. Balanced delivery. Combine short-term value with long-term foundational improvement – AKA value first, and value fast.
5. Don’t forget the story. Data strategy is a storytelling challenge because we’re trying to explain the relevance of complex technical activities with fast-moving technologies in a jargon-heavy field. You need to spend time thinking about the best way to narrate this journey so it resonates beyond technical teams.

Causes of data and AI failure

The panel went on to talk about how taking a balanced approach is better than an extreme one. For instance, years-long ‘big bang’ programmes that are delivered too late, which leaves everyone fed up. Or endless proofs of concept that never actually influence business reality.

What you’re after is a more balanced approach, one that shows value in there here and now. When your people realise the value early on, it’s far easier to build momentum and get buy-in from stakeholders to gain the steady investment you need in core capabilities.

Real world experience from Softcat

The difference between poor, average, and great data strategies

So, how do you know if the data strategy you’ve been working on is actually set up for success? Joe stated that most organisations ‘do data, but only the great ones turn it into a competitive advantage that feels effortless to the end user’. He went on to share what differentiates a poor strategy from an exceptional one.

Poor data strategies tend to be very reactive to one problem. They’re siloed, low trust, spreadsheet-driven, and without much thought about long-term outcomes. They become better/’good’ with centralised platforms, better visibility, and some governance. But there’ll be uncertainty about next steps – a core differentiator between a ‘good’ and ‘great’ data strategy, which can be characterised with:

• Intuitive, trusted, and embedded data
• Data-literate users
• Decisions powered by AI and automation

Ultimately, a great strategy turns data into the engine of the business. (See how we designed a data strategy and roadmap for RAW Charging that empowered automation and scale, improved data culture, and drove timely, consolidated insights: RAW Charging case study.)

To wrap up

As the webinar came to a close, Joe Ryan, and James reiterated the key takeaways from the session on data and AI success. 

1. Data and AI are now core organisational capabilities, not optional or niche.
2. All data and AI decisions should be guided by value first and value fast.
3. A successful roadmap balances quick wins with long-term foundations.
4. Defining the future requires a complete, credible story, not just a vision.
5. The biggest risk is doing nothing. So if you’re in this boat, reach out to our team for friendly guidance on where – and how – to get started.

Q&A

How to prove AI ROI?

Speaking pragmatically, remember that not all users want to understand the technology. They want to understand the impact on them and what they’re going to get out of it, so try and relate it back to them and their strategic intent as much as you can. Early AI ROI can usually be found in/proven by:

• Focusing on areas with better data to unlock some early value
• Using GenAI to highlight and fill data gaps, so the data can be put to better use
• Fixing data that undermines high-value processes

What’s the role of citizen development in data and AI success?

Citizen development should be enabled, not blocked! James says, “What we’ve started to do at Softcat is bring in the concept of a ‘centre of excellence’.” It’s where Softcat unites overall governance, principles, and ownership in a central place to allow users (citizen developers) within the business to work with the excellence team to make sure they’re abiding by Softcat’s principles.

The post How to Build a Roadmap for Data & AI Success  appeared first on Oakland.

So, how do you make sure your generative AI project actually brings value and still exists 12 months later? I’ve written out some learnings from our experience scoping, building and deploying AI solutions at Oakland Everything Data. (I then ran this blog through CoPilot to make sure it read okay, but I decided to ignore a lot of the suggestions. I secretly worry all content on the internet will sound the same in a few years, but Nicola, our Marketing Manager, assures me it won’t…)

What is Generative AI?

Generative AI is an artificial intelligence that creates original, new content. Think text, images, videos, music. It learns from huge datasets of information that already exist, spotting patterns and structures in the data to then produce novel outputs. When using a GenAI model, users give it a prompt. It then creates the original content off the back of that – ChatGPT is the most well-known example and one you’ve probably already used.

Solve the Right Problem with the Right Tool

So, how should businesses choose the right use-case to solve with AI? 

Firstly, think about what you’re trying to do and consider if you even need AI. Hype is powerful and budgets often get approved on buzzwords, like GenAI. But, spending money on a tool which will make a ‘pretty interesting’ case study and nothing else won’t lead to a successful, money saving solution. 

At Oakland, we often run workshops with clients to discuss where and how they could use AI in their business. The shortlist usually ends up at around 10 or 20 project candidates. More often than not, 80% of these problems are just general or more advanced analytics problems, with some golden AI problems buried amongst them. This isn’t an issue as they could be brilliant candidates in their own right – maybe even solvable using some element of AI to enhance the  output. But getting the right problem to solve is important.

“Choose a challenge which will have a high impact on something lower risk. Avoid regulated areas, critical workflows, or things which require more exact output. Look for problems which are time consuming, automatable, and areas where AI can add to existing human processes, rather than fully replacing them.”

Jack Evans, Principal Consultant

Making work easier so individuals can achieve more or be more impactive is the key, as is choosing an existing process to improve rather than creating something completely new.

AI Solutions can be More than Chatbots

There’s nothing wrong with a good chatbot. In fact, they’re often an ideal way for a user to interact with an AI model. The problem is that people often think this is the best and only way to use AI within an organisation, because it’s the most visible format for LLMs. But they’re not the only way! 

AI can come in many different forms. The best solutions use the best parts of different tech to solve a problem and have a bigger impact. Examples include:

• Using AI alongside machine learning, intelligent forecasting, or even Power BI reports to summarise findings
• AI automatically generating or updating documents
• AI acting as the ‘glue’ between processes, triggering automation workflows between systems

(Agentic systems are the dream to bring real value and change through multi-step autonomous actions rather than just Q&A, but these can get complicated quickly. So let’s take things slow!) 

Just thinking that the only way to surface AI is through a chatbot is limiting. Working through a problem and exploring the different outputs and user interaction are important to building something brilliant.

Appropriately Scale the Generative AI Solution

So, you have a decent user case in mind and you want to start developing an AI solution. I feel like this wouldn’t be a good blog if I only called out the need to “plan a lot” (I feel like that’s obvious for any IT project) – but the main thing is to plan with some specific considerations in the AI space.

Plan for future phases, with a high-level roadmap of where to begin (smaller aspirations) to what comes next (bigger aspirations). This will help from a cost management and output standpoint. It’s also a great way to highlight the need for iterative development and the need for potential commitment as you do with any proof of concept IT project. 

A PoC will not (and should not) solve all problems or immediately become a production solution without extra steps. Start with small, accessible, non-sensitive, and good quality data sources to minimise potential obstacles – otherwise, you could trip at many hurdles along the way. 

“Remember that Generative AI works best on firm data foundations. It’s vital to make sure that you’re confident in both the quality of the data it’s based on, as well as the types of insights you want.”

Jack Evans, Principal Consultant

Technical Architecture Considerations

Tooling in this space is evolving fast. Very fast. You don’t want to find out the method you’ve chosen is out of date or not suitable before you’ve even finished the development. Seek advice from vendors on the right approach, keep an eye on roadmaps and research as best as you can to make things scalable and extensible.

Many pilots do prove the concept, but fail to take the project through to the production stage. This is important, because getting a solution through to production means it has more structure and controls, whilst also having more chance of being adopted. 

Governance, technical design authorities, and processes are not always ready to handle AI solutions. Plan for this potential lag and guide people through the ins and outs of the project – whether this be explaining potential concerns around security, data privacy, or Skynet scenarios, or just walking through the selected tech architecture.

Measure GenAI Project Success Accordingly (and be Realistic)

With the pressure to justify budget requests, aspirations can often overreach – especially when discussing new technology and the promises that follow. AI has a reputation for being a way to find the answer to many corporate problems, but this is not always the case! Asking a chatbot to “find and fix all the problems” is unlikely to yield the best results out of the box.

Success measures are, however, important to track progress. Without these guides (as with any iterative process), it’s hard to know when the aims have even been met and when to stop.

Success is often defined as producing relevant benefits. Yet these can be difficult to quantify with AI and regularly aren’t directly financial. For instance:

• A greater understanding of information
• New, smoother, more efficient processes
• Improved transparency to data

If you choose particular use cases, it can be easier to align to financial measures – but that requires a specific type of problem you’re solving. On top of this, it’s a good idea not to promise the world. Keep any targets within the realms of possibility (e.g. 10% increased throughput of tickets).

Tailor, Build, and Test GenAI – A Lot

Building an AI solution requires more than just adding data to an LLM and asking it a question. Don’t get me wrong, you would likely get an answer, but it might not be the level of detail you’d like. The “out of the box” experience for a lot of LLMs takes time and configuration to improve and develop. Prompt engineering is very much a necessary commitment to make sure the AI model has everything it needs to answer the best it can. Allocate time to this activity and test, iterate and then test some more. 

As an example, setting how much speculation an AI model should be able to do and clarifying terminology will help tailor a response. 

• Generating legal documents? Dial down the free will and force the model to reference real facts. 
• Summarising historical spend? A bit more flexibility could be given to provide more options on how the user can query the AI model.

This work helps the user feel like the answer was written for them with the context of how they wanted the response. If they wanted a general answer, they would’ve used ChatGPT or CoPilot.

GenAI Integration and Business Change

As with any IT project, developing the solution is only half the problem – getting people to use it in the way it was intended is a whole other challenge. Proof of concepts regularly have less time allocated to business change, but this makes sense given the smaller scope. The issue is often around the user case being selected, because if this is right, then it really helps with adoption later on.

“If you’re building a generative AI solution, you should aim to look at existing processes to augment and improve with AI. Developing a whole new process can often add complications, as it’s easier to jump onto the existing structure and setup that users are familiar with.”

Jack Evans, Principal Consultant

The other consideration is getting users to adopt the fancy new AI solution which has been developed. Chatbots, as an example, often rely on user input to thrive and if no suggestions are given, then a user can sometimes have no idea what to ask. This results in the blinking cursor of doom facing a user, with a powerhouse of wonder and capability sitting within a model that someone doesn’t know how to interact with. 

Bake suggestions and prompts into the tool, plan a process to push insights to users wherever possible, and help guide users to find what they want easier. People will always need upskilling, but the more heavy lifting the solution can do in this space, the better.

Impactful Generative AI Projects

With any luck, thinking about some of the above will increase the chance that your AI solution will exist six months after development starts. Patience is already wearing thin and we’re seeing an increased push of ROI reflections creeping into the thoughts of budget holders. 

“Just build some AI” needs more consideration and, unfortunately, doesn’t hold the same weight for immediate £££ signoff and guaranteed efficiencies once built. If you’re not careful, it will simply be added to the pile of novelty solutions that didn’t actually save any money or improve any processes. Or worse, it was everything everyone asked for but no one knows it exists or how to use it!For support with scoping out a meaningful generative AI user-case for your organisation, please speak to us.

The post Generative AI: How to Make Your Project a Success appeared first on Oakland.

As ever, Richard brought his no-nonsense home truths to the conversation, which you can catch up on below. AI and business topics they dived into include: 

• What it really means to “liberate data” in the age of AI
• Why hype-driven adoption can lead to more headaches than headlines
• How organisations can get back to basics to unlock genuine business value from AI

True to form, his trademark mix of practical insight, a dash of scepticism, and the occasional hammer analogy kept the discussion around AI for business grounded in real-world experience.

“Let’s stop looking for things to hit with the AI hammer – and start with what actually adds value.”

Richard Corderoy, CEO of Oakland Everything Data

No More AI Fairytales

If you’ve been to any conference lately, you’ve probably heard the AI hype machine in full swing. 

• “It’s the future!” 
• “It’ll change everything!”
• “If you’re not doing it, you’re already behind!”

Richard says, “Honestly, I think we’ve lost our heads a bit. Everyone started with, ‘I have a hammer, what can I hit?’ and, funnily enough, that causes a lot of mess and not much actual value.”

For Oakland Everything Data, we believe the question isn’t “Should we do AI?”. It’s “Why do we need it, and what are we trying to achieve?”.

Value First – Start at the End

Oakland’s mantra is simple: start with value. We’re so passionate about value that it is one of our five principles for creating lasting data impact. 

“None of this matters until someone makes a better decision, a customer gets a better service, or a process actually changes for the better,” Richard explains. “So let’s start at the end –  what do you want to achieve – and work backwards from there.”

That means AI isn’t the starting point. It’s a tool, one of many, to help solve a business problem. And in Oakland’s experience, most of the time the real blockers aren’t technological at all. They’re about messy processes, unclear ownership, or bad governance.

Why AI Projects Stumble

When asked what’s holding big firms back, Richard doesn’t hesitate: “Data complexity. Even mid-sized organisations are drowning in it. They’ve tried a few tech fixes, maybe moved to the cloud, but haven’t got the full value they expected. Then AI comes along with all this promise – and panic sets in.”

He sees it often: the fear of being left behind meets uncertainty about where to start. Add pressure to spend wisely, and you’ve got a recipe for half-baked experiments that never make it to production.

Agentic AI – The “Smart Intern” Approach

One of Richard’s favourite concepts is Agentic AI – not a giant all-knowing brain, but a network of small, specialised AI “agents” that each do one job well.

“Think of them as smart interns. You wouldn’t hire 50 people to read and categorise all your product descriptions, but you can get a couple of agents to do that. They’re not free but they’re a lot cheaper than a human team.”

Richard Corderoy, CEO of Oakland Everything Data

The trick? String these agents together so they can collaborate much like people in a process and let them handle repetitive, time-heavy work so humans can focus on higher-value thinking.

A Real-World Example

Richard shares a client project he’s particularly proud of. A large sales organisation needed to recategorise tens of thousands of products into new business-defined categories.

“The old way? Interns Googling products, reading PDFs, updating records… and then doing it all again 50,000 times. Painful.”

Instead, Oakland built a team of AI agents:

• One read and extracted existing product data.
• Another searched supplier websites.
• Another analysed documents.
• A “coordinator” agent pulled the inputs together and made the final categorisation.

The result? Accuracy jumped to 98%, manual effort dropped drastically, and the process now runs automatically for every new product.

“It’s not about perfection. If a human team had a 5–10% error rate, we’d accept it. So why demand 100% from a system? Set sensible tolerances and move on.”

Richard Corderoy, CEO of Oakland Everything Data

Governance Without Red Tape

Richard is clear: governance matters, but it can’t become a three-year paperwork exercise.

“We’ve all seen those rooms with shelves of process binders no one looks at. That’s governance done wrong. The right level of governance depends on what you’re governing and the risk involved. And you can improve governance while delivering value, you don’t have to wait years before you start.”

Use our blog to understand more about data governance in the age of AI.

Ethics – Don’t Hold AI to a Higher Standard than People

When the conversation turns to AI ethics, Richard challenges a common double standard: “We say we don’t want bias in AI, but every human decision-maker has bias shaped by where they’re from, their education, their experience. AI shouldn’t get a free pass, but let’s not pretend bias is new.”

Oakland’s approach is to help clients define what “ethical” means for them, pick use cases with manageable risk, and design governance around that.

Richard’s Advice for Businesses Feeling Overwhelmed

1. Stop talking about AI as if it’s one thing. 

Decide what type of AI you’re talking about and why it matters to you.

2. List your biggest problems without a tech person in the room. 

Then figure out which ones could be solved with AI.

3. Accept “good enough” in the right contexts.

 Perfection is a luxury, value comes from speed and integration.

4. Build in small steps. 

Prove value early, then scale.

“It’s like electricity – when it first came along, the lightbulbs were rubbish, but the potential was huge. We didn’t throw it out, we found the right applications. AI’s the same. It’s not magic – it’s just another tool. Let’s use it properly.”

Richard Corderoy, CEO of Oakland Everything Data

“It’s like electricity – when it first came along, the lightbulbs were rubbish, but the potential was huge. We didn’t throw it out, we found the right applications. AI’s the same. It’s not magic – it’s just another tool. Let’s use it properly.”

Richard Corderoy, CEO of Oakland Everything Data

For all of Richard’s wisdom, please watch or listen to the full webinar. And to understand how we can support practical AI for your business, please contact our friendly team.

The post Practical AI for Business: Cutting Through the Hype with Richard Corderoy appeared first on Oakland.

Oakland is a data consultancy with a 40-year legacy rooted in rigorous research and operational excellence. We wanted to investigate the UK’s readiness for AI, so we embarked on this study with YouGov’s help to help us cut through the hype. 

Research has always been a cornerstone of keeping up with modern business. We centre everything we do around our customers rather than relying on the perspectives of technology vendors and analysts. The report focuses squarely on businesses navigating the realities of integrating generative AI into their operations. Here’s what we discovered – and why it matters.

Generative AI The Hype vs. Reality Gap

A disconnect between potential and practical application marks the generative AI landscape. Businesses are awash with promises of revolutionary capabilities, yet many lack the foundational skills, processes, and AI governance frameworks necessary to make those promises a reality. Our report supported what we’ve found on the ground from speaking to many organisations: most companies aren’t yet in a position to really take advantage of the technology. 

This isn’t something we are surprised by, and it is, in fact, something we’ve seen before; buzz around new technologies often masks the complexity of real-world implementation. Businesses must navigate the technical challenges and the organisational readiness to adapt.

This mismatch is particularly evident in the UK, where many still grapple with fundamental data challenges in their everyday jobs. From data quality issues to incomplete data pipelines to insufficient expertise, the prerequisites for successful generative AI adoption remain unmet for many.

Generative AI’s Two Waves of Innovation

Oakland’s findings align with a broader observation about technological revolutions: they unfold in two distinct waves. The first wave sees the emergence of the technology itself – in this case, large language models (LLMs) and generative AI tools like ChatGPT and Microsoft Copilot. The second wave, however, is where real transformation occurs. This is when we work out what to actually do with this technology and develop meaningful applications that integrate the technology into processes and products. It’s a phase that demands more than excitement; it requires thoughtful engineering, governance, cultural adaptation, and realistic expectations.

The impressive capabilities of LLMs are currently best demonstrated in scenarios like chatbots or simple content generation. However, translating those capabilities into reliable, high-value enterprise solutions remains a significant challenge. Showcasing that we have a really powerful intelligence is a very different challenge to embedding it into business processes especially ones that are of sufficient importance to the business to drive a return on investment that everyone is happy with. 

Our AI consultancy generates value from your data to help you work smarter and deliver meaningful business transformation. 

Find out more here.

Hard Lessons from Early Generative AI Adoption

The report highlights a cautious approach among practitioners, many of whom have scars from previous waves of technological over-promise. Data science and machine learning – two fields that experienced similar hype cycles – serve as cautionary tales. Data leaders have had their fingers burnt before, so they understand the pitfalls of inflated expectations and the high costs of overinvestment in unproven solutions.

Oakland’s findings show that early adopters in the UK are deliberately narrowing their focus to manageable, well-defined use cases. For example, generative AI excels in tasks like reading product descriptions to categorise items or identifying abbreviations in catalogues. These tasks are repetitive and internally focused and benefit from generative AI’s ability to handle high volumes of structured input. However, these projects require significant expertise and investment to deliver tangible results.

The Cost of Complexity

Despite AI’s potential, generative AI solutions are not plug-and-play. Custom applications require weeks of engineering effort, rigorous testing, and extensive oversight. 

“Even a seemingly straightforward proof of concept can quickly escalate into a six-figure project, which, in today’s climate, where budgets for R&D and new technology are often the first activities put on hold, can cripple activity. For small and medium enterprises, this level of investment can be prohibitive, underscoring the divide between early adopters and those waiting on the sidelines to see what happens.” 

Joe Horgan – Oakland Principal Consultant.

Learning from the Plateau of Productivity

For businesses taking a “wait and see” approach, there is wisdom in watching early adopters navigate these challenges. This mirrors other technological journeys, such as electric vehicles, where initial teething problems gave way to broader adoption as solutions matured and costs decreased. Similarly, the report predicts that generative AI’s most exciting phase will emerge post-hype – once expectations are tempered and businesses focus on realistic, high-value use cases.

So, what steps should a business take to become ready for generative AI implementation?

Building a solid business case for Generative AI is fundamental:

1. Be Strategic with Use Cases: Focus on simple, repetitive tasks where generative AI can deliver immediate value. Avoid overly ambitious projects that hinge on unproven capabilities. For example, Generative AI is brilliant at performing simple tasks that would take humans far too long to do. 
2. Invest in Expertise: Whether through hiring or developing partnerships with people like Oakland, having skilled AI engineers is critical to navigating this complex landscape. The pace of change with this technology is mind-blowing, and you need to be able to keep up.
3. Set Realistic Expectations: Understand what generative AI can and can’t do today. The technology’s future potential is vast, but meaningful application requires a grounded approach. You need to know what the LLM is capable of before you can assess if it can solve your problem.
4. Learn from Others: Monitor the successes and failures of early adopters. Use their insights (mistakes) to inform your strategy and reduce the risks of premature investment.

But what exactly are the benefits of using Generative AI and Intelligent Agents in your business? Read our blog to find out!

Is Your Data Ready for Generative AI?

Generative AI represents a powerful new tool in the digital toolbox, but it is just another tool. The massive hype is quite unhelpful because it creates ridiculous expectations. People throw money at it, and it creates bad vibes by diverting resources into older, more proven technologies. It’s when the technology appears on Gartner’s trough of disillusionment that you can start to think clearly and seriously about your Generative AI initiatives and how they can help drive your operational efficiency and competitive advantage. Its transformative potential will only be realised through careful, deliberate application. As the UK moves beyond the initial frenzy of excitement, organisations have an opportunity to define the second wave of innovation – where the focus shifts from possibility to productivity.

Oakland’s Plugged In or Left Out report underscores the importance of patience, planning, and pragmatism in navigating this transformative era. The report highlights that while generative AI’s capabilities are impressive, they are often limited in scope today. For example, simple, repetitive tasks like labelling products or categorising data represent the “low-hanging fruit” where AI can currently excel. However, even these cases require a disciplined approach to implementation.

The road ahead may be longer than the hype would indicate, but it also offers greater opportunities for sustainable growth. By focusing on realistic use cases, investing in expertise, and learning from early adopters, businesses can position themselves in a prime position to get the sort of meaningful value that the businesses’ key stakeholders will be satisfied with. For those willing to embrace these principles, the future of generative AI is bright.

The post Plugged In or Left Out: The UK’s Readiness for Generative AI appeared first on Oakland.

For organisations to harness AI’s potential, robust data governance must be in place. Here at Oakland, like many data management consultancies we’ve been exploring AI-driven transformations, the differences between data governance and AI governance, and how businesses can overcome common challenges to ensure AI success. 

The Critical Role of Data in AI Success 

AI, at its core, is a data product. Whether in machine learning models, large language models (LLMs), or generative AI applications, the principle of “garbage in, garbage out” remains true. If AI systems are trained on poor-quality data, the outcomes will be flawed, potentially leading to misinformed decision-making, regulatory compliance risks, and reputational damage. 

Key data challenges impacting AI initiatives are: 

1. Data Quality Issues – AI models depend on accurate, complete, and consistent data. Missing values, duplication, and outdated records compromise AI outputs. 
2. Data Ownership Ambiguities – A lack of clear data ownership makes accountability difficult and slows decision-making. 
3. Understanding Data Landscapes – Without a well-documented data architecture, it is challenging to trace data lineage, assess data integrity, and ensure compliance. 
4. Bridging the Gap Between Vision and Execution – Business leaders often have ambitious AI visions, but data professionals struggle to translate these into feasible implementations due to underlying data constraints. 
5. Unclear Business Value – Lack of alignment to wider business goals and challenges to determine where AI efforts will deliver most value. 

Additionally, there is often a disconnect between executives pushing for AI adoption and the data teams responsible for managing the infrastructure. Many assume that AI will work out of the box, failing to recognise the need for strong governance principles that allow AI initiatives to scale successfully. Without this, projects often result in frustration, misalignment, and ultimately abandonment. 

Data Governance as the Foundation of AI Success 

This is where working with a data management consultancy like Oakland can play a crucial role in addressing these challenges. We provide organisations with structured methodologies to manage data effectively, ensuring data is accurate, reliable, and accessible for AI applications. 

Key Elements of Data Governance for AI 

1. Data Lineage and Provenance – Organisations must track where data originates, how it has been transformed, and who is responsible for it. 
2. Data Quality Management – Implementing data validation, cleansing, and monitoring processes ensures high-quality input for AI models. 
3. Ownership and Accountability – Establishing clear roles for data stewardship fosters responsibility and trust. 
4. Privacy and Security Controls – Protecting sensitive data and ensuring AI models comply with regulations like GDPR and the EU AI Act is critical. 
5. Feedback Mechanisms – Continuous monitoring and improvement of data assets and AI outcomes create an adaptive governance framework. 

Organisations that embrace these principles avoid the common pitfalls of AI projects, such as reliance on poor-quality datasets, uncertainty around data sources, and misalignment between business strategy and AI deployment. 

Data Governance vs. AI Governance: What’s the Difference? 

While data governance focuses on managing data assets within an organisation, AI governance extends beyond data to include: 

• Model Lifecycle Management – Ensuring AI models are built, tested, deployed, and monitored according to best practices. 
• Regulatory Compliance – Adhering to AI-specific regulations such as the EU AI Act and industry guidelines. 
• Ethical Considerations – Addressing fairness, bias, and transparency in AI decision-making. 
• Risk Management – Identifying and mitigating risks associated with AI models, including unintended consequences and security threats. 

Ultimately, AI governance builds on data governance. Organisations with a strong data governance framework can more easily scale AI governance practices, ensuring that AI models operate with integrity and compliance. 

As organisations navigate this landscape, they must also consider how AI itself can assist in governance efforts. AI-driven tools can help automate data lineage mapping, anomaly detection, and compliance monitoring, making governance more scalable and efficient. 

The Business Case for Investing in AI and Data Governance 

To secure executive buy-in for AI and data governance initiatives, organisations should: 

• Align with Business Objectives – AI initiatives should support core strategic goals, such as improving customer experience, increasing operational efficiency, or enabling new revenue streams. 
• Prioritise Use Cases – Focusing on specific, high-impact AI applications rather than boiling the ocean ensures measurable results and quick wins. 
• Leverage Existing Capabilities – Organisations with mature data governance practices can extend them to AI governance, optimising resource utilisation. 
• Demonstrate ROI – Quantifying cost savings, risk reduction, and performance improvements strengthens the case for investment. 

How Data Governance and AI Governance Enhance Business Performance 

Improved Decision-Making 

With well-governed data, organisations can confidently rely on AI-driven insights to make strategic business decisions, reducing uncertainty and improving outcomes. 

Increased Efficiency and Scalability 

AI-powered automation can eliminate repetitive tasks, but only if the underlying data is trustworthy. Data governance ensures that automation is built on a solid foundation, allowing businesses to scale AI initiatives seamlessly. 

Regulatory Compliance and Risk Mitigation 

AI regulations are rapidly evolving, and organisations that fail to comply face fines and reputational damage. Data governance ensures that AI models adhere to legal and ethical standards, reducing regulatory risks. 

Competitive Advantage 

Organisations that integrate AI governance with strong data management practices differentiate themselves by delivering AI solutions that are accurate, reliable, and compliant, gaining an edge over competitors. 

Implementing a Structured Data Governance and AI Governance Framework 

A structured, standardised approach to governance is key to AI success. Best practices include: 

1. Building a Cross-Functional AI Governance Team – Engage data stewards, compliance officers, IT leaders, and business stakeholders. 
2. Developing a Clear Data Strategy – Define data ownership, quality standards, and governance policies. 
3. Leveraging AI for Data Governance – Use AI to automate data lineage mapping, quality assessments, and compliance monitoring. 
4. Establishing Trust and Transparency – Ensure AI decisions are explainable, auditable, and aligned with ethical standards. 
5. Continuously Refining Governance Practices – AI and data governance should evolve with changing business needs and regulatory landscapes. 

Organisations that view AI as a data-driven capability rather than just a technological innovation position themselves for long-term success. Data governance provides the critical foundation needed for AI initiatives to thrive, ensuring high-quality data, clear accountability, and compliance with regulations. By aligning data governance and AI governance, businesses can unlock AI’s full potential while mitigating risks, ultimately driving better decision-making, efficiency, and competitive advantage. 

For enterprises looking to embark on or refine their AI journey, investing in data governance is not optional it’s a necessity. 

“AI, Generative AI or Agentic AI holds great promise. However, many organisations currently feel held back by the necessity to validate their data thoroughly, unclear understanding of AI risks, and the potential for unforeseen issues, like bias and data security. Either making them hesitant to fully commit, i.e. death by POC, or unrealised sustainable value when deployed. 

These are unchartered waters for most. The good news is you can choose the pace you want to go. Navigating AI governance is like steering a ship through a storm; robust data governance is your compass, guiding you to safer, clearer waters”.

Zareene Choudhury – Oakland Data Governance Lead

Want to find out more?

If you’d like to learn more about Oakland’s data governance consulting services, then get in touch by emailing hello@weareoakland.com If you’d like to learn more about what a suitable data governance framework looks like, click on the link.

The post Data Governance in the Age of AI appeared first on Oakland.

But what is agentic AI? In its simplest form, the term describes semi-autonomous machine “agents” that can go beyond the chatbot and perform enterprise-related tasks without human guidance. 

These AI agents can set goals, plan, and adapt their actions based on continuous feedback from their environment. This level of autonomy allows agentic AI to take on various roles, from managing routine processes to addressing complex collaborative projects across different platforms and systems.

Whilst there is still a lot of AI hype, there is also the realisation that AI technology will impact every business 74% of CEOs surveyed by Gartner in 2024 believe this is the case, and agentic AI has incredible potential. Wherever you are on your enterprise AI journey, Oakland is a data consultancy ready to help. Book a free AI workshop or speak to one of our experts.

What Sets Agentic AI Apart?

Agentic AI stands out because it can perform semi-autonomous actions within a business environment. Unlike traditional AI tools that primarily function as enhanced chatbots, agentic AI brings a higher level of cognitive engagement, reasoning, planning, and executing tasks that typically require human intervention. The technology is built on large language models (LLMs), which are trained to process and understand vast amounts of text, enabling them to undertake complex business functions that go beyond mere data entry or routine customer interactions.

Process Logging

This is a hugely important point – every step taken by our agentic AI is meticulously logged. Actions, decision points, and outcomes are all recorded in structured logs. These logs are invaluable for human review and provide a foundation for ongoing improvements and learning within the system through careful analysis and supervised learning.

Natural Language Processing and Task Parsing

Our agentic AI systems use state-of-the-art language models to break down instructions into actionable tasks. This process transforms complex natural language into structured task blueprints, meticulously crafted through advanced prompt engineering and template matching.

Task Planning and Sequencing

Agentic AI excels in organising tasks efficiently. It plans and sequences tasks into logical, branching pathways using well-defined action schemas and goal-oriented frameworks. These pathways consider all necessary steps while accounting for dependencies and constraints, ensuring a coherent and strategic approach to task completion.

Agentic AI vs. Generative AI

The distinction between agentic AI and generative AI lies in their capabilities and intended applications. Generative AI, a hot topic in recent tech discussions, primarily focuses on creating content – text, images, or code – from existing data. It excels in tasks such as drafting emails, generating reports, or producing artistic content, based on patterns it has learned from large datasets.

In contrast, agentic AI takes this a step further by generating content, making decisions, and taking actions based on its understanding. While generative AI can suggest actions, agentic AI can actually execute these actions within certain parameters, thereby acting as a semi-autonomous agent within an enterprise. This ability to not only suggest but also enact makes agentic AI a powerful tool for businesses looking to automate more complex and decision-driven tasks.

The Evolution of Business Automation

For decades, businesses have aimed to offload as many tasks as possible to machines – from calculations done by software like Excel to customer service interactions handled by chatbots. However, these tools have always been limited to specific, predefined tasks. Agentic AI changes the game by applying its reasoning capabilities to a wider array of activities, thereby enhancing productivity and allowing human employees to focus on more critical, less mundane aspects of their work.

Integrating Agentic AI into Modern Workflows

The integration of agentic AI into business workflows is transformative, enabling not just automation but smart automation. By understanding and acting upon natural language, these AI agents can manage and sequence tasks effectively, leveraging existing tools within the enterprise to enhance workflow. This capability allows businesses to augment their workforce’s productivity without necessarily expanding it, a critical consideration in today’s economic climate where efficiency and cost reduction are paramount.

The Practical Applications of Agentic AI

One of the most compelling uses of agentic AI discussed involves its deployment within large IT sales organisations. AI agents interpret and organise sales data, translating messy, inconsistently recorded entries into structured, actionable information. This capability is crucial for companies that must accurately understand their sales patterns and inventory needs, helping drive informed business decisions and strategic planning.

Oakland Agentic AI Case Studies

With the AI hype, you could be forgiven for thinking you might get left behind. For many businesses, if you’re in an industry which isn’t yet being reinvented by AI don’t worry you can afford to go at a more steady pace.

So where do we expect businesses will get the biggest bang for their AI buck? Gartner research shows that an average enterprise expects 47% of AI benefits from employee productivity, 33% from process improvement, and 20% from business model innovation.

Oakland is seeing the biggest benefits in smoothing out clunky, routine business processes to allow employees to concentrate on more interesting and complex problems. From our recent research, knowledge management is a key area in which Agentic AI technology can really start to prove business value (link to our report) 

In a utility-sector example, frontline operatives must triage a high volume of alerts from their infrastructure network. Assessing the urgency of these alerts involves analysing complex, often unstructured data such as environmental factors, recent events, and historical infrastructure issues. Agentic AI steps in to streamline this process. By processing all inputs and applying reasoning capabilities, the AI can prioritise alerts and suggest actions, effectively replicating the decision-making process of human operatives. This saves time and ensures that critical issues are addressed promptly, enhancing operational efficiency and reducing risks.

Outside of knowledge management, we’ve found that large sales organisations often face challenges in managing extensive and fragmented product catalogues. With thousands of products listed, duplication and inconsistency in naming conventions, often due to decentralised data entry, can lead to significant inefficiencies. For example, the same product might be entered multiple times with vague or inconsistent descriptions, making it difficult to classify and report accurately. This lack of a unified taxonomy can create obstacles when aligning products with new organisational frameworks designed to improve financial reporting or operational insights.

Enter Agentic AI… Using Large Language Models (LLMs), businesses can process and understand product descriptions, cross-reference them with external data, and align them to standardised names and categories. These AI tools can manage vast datasets, accurately mapping products to a consistent taxonomy, even when descriptions are incomplete or unclear. Additionally, they can automate the classification process while incorporating exception-handling workflows for items requiring manual review. This approach enhances operational efficiency and provides more accurate and actionable data, enabling organisations to make informed decisions and deliver improved financial and operational insights.

These are just a few examples of the types of projects Oakland is currently working on, but there are hundreds of others.

Further Benefits of Implementing Agentic AI

Implementing agentic AI in business operations streamlines processes, significantly reducing the need for manual intervention and accelerating task completion. This technology enhances efficiency and boosts accuracy and consistency, minimising human errors and ensuring that tasks are executed reliably. 

With its scalable nature, agentic AI can manage expanding workloads without corresponding increases in human resources. Furthermore, it liberates human employees to concentrate on strategic and creative tasks, thereby adding substantial value beyond routine activities.

Here are the benefits of incorporating agentic AI:

Enterprise-wide System Integration

Agentic AI streamlines integration by interacting with existing information systems much like a human user but without requiring bespoke API development. These agents can navigate multiple interfaces, seamlessly transfer data between systems, and ensure consistency across your technology stack. For example, an agent could efficiently synchronise customer data across your CRM, billing system, and support platform, managing format conversions and validation checks with ease.

Autonomous Operation and Self-correction

Agentic AI operates with minimal human oversight, thanks to its advanced error detection and resolution capabilities. These systems autonomously adjust their strategies when encountering obstacles, reducing dependency on human intervention. For example, a document processing agent might automatically detect and correct formatting inconsistencies and log its actions for future review, ensuring accuracy and compliance.

Boosted Team Productivity and Strategic Focus

Multi-agent systems can take over routine operations and complex analytical tasks that traditionally consume significant human resources. This allows teams to focus on strategic initiatives and creative problem-solving. A research agent, for instance, could continuously analyse market trends, compile data, and generate insights, freeing analysts to focus on developing detailed strategic recommendations.

Dynamic Real-time Optimisation

AI agents excel at improving operational efficiency through real-time monitoring and adaptive decision-making. They process streaming data, identify emerging patterns, and implement timely improvements, which is invaluable for complex, multi-step processes. For example, a manufacturing agent might adjust machine parameters and coordinate maintenance schedules to optimise production metrics and improve output quality.

Scalable Learning and Knowledge Management

A standout feature of agentic AI is its ability to learn from diverse interactions and apply this knowledge across different scenarios. This ability enhances decision-making and problem-solving across the organisation. For instance, a customer service agent could identify effective resolution strategies from thousands of interactions, share these insights with the support team, and refine its approach based on feedback and emerging trends.

These benefits underscore how agentic AI can transform various aspects of an organisation, driving efficiency, adaptability, and strategic depth across operations. Companies can unlock substantial potential by adopting agentic AI, positioning themselves for success in an increasingly automated and data-driven business landscape.

The Challenges and Considerations

Despite its benefits, deploying agentic AI comes with challenges, particularly concerning governance. As these AI systems can perform actions semi-autonomously, businesses must implement robust governance frameworks to ensure that AI actions align with company policies and ethical standards. This includes setting up processes to review and approve AI-generated decisions and actions, ensuring they are appropriate before being enacted.

The Future of Agentic AI

As we look towards 2025 and beyond, the landscape of agentic AI is promising but requires careful navigation. These AI agents could take on increasingly complex tasks within the business sector, potentially leading to a new era of productivity and innovation. However, this will also require businesses to stay vigilant about ethical and practical implications of semi-autonomous systems.

Agentic AI represents a significant leap forward in business technology. By shifting the cognitive load from humans to machines, businesses can streamline operations, enhance employee productivity, and focus on innovation and strategic growth. As we advance, it will be crucial for companies to balance the capabilities of agentic AI with thoughtful governance and a clear understanding of its role within the broader business ecosystem. This balancing act will determine how profoundly agentic AI can transform business operations in the coming years.

Strategic Implementation of Agentic AI

To successfully implement agentic AI, organisations should identify areas where agentic AI can make the most significant impact, focusing on processes that benefit from automation and decision-making capabilities. Partnering with AI specialists who can provide insights and support throughout the implementation process is crucial. Regularly reviewing the performance of AI systems and making adjustments as necessary to optimise their functionality is essential for maintaining efficacy.

Read our guide below for further insight into crafting a data strategy that integrates with AI.

Agentic AI is reshaping how businesses operate, offering powerful tools for automation, decision-making, and process optimisation. By understanding and integrating agentic AI, organisations can achieve greater efficiency, improve accuracy, and better allocate their human resources towards growth and innovation.

Looking to enhance your organisation’s data capabilities? Discover how a data consultancy like Oakland can assist you. Dive into our AI services or visit our main knowledge management guide for deeper insights into our support offerings.

The post What is Agentic AI? appeared first on Oakland.

• The UK AI Market is worth more than £16.6B and is projected to grow to over £700B by 2035 
• 28% of businesses have built their own AI solutions or are using existing AI tools like CoPilot or ChatGPT 
• In the Autumn Budget in November 2024, the UK government laid out plans for an AI Opportunities Action Plan, which should provide a ‘roadmap to capture the opportunities of AI to enhance growth and productivity. 

Are you in the AI game? 

There’s no question that ‘AI-driven’ business is here to stay… much like ‘data-driven’ (insert your own marketing line here). But Data Governance Consulting at Oakland is very much rooted in the operational and practical side of real-world business. 

We prefer to consider it as “AI-enabled: A business that gains real value from their AI solutions’’. 

Back to our earlier point, 2025 AI is all about Agentic AIs: But is Agentic AI the future or just another AI technology? (although we have been developing intelligent agent solutions way before they were renamed Agentic AI, and all the major tech providers got on board)  

The beauty of AI Agents is that they are so much more than chatbots. They enable organisations to deliver focused value and target specific problems and opportunities in the data ecosystem. This is why honing down on use cases and relating them to the pains of business users is crucial in creating lasting AI solutions. 

Read the small print! 

So yes, AI promises to improve decision-making and deliver real value in a game-changing way. But… make sure to pause and read the small print on this promise, in particular: 

‘Your data quality and your AI governance capabilities will directly impact the value you extract from your investments in AI’ 

A study from RAND concludes that 80% of AI projects will likely fail. This prognosis is not dissimilar to Gartner’s, which predicts that 30% of GenAI projects will be abandoned after POC.  A key reason mentioned in both studies is the lack of high-quality data. 

What is high-quality data? (A holy grail..? Of course not!) 

• First and foremost, it’s important to know what data you have, where it exists, and who in the business has accountability for it. You don’t need to boil the ocean. Start with critical data domains related to your AI use cases.
• It knows what benchmarks and rules need to be applied to your data to classify it as good or bad: it is unlikely that you need 100% completeness for some of your data sets – understand what business purpose the data caters for, and this helps you define your benchmarks. 
• Having a view of your data eco-system is not just about having an architecture map that details the technical integrations. Have a view of the business processes and information requirements that underpin this architecture. Highlight where the risks are, e.g., because of duplication, manual interventions, or siloed processes. 
• Having mechanisms to monitor, log, and address data issues as they get flagged. This allows the business to mitigate risk and avoid confusion when resolving issues quickly but also allows it to be more strategic in improving business capabilities. 
• Documenting your critical data dependencies and how your data flows cross-functionally, i.e., your data lineage. This not only helps to make informed decisions on what protocols are needed to keep your data secure and address issues but also allows you to nail down requirements when scaling your data capabilities quickly. 
• If you are in a heavily regulated industry, then data security and regulatory compliance will require more robust data governance unless you want to risk £££ in fines and loss of your reputation. 
• People, People, People: Do not forget that ultimately, it is your business users who will make or break any data initiative. Training, engagement, data mindset, and change management are all key contributors to creating and sustaining good data. 

AI Governance vs Data Governance

Governing AI solutions is indeed wider than just governing your AI data. Trust is perhaps the greatest challenge to business adoption of AI, which is why it is just as important to govern the technical side of things, for example, how the models and algorithms are developed, trained, and deployed, as well as the architecture they rely on. 

AI governance faces a few challenges, including: 

• Explainability: A lot of AI models can feel like mysterious black boxes – we know something is happening, but we have no idea how! 
• Unstructured Data: Things like emails, documents, images, and videos are everywhere. Unstructured data makes up about 80-90% of all new data. It’s like trying to organise your sock drawer – where do you start with all the holey, single, Peppa Pig themed socks that have found themselves in your draw?
• Ethical Concerns: It’s critical to make sure AI systems are fair and unbiased. 
• Data Privacy and Security: Protecting sensitive data used in AI training and operations is essential.  

Do I need Data Governance before Artificial Intelligence?

Data governance is essential before implementing artificial intelligence (AI) if you want your AI initiatives to succeed. Here’s why:

1. Data Quality Drives AI Performance

AI systems depend on high-quality data to learn and make decisions. Without proper data governance, your AI models may be trained on inaccurate, incomplete, or biased data, leading to unreliable outputs and potentially harmful business decisions.

2. Ensuring Data Compliance and Security

AI often involves handling sensitive data. Data governance ensures compliance with regulations such as GDPR, CCPA, or industry-specific standards. This reduces the risk of legal penalties or reputational damage from improper data usage.

3. Establishing Data Accessibility and Consistency

Data governance creates a framework for consistent data definitions, ownership, and access control. AI thrives on integrated and well-structured datasets. Governance ensures that the right data is available to the right teams in a usable format, eliminating silos and duplication.

4. Mitigating Bias and Ethical Risks

Poorly governed data can introduce bias into AI models, leading to unfair or unethical outcomes. Data governance provides processes to identify, address, and monitor bias, ensuring AI systems make equitable decisions.

5. Cost and Efficiency Benefits

Investing in data governance upfront reduces the time and cost of preparing data for AI initiatives. It prevents expensive rework or failures by addressing data issues early, rather than discovering them during or after model development.

Read our blog on how you can demonstrate the ROI of Data Governance.

6. Scalability for Future AI Projects

Data governance provides a scalable foundation for expanding AI capabilities. It ensures that as data volumes grow, they remain manageable, trustworthy, and usable for future AI-driven projects.

Nothing’s perfect.. and that’s alright 

At Oakland, data governance consulting doesn’t mean you need perfect data to embark on or progress on your AI roadmap.    

AI gets your data talking.. you might not like what it says if your data is incomplete or of poor quality, but on the plus side, you can also use AI to flush out where your data issues are (every cloud has a silver lining) 

How do we manage AI regulatory requirements 

As with everything, a balanced approach is essential.  

Yes, it is a balancing act between being compliant and giving the business space to innovate. The good news is that, although the AI regulatory landscape is continuously evolving, a key theme is the ability to demonstrate that your AI is trustworthy—and a data governance framework allows you to demonstrate trustworthy AI data pipelines. 

“AI, Generative AI or Agentic AI holds great promise. However, many organisations currently feel held back by the necessity to validate their data thoroughly, unclear understanding of AI risks, and the potential for unforeseen issues, like bias and data security. Either making them hesitant to fully commit, i.e. death by POC, or unrealised sustainable value when deployed. 

These are unchartered waters for most. The good news is you can choose the pace you want to go. Navigating AI governance is like steering a ship through a storm; robust data governance is your compass, guiding you to safer, clearer waters”.

– Zareene Choudhury – Oakland Data Governance Lead

Want to find out more? 

If you want to know more about how to leverage Data Governance to get your AI Data trustworthy – then tune into our webinar, where Zareene Choudhury will be joined by Richard Adams of erwin by Quest. 

Get in touch

If you’d like to learn more about Oakland’s data governance consulting services, then get in touch by emailing hello@weareoakland.com

Want to read more about what a suitable Data Governance Framework looks like?

And if you want to find out how AI agents can make a difference to our business, read some of our case studies here, where we’re already delivering value to clients (well before AI agents became ‘the phrase of the year’! 

The post What is the Role of Data Governance in AI? Should You Even Care? appeared first on Oakland.

We start 2025 with a new term, but the same premise Agentic AI and AI Agents are where the market is heading. Autonomous systems or ‘AI agents’ perform all of those mundane (and increasingly not so mundane tasks that we never get around to doing). If 2024 was about figuring out how to take models into production, 2025 will be the year when we see agents emerging as first-class citizens in enterprise environments.

Where we started: The AI landscape at the beginning of 2024

At the outset of 2024, the AI world was still buzzing from the release of GPT-4, which solidified OpenAI’s leadership in the generative AI space. GPT-3.5 Turbo was being phased out as GPT-4 became the new standard. In the open-source arena, Meta’s LLaMA 2 was gaining traction, offering businesses an alternative to closed, proprietary models. Meanwhile, Anthropic continued to develop its Claude series, and Google’s Gemini 1.0 arrived, signalling serious competition in the race to dominate AI capabilities.

At the same time, Retrieval-Augmented Generation (RAG) started to gain serious traction in enterprise contexts. This approach—which combines large language models with external data retrieval—became the default architecture for deploying AI systems that required domain-specific knowledge. Tools like Azure OpenAI’s implementation of RAG and Google’s NotebookLM began offering practical solutions for integrating business data into AI systems. By the end of the year, deploying an AI solution without integrating it with your organisation’s data became unthinkable.

Production the key focus of 2024

While 2023 was the year of experimentation, 2024 was the year of making AI operational. Enterprises moved beyond proof-of-concepts (PoCs) to attempt production-grade implementations of LLMs. Yet, this process proved anything but smooth. The Gartner statistic that 85% of generative AI projects fail to make it into production rang true, illustrating the unique challenges that this technology presents.

The Production Problem

Deploying LLMs into production is fundamentally different from traditional software. In 2024, businesses grappled with challenges such as:

Cost considerations

Dedicated AI budgets, in the main, are few and far between. Data teams are being challenged to do more with less. Add to this the double whammy that most AI projects are far more expensive than initially projected. Gartner reported that many projects cost 300 to 500 times more than anticipated, due in large part to infrastructure, security, and integration costs.

Data integration

LLMs need access to enterprise data to add value. Whether via APIs, RAG pipelines, or custom integrations, aligning AI capabilities with business data became a priority.

Infrastructure limitations

AI requires massive compute resources. Many organisations found themselves constrained by global hardware shortages. For example, in the UK, models have to be deployed in Sweden because the necessary GPUs are simply not available locally.

Security and governance

The biggest challenge we see and one that the biggest tech vendors in the globe are wrestling with. Moving your enterprise data into AI workflows raises serious concerns around data sovereignty and governance. Even Microsoft’s Copilot could not guarantee that enterprise data would stay within a specific geographic region; an issue that created significant friction for regulated industries.

The SaaS boom

To address these production challenges, 2024 saw an explosion of Software-as-a-Service (SaaS) offerings targeting AI deployment. These tools aimed to simplify everything from RAG implementation to software development augmentation. For instance:

Copilot emerged as the dominant enterprise AI tool, thanks to Microsoft’s vertically integrated stack.

Third-party solutions like Cursor, Devin, and Windsurf tackled specific pain points for developers, automating workflows and improving coding efficiency.

Numerous RAG-focused SaaS platforms offered plug-and-play solutions for integrating enterprise data into LLMs, such as vector database providers like Pinecone and open-source alternatives like Weaviate.

The result? Companies could adopt AI solutions faster, but they also became increasingly dependent on these third-party tools to abstract away the complexity of production deployment.

The Physical Infrastructure Bottleneck

A major theme of 2024 was the physical infrastructure constraints facing AI. As global demand for compute power skyrocketed, manufacturers like TSMC and NVIDIA struggled to produce enough GPUs to keep up. Cloud providers like Azure, AWS, and Google Cloud were unable to provision sufficient hardware in all regions, forcing businesses to look for creative solutions.

This scarcity brought back challenges reminiscent of the 1990s on-premise era: organisations had to think carefully about where their workloads would run and how physical infrastructure limitations would impact their deployments. A system that worked seamlessly in Sweden, for example, might be impossible to deploy in the UK. As AI models become increasingly powerful, ensuring access to hardware remains a critical factor for enterprise success.

Enterprise Adoption Trends in 2024

Several trends emerged in the enterprise AI space this year:

1. Knowledge Management as the Leading Use Case

Our research found that knowledge management was the top enterprise use case for AI in 2024. Tools like RAG-based systems made it possible to extract insights from vast documentation repositories, improving workflows in industries like transportation, legal, and healthcare. For example, Oakland launched Network Rail’s first AI programme by deploying an AI-driven knowledge management system to surface lessons learned from past incidents, providing actionable insights for operations teams.

2. Custom Copilots

Enterprises increasingly embraced custom Copilots, integrating Microsoft’s Copilot platform with their own data to create tailored AI assistants. Rather than treating Copilot as a glorified spell-checker or summarisation tool, businesses began leveraging it for deeper productivity gains. Custom copilots became the new standard for enhancing workflows in applications like SharePoint, Teams, and Outlook. If you’ve seen the keynote speech at Microsoft Ignite you may be forgiven for thinking launching an AI agent or custom Copilot was as simple as creating a Power Point presentation. Sadly this is far from the reality.

3. The Imagination Gap

Despite these advancements, many organisations still struggled to identify where AI could deliver value. As one client insightfully put it: “I thought this was just a search bar.” Bridging the imagination gap remained a key challenge in 2024. Demonstrations of AI capabilities—particularly in workshops and proofs of concept—became essential for helping businesses visualise AI’s potential.

Preparing for 2025: The Rise of Agents

If 2024 was the year of LLM production, 2025 will be the year of AI agents. These systems will go beyond answering questions or summarising documents to autonomously take actions on behalf of users.

What is an AI Agent?

An AI agent combines a large language model with tools, workflows, and integrations that enable it to perform tasks independently. For example, a customer service agent might:

Retrieve information from a knowledge base (RAG).

Analyse a customer query.

Update a CRM system or initiate a workflow without human oversight.

These semi-autonomous systems will begin with routine tasks, such as scheduling, updating records, or generating reports. Over time, they will evolve to handle increasingly complex and non-routine business actions.

Risks of Agentic Systems

While agents hold immense promise, they also introduce new risks:

Capability gaps: Are LLMs capable enough to handle critical business tasks? The limitations of current models will be tested as organisations push agents to take on more responsibility.

Oversight and control: How do we ensure that agents operate within predefined guardrails? Governance frameworks will need to evolve to account for AI systems acting autonomously.

Trust and accountability: Who is responsible when an AI agent makes a mistake? Ensuring transparency and accountability in agentic systems will be critical.

Lessons Learned from 2024

As we look ahead to 2025, there are several lessons that enterprises can take from the past year:

Stay close to vendor roadmaps: The pace of AI development is unprecedented. Whether you’re using OpenAI, Microsoft, or Google tools, keeping a close eye on their roadmaps is essential to avoid obsolescence.

Build for flexibility: Given how quickly AI models evolve, organisations must build solutions that are adaptable to new technologies and frameworks.

Prioritise data integration: AI without access to enterprise data is just a toy. Ensuring seamless, secure integration between LLMs and business data sources is non-negotiable.

Invest in security and governance: Understanding where data is stored, how it moves, and what security wrappers are in place is essential for any AI project.

Start small, scale thoughtfully: AI projects are expensive, complex, and prone to failure. Begin with focused, high-impact use cases and scale gradually as you learn what works.

Conclusion

2024 has been a foundational year for AI. Enterprises have begun to move beyond experiments to deploy large language models into production, but challenges around infrastructure, security, and cost remain significant. The rise of SaaS solutions has made AI more accessible, yet the complexity of integration persists.

Looking ahead, 2025 will bring the next evolution: Agentic AI and AI agents that autonomously perform tasks and drive measurable business outcomes. As organisations prepare for this shift, the lessons of 2024 will serve as a valuable lesson.

Looking Ahead to 2025

As we screech into 2025, the role of AI and data within organisations will continue to accelerate and evolve. Several key trends and priorities are set to define the year ahead for data leaders, presenting both challenges and significant opportunities:

Operationalising AI at Scale

While 2024 saw a wave of AI experimentation and pilot projects, the focus in 2025 will shift toward scaling these initiatives to drive measurable ROI. Leaders will prioritise robust AI operationalisation frameworks, integrating models into production systems while ensuring governance, efficiency, and reliability. This will require alignment between data infrastructure, processes, and teams to ensure AI initiatives deliver tangible business value.

AI Governance and Ethical Frameworks

As regulatory environments mature and AI becomes increasingly embedded in decision-making, governance will move to the forefront of the AI agenda. Data and AI leaders must ensure their models adhere to ethical, transparent, and explainable practices, especially in sensitive industries such as finance, healthcare, and public services. Implementing strong governance policies that align with emerging regulations like the EU AI Act will be paramount.

Focus on Data Quality and Unified Platforms

Scaling AI is impossible without trusted, high-quality data. In 2025, data leaders will invest in modern data platforms that centralise, clean, and govern data across organisational silos. Unified platforms will become the backbone for scalable analytics and AI, enabling faster time-to-insight while reducing the friction between engineering and business teams.

Generative AI Maturity

The generative AI wave will mature further, with organisations moving from experimentation toward targeted, high-impact use cases. In particular, enterprise adoption of generative AI for content generation, code development, knowledge management, and customer experience will drive competitive differentiation. The demand for fine-tuning and customising foundational models to fit specific business needs will rise.

Investment in AI Talent and Upskilling

AI and data teams will continue to face talent gaps as demand outstrips supply. Organisations will double down on upskilling initiatives, developing AI fluency across the enterprise not just for technical teams but also for business leaders and frontline employees. Data and AI leaders will need to foster a culture of collaboration, pairing technical expertise with domain knowledge to drive innovation.

Measuring Value from AI and Data

In a challenging economic environment, data and AI leaders will face increasing pressure to prove ROI from their investments. Organisations will emphasise tracking clear KPIs, aligning AI and data initiatives with strategic business outcomes, and ensuring budgets are directed toward high-value priorities.

AI for Sustainability and Social Impact

2025 will also see AI playing a growing role in addressing sustainability and ESG (Environmental, Social, and Governance) goals. From optimising energy consumption and reducing waste to improving social equity, forward-thinking organisations will leverage AI to balance profit with purpose.

Our Closing Thoughts

As we move into 2025, the organisations that work hard to bridge the AI ‘imagination gap’ will thrive. Overcoming budget constraints and pressure to deliver a strong ROI will need data leaders with a strong stomach. Balancing innovation with governance, agility with resilience, and experimentation with measurable outcomes is no mean feat. But in the world of AI – who dares wins.

At Oakland, we provide pragmatic and actionable ways to get started with AI. In a rapidly evolving technology landscape, you need experts who can anticipate what’s next and align advanced AI—like Agentic AI—with your existing platforms, strategy, and governance frameworks. Oakland’s AI consultants can take the legwork out of putting AI to work across your organisation. Get in touch today or learn how we approach AI.

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In this guide, we explore the concept of lessons learned in project management, its purpose and benefits, the types of lessons companies can encounter, and an encompassing framework you can use to improve your approach. 

We’ll also examine how generative artificial intelligence is fast becoming a transformative tool for project managers, allowing them to understand and act on lessons learned in an instant. 

Learn how we helped Network Rail organise, understand, and act on its vast lessons-learned library prior to a critical £44 billion investment period.

What Are Lessons Learned?

Lessons learned are documented insights and knowledge gained from past project experiences. By understanding how projects unfolded, including their successes, failures, incidents, errors, and the resulting learnings, project managers can create reports used to guide their actions in future projects, ultimately making them more effective and successful. As such, it’s an important aspect of knowledge management.

Lessons learned is a people-focused practice that brings together all the staff on a project to collate and then distil their experiences—it’s a collective endeavour everyone should be responsible for. It’s best conducted at the end of each project phase to ensure all relevant lessons are captured. Reviewing relevant lessons learned should also be a key part of project planning.

When integrated into every project, lessons learned have the potential to transform how a project manager, team, department, or business operates. It can improve the speed and quality of delivery, avoid costly mistakes, and upskill staff, boosting return on investment in the process. 

What Are The Benefits Of Lessons Learned?

Lessons learned are a massive help for your project teams and the wider organisation for a whole host of reasons:

Better Decision-Making

The most important goal of lessons learned is improving the efficacy of future decision-making. A greater understanding of how previous projects unfolded lets project managers guide their teams more effectively. 

If a previous project required less time than required, for instance, managers can reduce the time available for similar future projects, enabling team efficiencies.

Avoiding Mistakes

Most projects will experience difficulties of some kind or another. By logging these, understanding why they occurred and their impacts, and ensuring they are accounted for in the future, projects can proceed more smoothly. 

If a previous project overran due to poor team communication, for example, a lesson learned would be to implement more regular catch-ups or promote the use of communication platforms.

Improved Efficiency and Performance

Lessons learned let organisations hone their process and approach. By helping your teams work more effectively, projects will progress more efficiently and ultimately have a greater positive impact. 

Better Adaptation and Resilience

In fast-paced, project-based working environments, teams regularly need to adapt to change: scope, timelines, feedback and so forth. With a bank of relevant lessons learned at your disposal, you can adapt your approach quickly, confident you’re making the correct split decisions.

Creating a Culture of Improvement

If a business wants to grow and develop, it needs to engage in a process of continual improvement. Culture is foundational to this. 

Staff need to feel that they can share their experiences, that their input is valued, and that it has a positive impact. If they do and this becomes a key aspect of the working culture, then the sharing and use of lessons learned will become self-perpetuating.

Greater Innovation

By taking time to understand what went well, staff can apply learnings to different situations and project types. By becoming aware of pitfalls and why they happen, they can find better ways of working in the future. The result is a continual process of creative innovation that touches and improves all aspects of projects.

Lessons Learned: Categories And Examples

So, what constitutes a lesson learned? There are plenty of categories to look out for spanning all aspects of projects.

Time 

Time-based lessons learned typically involve scheduling issues and a lack of understanding of a project’s critical path. Missed deadlines, staff overtime, a lack of contingencies, and dependency-related bottlenecks can all have an impact.

Cost

It is critical to accurately estimate a project’s cost, put funds aside for contingencies, and review spending regularly throughout the process. Funding shortfalls, a lack of cost monitoring, or overly ambitious budgeting can lead to these lessons.

Scope

Projects must have clearly defined objectives that all stakeholders agree on. They must also be backed by a change control process that only allows approved changes to alter the scope mid-project. Failure to do this can lead to projects becoming over budget and behind schedule.

Technical

Every project depends on several technical inputs, whether that’s tools, software, or systems. Failure to account for these can result in lessons learned about the constraints and benefits of different tools and software or the need to better integrate and provide training on new systems prior to the start of new projects.

Quality

Quality checks and processes like end-user testing are crucial in guaranteeing the project is up to standard. Failure to do so can lead to lessons learned regarding customer support issues, poor usability, and project delays.

Risk

Risk management needs to be accounted for throughout the project. If risks aren’t adequately identified in the planning stages, they can lead to issues later down the line. Risk mitigation plans are required, too – if they’re not in place, problems can have a much greater negative impact than they otherwise would have.

Communication

Project managers need to have a communication plan throughout the project lest it overrun or veer off course. Throughout the process, it’s also important to document progress, decisions, and changes. If these aren’t accounted for, lessons can materialise due to a lack of continuity and clarity, leading to similar problems.

Resource Management

Lessons learned around resource management include accounting for the right skills within a team or department, which can lead to either underperformance or project scopes not being as great as they could have been. Improper allocation of resources is also a common learning – failure to accurately assign resources can lead to burnout, staff turnover, or result of underallocation. 

Stakeholder Management

Project lessons around stakeholder management might include a lack of engagement leading to poor buy-in. Communication is also key – a lack of frequent and clear communication can lead to frustration or misaligned expectations.

Compliance

Lessons learned can include compliance, standards and regulatory factors, including not meeting key regulations, an inability to keep up with changing policies, or a failure to match company standards.

What Process Framework Is Best For Conducting Lessons Learned?

When properly harnessed, lessons learned can be game-changing. But whether you’re new to the concept or are a seasoned project professional wanting to hone your approach, what is the best process framework to use? A five-step process is a great place to start.

Step 1: Identify

This first stage involves capturing lessons learned so they are ready to be processed and used in future projects. Make sure to account for the following:

• Reflection: Get your team to regularly reflect on their experiences during the project, noting down thoughts and opinions in between the more structured identification activities below.
• Surveys: Send out lessons learned surveys to stakeholders after significant project phases to harvest feedback while it’s front of mind. 
• Team discussions: As a team, discuss how the project went: positives, negatives, and improvements. These discussions should ideally be facilitated by someone who isn’t the project manager to encourage honesty.
• One-to-one interviews: Talk to team members and stakeholders one-on-one about their experiences. These conversations may glean more honest feedback than surveys and roundtable discussions.

Using the lessons learned categories listed earlier in the article is a great way to structure surveys and sessions. In them ask simple and powerful questions: what went wrong, what went right, what needs to be improved, and how.

Step 2: Document

For lessons learned to be effective, they need to be documented so they can be sharable and used to benefit future projects. Throughout the lifecycle of the project, as identification tasks have been conducted, collate and log the feedback, including:

1. The specific lesson
2. The category of project
3. The category of lesson
4. The project the lesson originated from
5. Keywords related to the lesson and project
6. Who raised it
7. When it was raised
8. How significant an issue it was on a scale of 1-5.

With the above framework, actions can be prioritised, and lessons learned can be quickly and easily retrieved during the planning phase of future projects. Custom generative AI tools can be a time-saving tool here, organising feedback in a clear report format.

Step 3: Analyse

Once you’ve logged them, take time to understand the lessons learned so you can use them to improve your process and approach. What were the most significant problems? Why did they occur? What could be done to rectify them? Once analysed, you should have an idea of the greatest opportunities for improvements, which you can then assign as actions to the relevant stakeholders. Ensure the resulting report includes the following:

• A top-level summary of lessons learned (findings and recommendations)
• An executive report providing findings and recommendations in brief (helpful for decision-makers)
• Detailed findings 
• Detailed recommendations
• Relevant project metrics (to show the impact of the lessons learned).

Armed with your findings and recommendations, you can then present them to stakeholders and your project team.

Whether you’re planning, midway through, or have finished a project, this process needn’t be manual or long-winded. With an intelligent AI agent, you can take the power of a large language model (such as Open AI’s GPT model) and tailor it to the task at hand. 

When put to work on lessons learned analysis, users can ask intelligent agents questions through a chat functionality. In an instant, the agent scans through huge volumes of data, interprets and interrogates it, and then provides accurate insights or broader summaries to the user, saving significant amounts of time and effort.

Step 4: Store

Lessons learned must be stored in an organised library that is easily accessible to users. If it isn’t, the effort of interacting with the system will put off users and lessons will be ignored.

AI can also assist in storing and organising lessons-learned documentation. Integrated with your systems, intelligent agents can categorise and store logs in the right place and retrieve the relevant insights for users without them needing to enter the library. AI can provide

Step 5: Retrieve

An ongoing final step, retrieval of lessons learned, is a critical part of the project planning process. 

Whether manually accessing a library of documentation or interrogating data en masse using an intelligent agent, search for reports relevant to the upcoming project. Take time to understand the pitfalls and successes of previous projects, and thread these into your plan to level up your approach.

Can Generative Ai Improve Lessons Learned?

Generative AI is a key focus area for all manner of organisations, with its use cases particularly beneficial for those that work on a project-by-project basis. No less is this true than lessons learned; the technology has the potential to improve the process from start to finish:

Data Analysis and Insights

• Recognise patterns: AI can analyse past project data to identify patterns and trends, helping teams understand what strategies worked well and what didn’t.
• Identify root causes: Generative AI can help uncover the underlying reasons for project successes or failures by bringing together several project metrics and outcomes.

Automated Documentation

• Generate reports: AI can automatically generate comprehensive lessons learned reports based on project data, feedback, and outcomes, reducing the manual effort required to produce them.
• Knowledge management: By organising and categorising lessons learned in a database, AI can ensure that valuable insights are easily accessible for future projects.

Continuous Learning

• Feedback loops: Generative AI can facilitate real-time feedback, allowing teams to continuously update lessons learned as new information and insights become available.
• Personalised recommendations: AI can suggest best practices or lessons relevant to specific project types or contexts, improving the applicability of insights.

Scenario Simulation

• What-if analysis: AI can simulate different project scenarios based on historical data, helping teams visualise potential outcomes and make informed decisions.
• Risk assessment: Generative AI can identify and assess risks by analysing past project challenges, enabling proactive measures in future projects.

Enhanced Collaboration

• Collaborative Platforms: AI can facilitate better collaboration among project teams by summarising discussions, highlighting key takeaways, and ensuring that lessons learned are communicated effectively.
• Sentiment Analysis: By analysing team communications, AI can gauge team morale and engagement, providing insights into the human factors that affect project success.

Integration with Project Management Tools

• Seamless Workflow: AI can be integrated into existing project management tools to automate the collection of lessons learned during project execution, making the process less intrusive.
• Real-Time Updates: AI can provide real-time updates on lessons learned and related best practices as teams work on projects, ensuring that insights are applied immediately.

By leveraging generative AI in these ways, project managers can foster a culture of continuous improvement, making lessons learned a more integral and efficient part of the project lifecycle.

Oakland’s AI consultants have helped several organisations implement smarter, faster, lessons learned. With our help, you can take the legwork out of collating, analysing and retrieving lessons learned and put them to work across your organisation. Get in touch today or learn how we approach AI.

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But what does an effective knowledge management process look like? This guide will walk you through the key components, from gathering and organising information to making it accessible to the people who need it. We’ll also explore how tools like AI and intelligent agents can help streamline and enhance these efforts, ensuring that your knowledge flows smoothly throughout the business.

What is a Knowledge Management Process?

A knowledge management process is the system through which an organisation collects, organises, shares, and utilises its information. It’s about creating a structure that allows knowledge, whether it’s in the form of reports, research, employee expertise, or other data, to move seamlessly throughout the business. At its core, knowledge management is designed to make sure that everyone in the organisation can access the information they need, when they need it, to make better decisions, collaborate more effectively, and foster innovation.

The process itself can vary from organisation to organisation, but it typically involves capturing knowledge from both internal and external sources and distributing it in an accessible way. In recent years, the rise of AI-powered tools has transformed how companies approach knowledge management. Intelligent agents and AI systems can now automate many of the tasks that were previously done manually, helping businesses streamline their information handling.

The Role of the Knowledge Management Process

The role of a knowledge management process extends beyond simply storing information. It’s a strategic asset that enables businesses to harness their collective knowledge and turn it into a source of competitive advantage. A well-designed knowledge management process ensures the right knowledge is available to the right people, enabling faster, more informed decision-making.

When knowledge is easily accessible and shared across teams, it fosters collaboration and drives innovation. This is especially important in sectors that rely on continuous learning and adapting, such as technology, healthcare, or finance. By reducing silos and encouraging the free flow of information, businesses can respond more quickly to challenges and opportunities.

AI can significantly enhance these benefits by automating the gathering, organising, and distribution of knowledge. AI-driven tools can scan vast amounts of data, identify relevant patterns, and ensure that knowledge is always up to date. This makes knowledge management not only more efficient but also more effective. 

If you want to transform your knowledge management strategy, read more here.

How Do You Develop a Knowledge Management Process?

Building a knowledge management process takes careful planning. It’s about more than just implementing software; it’s about aligning your organisation’s knowledge flow with your business strategy. The first step is to evaluate your current approach. What’s working well, and where are the gaps? Understanding how knowledge currently flows or doesn’t flow through your business is critical to setting a strong foundation.

From there, you’ll need to set clear goals for what you want to achieve with your knowledge management process. Maybe your priority is making information easier to find, reducing duplication of efforts, or fostering better collaboration between departments. These objectives will guide the design of your process.

Once you have your goals, it’s time to choose the right tools. This could involve setting up a centralised knowledge base, where employees can easily access information, or using AI-driven solutions to automate data management tasks. Intelligent agents can help by automating the retrieval and organisation of information, making it easier for employees to find what they need quickly.

Creating a culture that supports knowledge sharing is just as important as the tools you use. Too often, valuable knowledge is locked away in silos, with different teams or departments holding onto information that could benefit the wider organisation. Encouraging a culture of open communication and knowledge sharing can help ensure that information flows freely and everyone benefits from the insights available.

Finally, remember that a knowledge management process should evolve as your organisation grows and changes. Regular reviews will help you ensure that your system continues to meet your needs. Don’t be afraid to adjust your approach as necessary, especially as new technologies become available.

9 Stages of the Knowledge Management Process

1. Identification

The first step in implementing a knowledge management process is to identify areas where knowledge is lacking or where gaps exist. This can involve evaluating which departments or teams are missing critical information and where decisions are made without the necessary context. By clearly identifying these gaps, organisations can target areas where knowledge management will have the most significant impact.

2. Collection

Once knowledge gaps have been identified, the next stage is to collect the required information. This knowledge can come from various sources, including internal documents, research papers, and employee insights. Collecting this data in one centralised location ensures it’s accessible to all employees. In this phase, it’s crucial to have a systematic approach to gathering knowledge to avoid missing out on critical information.

3. Organisation

After collecting knowledge, it needs to be organised in a way that makes it easy to find and use. This might involve categorising knowledge by department, topic, or function. A well-organised knowledge base ensures employees can quickly find the necessary information without wasting time. AI tools can assist in this process by automating categorisation and helping ensure that data is properly labelled and tagged for ease of use.

4. Storage

Storing knowledge securely is essential for maintaining its integrity. Whether stored on a cloud-based platform or in physical archives, knowledge must be kept in a way that makes it accessible to those who need it while protecting it from unauthorised access. AI can also support secure storage by enabling quick, automated searches and retrievals, ensuring that stored information is easily retrievable.

5. Sharing

Knowledge must be shared across the organisation to have a real impact. By fostering a culture of open communication, companies can ensure that employees are not hoarding information but instead sharing it with those who need it most. AI tools, such as intelligent agents, can automatically distribute information to the relevant teams, ensuring that everyone has access to the knowledge they need when they need it. 

6. Application

Once shared, knowledge must be used. This stage ensures that employees can apply the knowledge they’ve received to their everyday tasks, improving decision-making and operational efficiency. Training and guidance are crucial in this stage, as they help employees integrate new knowledge into their workflows effectively.

7. Maintenance

Knowledge is only valuable if it’s kept up to date. Maintaining and reviewing the information stored in the knowledge base is vital to ensure that outdated or irrelevant data doesn’t compromise decision-making. AI tools can automate this process, identifying old or redundant knowledge and flagging it for review.

8. Protection

Protecting sensitive knowledge is critical. Whether it’s intellectual property, client data, or internal company strategies, knowledge must be secured against breaches or leaks. A robust security framework, often supported by AI monitoring tools, helps safeguard this valuable information while ensuring accessibility to authorised personnel.

9. Evaluation

The final step is to evaluate how well the knowledge management process works. This involves assessing its overall effectiveness, identifying areas for improvement, and making necessary adjustments. Regular evaluations ensure that the process continues to meet the organisation’s needs. Continuous improvement is key to ensuring the knowledge management process remains relevant and useful.

Why Knowledge Management Processes Are Important

A well-structured knowledge management process is crucial for businesses that want to remain agile and innovative. It enables organisations to unlock the full value of their collective knowledge, making it easier to respond to challenges, seize opportunities, and make informed decisions.

Knowledge management processes reduce redundancies, speed up decision-making, and foster a culture of collaboration. They also ensure that important information doesn’t get lost in the shuffle, especially in fast-paced environments where knowledge can quickly become outdated or forgotten.

By incorporating AI tools into your knowledge management process, you can take it a step further. AI can automate routine tasks, such as data collection and organisation, freeing up employees to focus on higher-level strategic work. However, it’s important to remember that AI should enhance, not replace, human input.

Ready to Take Your Knowledge Management Process to the Next Level?

Building a knowledge management process is one of the most impactful steps you can take to improve efficiency, foster innovation, and drive long-term success. By making information easily accessible and encouraging knowledge sharing across your organisation, you’ll set your business up to thrive in an increasingly information-driven world.

If you’re ready to optimise your knowledge management process, Oakland can help. Explore our AI services or check out our main knowledge management guide for more insights into how we can support your efforts.

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