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.

VentureBeats, 2022

“Organisations with little or no cloud cost optimisation plans end up overspending on cloud services by up to 70% without deriving the expected value from it”

 Gartner, 2022

Big Tech under pressure from cost-conscious cloud customers (ft.com)

The cost of cloud resources has been increasing at an alarming rate for many customers who use cloud service providers such as Microsoft Azure, Amazon Web Services, or Google Cloud, and for those who use multi cloud providers then the costs for cloud usage can be eye-watering. For many, large volumes of data have been integrated into these platforms to capitalise on the huge range of services these cloud providers offer. Ranging from unparalleled data resilience to large-scale advanced analytics.

Initially, these costs may have offered cost savings from the traditional on-premises cost, but as data volumes increase, the cost savings evaporate, and cloud cost management increases with many organisations IT teams overspending on their cloud environment. The good news is there are ways and approaches to reduce cloud costs that can be built into your cloud strategy.

Where do I begin?

At Oakland, we’ve seen a lot of organisations giving more attention to improving Cloud FinOps as a function or approach. Cloud FinOps, short for Cloud Financial Operations, is a set of practices that help optimise cloud spend. Cloud operational management is often decentralised, with costs that can be hard to predict or control. For example, cloud services costs often just arrive weekly or monthly as one large invoice to someone outside of IT with no real transparency on what is being invoiced for. Understanding these costs as an organisation is half the battle.

Following cloud cost management best practices or frameworks can result in the growth and development of cloud solutions in line with manageable costs. Examples of cloud cost management best practices include:

• Improved collaboration between business and technical stakeholders works towards a common goal of becoming more cost-effective and reducing cloud expenditure.
• Increase transparency by utilising reporting, resource tagging, and other cost management tools
• Track and monitor the creation and running of cloud resources.
• Establish clear internal responsibility of cloud costs to appropriately assign expenses.

What are the other ways to reduce costs?

This is only half the story, as robust Cloud FinOps is underpinned by different processes aiming to improve and enhance an organisations approach to managing cloud spend. There are other ways to reduce costs which range from straightforward to more involved:

Decommissioning

Many organisations have multiple legacy systems storing data. These systems are often:

• Not used regularly
• Replaceable with a modern equivalent
• Expensive to maintain and keep live
• Require specialist knowledge to integrate with and accommodate

Fewer legacy systems regularly result in simpler data architecture and lower costs from the time invested in the above.

Through identifying valid candidates for decommissioning, a business case can be created to support migrating from and/ or closing down these legacy systems.

Resource planning/scaling

Cloud resources utilise computing power and storage measures to establish an overall cost.

Some processes may be using too many resources or running more often than necessary increasing cloud bills. Reserving resources for longer periods of time will ensure cost optimisation for your cloud expenditure.

Consider ingesting data for a report:

• Does the ingestion pipeline need to run as often? For how many months/ years?
• Would it be an issue if it took longer to run?

Amending such factors to scale down resources whilst accounting for the impacts can result in an overall reduction in cloud spending

Cloud Platform Modernisation

Sometimes, a resource-intensive process is still required but costs your IT teams a lot of money to maintain and run.

Re-engineering a pre-existing solution by using more up-to-date and efficient methods may result in cost savings through solutions running faster and being easier for your IT team to maintain.

This approach also allows to alter or augment the solution during this modernisation process.

An up-to-date process that is deemed to be an industry-standard regularly provides more flexibility and cost-saving options than a legacy process or technical approach.

Look at all the money I can save!

Yes, but to a point. Knowing these approaches is different from applying them, as it is often not so straightforward that you can just delete some data, remove a system, shrink resources, or overhaul an inefficient process. Working with a business to know where to practically reduce spending or costs utilises a lot of previous experience rather than turning everything off (or, at worst just doing everything the Azure Advisor says regardless of the impact). Working towards a tangible plan, aligning stakeholders, and initiating the agreed actions are all important requirements to manage any dependencies or concerns and give the business value your organisation is looking for to reduce its IT spending.

At Oakland, we offer a holistic review and outline recommendations and options in an actionable report and give you a deliverable plan. We utilise our vast wealth of experience and are able to look against different lenses to focus the review. These include focusing on architecture design, engineering approaches, governance management, ESG, strategic and tactical alignment, and even market trends. The output is a clear, tailored plan to result in reduced cloud spend / improved Cloud FinOps.

If you would like to find more information about how we can help reduce your cloud spend, please get in touch by emailing hello@theoaklandgroup.co.uk or calling 0113 234 1944.

Jack Evans is a Principal Consultant at the Oakland Group.

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