Many organisations misunderstand the nature of artificial intelligence and what it can and cannot do for business. As a consequence, there are many more organisations willing to apply the technology in their projects than ones ready to do it. Antonio Nieto-Rodriguez and Ricardo Viana Vargas created the AI readiness spectrum checklist to help leaders understand where they stand with AI.
When it comes to artificial intelligence (AI), project leaders should ask themselves two key questions. First, are my organisation and I willing to adopt AI-inspired tools as part of the ordinary course of doing business, particularly in project management? This question will give you a sense of your organisation’s appetite for machine learning (ML)–inspired technology. However, the second and more critical question is: are my organisation and I ready to take this leap forward? This question will give you a sense of how quickly you can identify and apply AI to projects.
The not-so-happy news is that there are many more organisations willing to apply AI in their projects than ones ready to do it.
What explains the lag? The first limiting factor is misunderstanding the nature of AI and what it can and cannot do for business. If you are unsure about what AI can do for your business, you are likely unaware of how to lay the foundations for AI adoption. Another factor is miscalculating how people and culture restrict the organisation’s willingness to adopt technological solutions. Even the best and most up-to-date technologies can be defeated by leaders and workforces gripped by uninformed fears about the impact it can have on their lives.
We can all agree on one thing: willing and ready or not, AI is coming.
AI significantly improves project outcomes
It is no exaggeration to suggest that project management is at the heart of business operations and transformations. Just about everything you do in pursuit of success involves a project of one sort or another. The truly successful organisations devise, design, implement and complete projects with more certainty.
Not all organisations have a grasp on those competencies. The Standish Group, which for several decades has charted the success of technology projects, estimates that only one-third of all projects around the world achieve their goals. Less than one-half of those “so-called successful” projects produce high-value returns.
The introduction of AI has the potential to massively improve this woeful record of success if, and it’s a big if, organisations are both willing and ready to embrace AI and allow it to come to fruition.
Gartner research suggests that by 2030, 80 per cent of basic project management tasks will be run by AI and powered by big data, machine learning and natural language processing. AI innovations are arriving on what seems like an almost daily basis. For example, the business world has only just familiarised itself with the possible benefits of ChatGPT, an AI-inspired chatbot that powers things such as the new Bing search engine. The first version of ChatGPT was released in November 2022 and by March 2023 its creators had already released GPT-4, a fourth-generation version capable of accepting text and image inputs. The incredibly fast pace of change in AI applications puts a lot of pressure on businesses to keep up. However, not all organisations have the capacity to use AI to improve their business operations and project management. This lack of readiness can be defined in a number of ways, but at the heart of this failure is the inability to find and harness the power of data.
Data is the heartbeat of AI readiness
All AI adoption processes begin with data—lots and lots of data, properly consolidated and organised. AI is only as good as the data you have at your disposal, and if that data does not exist, or is poorly stored and haphazardly organised, then you’re going to have trouble migrating from the community of the willing to the community of the ready.
In the New York Times, Steve Lohr mentions that data gathering and cleaning takes up roughly 80 per cent of the time preparing a machine learning algorithm. That is where we take raw and unstructured data and transform it into structured data that can be used to train an ML model. Once that has been done, the possibilities are nearly endless.
Our own research has shown that, once employed, AI can help organisations select and prioritise projects, identifying launch-ready ones sooner and removing human biases in decision-making.
AI can help provide faster project scoping, planning and reporting. It can also help create and implement sophisticated advanced testing systems and software that once were only available to the richest companies engaged in the largest and costliest projects.
However, these dividends are only available if an organisation is ready to accept and deploy the technology. There is a broad spectrum of readiness among business organisations, from “all-systems go” to “don’t know where to start” orientations.
The AI readiness spectrum checklist
You would never start out on a long car trip without first finding out if people within your group have a driver’s license. The same basic assessment must be conducted to determine whether your organisation has the mindset, culture, and people necessary to create complete readiness. How can you tell how ready your organisation is? The following questions do not touch on all the concerns, but they capture major issues that must be resolved before you are ready. Be frank in answering each question and give your organisation a score, ranging from one (least ready) to five (most ready). If the score falls below 24 (an average score of three on each question), then you have some work to do to build readiness:
| The AI readiness spectrum checklist | |||
|---|---|---|---|
| 1. | Do you have the people and patience to build an accurate inventory of all current and past projects, including the latest status updates? | This is essential information that will help you determine the scope of an AI implementation project. | |
| 2. | Do you have the resources to gather, clean and structure your organisation’s data? | Many organisations have data on all aspects of business operations, yet it is hidden away in physical file cabinets or stored digitally in different IT platforms. You need all the data collated and organised on a single platform so that you can get a complete picture of your project. | |
| 3. | How ready is your organisation and its people to abandon old management habits, such as monthly progress reports that will be rendered redundant by AI? | AI possesses great transformational potential, but only if the technology is used the way it was intended to be used. Asking machine learning models to pump out monthly progress reports in the same format done before AI arrived is a poor use of the technology. | |
| 4. | Are you prepared to invest in training staff on how to use the new technology? | It will prove very difficult to fire all existing staff and hire all new staff with more familiarity with AI processes. The best organisations are those that look to up/reskill employees on how to get the full value from ML models. | |
| 5. | Are your senior leaders prepared to hand over the reins on the high-stakes decision to implement AI applications? | There is little room for naysayers when it comes to implementing AI. Steps must be taken to educate senior leaders on the potential and limitations of AI solutions so they will be confident to lean on the technology to make certain decisions. | |
| 6. | Does your organisation tolerate mistakes and setbacks to allow time for the organisation and technology to grow together? | Some organisations simply don’t tolerate failure and are destined to be disappointed with AI solutions, particularly if they don’t work exactly as promised from the beginning. AI is not a plug-and-play technology platform; it requires organisations to evolve and learn how to use it to full advantage. | |
| 7. | Does your organisation have an executive sponsor with both the expertise and credibility to lead an AI transformation? | Your employees want to know that the executive sponsor has intimate familiarity with the technology and can explain precisely what it can and can’t do. | |
| 8. | Does your organisation have the patience for transformation that can take years to fully develop? | Leaders who are not ready to delay gratification will likely not have the patience to see AI transformation reach fruition. Patience is the lifeblood of AI transformation. | |
There is no doubt that AI and machine learning models are the future of project management. The uncertainty, then, is not about whether your organisation should embrace AI; it’s about whether it is willing and ready.
These questions can only be answered through a process of unblinking self-assessment. Time to go forth and ask some tough questions. The payoff could be the ability to be among AI adoption leaders rather than being mired among the laggards.
- This blog post represents the views of its authors, not the position of LSE Business Review or the London School of Economics.
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The build readiness checklist is very well described in easy to understand. Thanks to both of you.
~Marco Mongalo, PMP, MBA, MSc
Very useful article and checklist. Just a small side Information to „ You need all the data collated and organised on a single platform so that you can get a complete picture of your project.“ —> most important you have your data structure and organised and then they can be connected via data integration platforms (with no need to import and save data on the additional „one“ platform). Microsoft Azure offers this but also many other companies.