{"id":5211,"date":"2020-12-02T16:51:37","date_gmt":"2020-12-02T16:51:37","guid":{"rendered":"https:\/\/www.theoaklandgroup.co.uk\/?p=5211"},"modified":"2020-12-02T16:51:37","modified_gmt":"2020-12-02T16:51:37","slug":"ai-to-assist-projects-has-been-around-for-decades-why-arent-we-using-it","status":"publish","type":"post","link":"https:\/\/weareoakland.com\/blog\/ai-to-assist-projects-has-been-around-for-decades-why-arent-we-using-it\/","title":{"rendered":"AI to assist projects has been around for decades \u2013 why aren\u2019t we using it?"},"content":{"rendered":"

I recently read a fascinating\u00a0article entitled \u2018Using Artificial Intelligence Techniques to Support Project Management\u2019. It discusses AI-assisted decision-making on projects, knowledge-based interactive graphics and hybrid computer-human systems. It sits well with the large number of articles and reports that both APM and IPMA have been sponsoring recently, and with the launch of the Project Data Analytics Taskforce. There is only one problem \u2013 this article was written in 1987.<\/p>\n

The author was engineering project management guru Ray Leavitt. And while Professor Leavitt has always been a far-sighted individual, his article points to the uncomfortable fact that we have been able to use AI to assist us in managing projects for decades. The real puzzle is why it has not been more widely adopted.<\/p>\n

So, what\u2019s the excuse?<\/strong><\/p>\n

We can\u2019t argue that technology has only recently developed to at last make it feasible. Data to conduct this type of activity has been hanging around in Primavera and related ERP systems for decades. And we don\u2019t need gargantuan amounts of computing power for these calculations. Data at the level of a project is not \u2018big data\u2019. There may be millions of data points within a project, but I would be really surprised if you pointed out a programme made up of more than 50,000 projects. 50,000 of anything is not big data.<\/p>\n

So, responsibility for the failure to exploit AI in project management must lie elsewhere. A recent investigation that I undertook with colleagues at Sheffield, Manchester and Southampton universities, sponsored by APM, may help explain this conundrum. The\u00a0study is called\u00a0Project Data Analytics: The state of the art and science. We began by asking a wide range of project practitioners two simple questions:<\/p>\n

What are you doing with project data analytics now?
\nWhat will you be doing next?
\nWe used the answers to these questions to create a summary of where we are with project data analytics and, importantly, the barriers that are preventing its wider uptake. In analysing the results of our interviews, we identified that we needed to establish some succinct definitions in order to discuss results between ourselves and our interviewees.<\/p>\n

Three key definitions<\/strong><\/p>\n

Project data analytics\u00a0<\/strong>is the use of past and current project data to enable effective decisions on project delivery. This includes descriptive analytics (presenting data in the most effective format) and predictive analytics (using past data to predict future performance).<\/p>\n

Machine learning<\/strong>\u00a0is the name given to computer algorithms that \u2018learn from doing\u2019. In project terms, machine learning has, at its centre, algorithms that are used to spot patterns between some characteristic of projects or programmes and some aspect of project performance. This process gets more accurate the more it is used. Machine learning is a fundamental part of predictive project data analytics.<\/p>\n

Artificial intelligence<\/strong>\u00a0refers to the study of \u2018intelligent agents\u2019, autonomous non-human entities that can take in information from their environment and act upon their environment in a way that enables them to succeed in their goals. Intelligent agents need to have mastered machine learning and aspects of predictive data analytics. In a project context, some people have speculated that an intelligent agent could enhance or change the roles and status of many project professionals<\/p>\n

We summarised our findings in a 2×2 matrix.<\/strong><\/p>\n

\"\"<\/p>\n

This highlighted several issues that explain the slow uptake of AI and project data analytics by the project management community.<\/p>\n

The \u2018geeks meet business\u2019 divide<\/strong><\/p>\n

For most project practitioners, \u2018python\u2019 is a big snake and not a computer language for machine learning applications. Many project practitioners\u2019 eyes glaze over when \u2018geeks\u2019 start talking in terms of \u2018random tree forest algorithms\u2019 or \u2018data cleansing\u2019. Until we can find a clear, unambiguous way to talk about these technologies, we are not going to be able to take advantage of what they have to offer.<\/p>\n

Attitudes to data<\/strong><\/p>\n

The age-old GIGO (garbage in, garbage out) rule applies. If the data disciplines associated with our use of project control systems lead to poor data, then we can learn absolutely nothing by analysing that data. A first step in any wider use of project data analytics must be better data discipline, and this needs to start immediately.<\/p>\n

Basic statistical challenges<\/strong><\/p>\n

APM defines\u00a0a project\u00a0as \u201ca unique, transient endeavour undertaken to achieve planned objectives\u201d.<\/p>\n

If projects were truly unique, there would be no point in trying to \u2018pattern spot\u2019 across projects, which is what predictive project data analytics is trying to do. Given that we accept there are degrees of \u2018uniqueness\u2019, a very important question is: what sample of projects is it appropriate to compare other projects against? This problem of \u2018reference class\u2019 underlies any predictive capability and is one that has not yet been solved satisfactorily. Practitioners are still trying to establish a mechanism for comparing \u2018apples with pears\u2019.<\/p>\n

What data do we really need to make better decisions?<\/strong><\/p>\n

Finally, and arguably most importantly, practitioners still do not know what data is required to make better decisions in managing projects. This fundamental question remains a mystery. Project data analytics may yield some incredible insights, but it can only do this by using the data that is available. When it comes to identifying what data is needed to make better project decisions, we are definitely in the territory of \u2018unknown unknowns.\u2019<\/p>\n

Perhaps, given all of the above, it is no surprise that Ray Leavitt\u2019s expectation of AI\u2019s use in projects still hasn\u2019t been realised over 30 years later. If we are serious in our intent to use AI and predictive analytics in projects, then we must find a solution to the problems outlined in this article.<\/p>\n

Download the paper Project Data Analytics: The state of the art and science here.\u00a0https:\/\/www.apm.org.uk\/media\/46977\/pathfinder-report-web-final.pdf<\/span><\/a><\/strong><\/p>\n

Content Reproduced with huge thanks to Professor Naomi Brookes https:\/\/www.linkedin.com\/in\/naomi-brookes-999b111\/<\/span><\/a>and the Association of Project Management https:\/\/www.apm.org.uk\/<\/span><\/a><\/p>\n

Naomi Brookes was appointed as the first Professor of Complex Programme Management at WMG (Warwick University) in spring 2020, with a brief to establish a new research capability for WMG in project, programme and portfolio management. Naomi is joint lead of the data theme for the Infrastructure and Projects Authority\u2019s Project X research initiative, a member of APM\u2019s Data Summit and founder of the Project Data and Analytics Research Network. As well as leading on the Project Data Analytics research, Naomi is currently leading an APM research fund paper on Eliminating Modern Slavery in Major Projects, to be published in December 2020. She is a member of APM\u2019s Research Advisory Group.<\/p>\n","protected":false},"excerpt":{"rendered":"

I recently read a fascinating\u00a0article entitled \u2018Using Artificial Intelligence Techniques to Support Project Management\u2019. It discusses AI-assisted decision-making on projects, knowledge-based interactive graphics and hybrid computer-human systems. It sits well with the large number of articles and reports that both APM and IPMA have been sponsoring recently, and with the launch of the Project Data…<\/p>\n","protected":false},"author":3,"featured_media":5227,"comment_status":"open","ping_status":"open","sticky":false,"template":"","format":"standard","meta":{"_acf_changed":false,"content-type":"","footnotes":""},"categories":[9],"tags":[72,259,239,260,6,261,262,263,264,194,257,258,265],"class_list":["post-5211","post","type-post","status-publish","format-standard","has-post-thumbnail","hentry","category-insights","tag-ai","tag-apm","tag-association-of-project-management","tag-benchmarking","tag-data-analytics","tag-data-analytics-consultancy","tag-data-analytics-consultants","tag-descriptive-analytics","tag-naomi-brookes","tag-predictive-analytics","tag-project-data","tag-project-data-analytics","tag-project-data-experts"],"acf":[],"yoast_head":"\nUsing AI To Assist Projects | Oakland<\/title>\n<meta name=\"description\" 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