There is a lot of hype surrounding Artificial Intelligence (AI) and the vast majority of vendors are actively promoting AI in one way or another. AI is being portrayed as the new technology that will solve problems and unfortunately this has created a (possibly false) sense of overreliance of its applicability as the “solution to everything”, which is rapidly developing amongst many senior executives. Perhaps that promise may be true in the future, but currently there is a great deal of work yet to happen to enable organizations to truly leverage the benefits from AI. Another factor that hinders organizations’ ability to get value out of AI is that there is some confusion around what exactly AI is as there are many definitions and depths to AI. It is then important to get some clarity around both the definition and the desired outcomes. AI is not defined by a single technology. Rather, it includes many areas of study and technologies behind capabilities, such as voice recognition, natural language processing, image processing. These technologies and capabilities offer different value propositions to different sets of problems.
Leveraging AI to add value to the business is not easy and straight forward because for most organizations the ‘use cases’ for AI value realization are not that apparent.
At a holistic level, the value is typically either greater customer intimacy, increasing competitive advantage, improving efficiency or a combination thereof
Having a clear view of the relevant business issues will guide leaders in separating hype from reality, setting realistic goals and proving that AI is able to deliver value for the organization. At a holistic level, the value is typically either greater customer intimacy, increasing competitive advantage, improving efficiency or a combination thereof. At a more granular and practical level, it will likely come down to specific ‘use cases’, where key performance metrics and underlying issues can be easily identified, corrected and then measured to determine success.
Exploring and agreeing on a specific use case in itself can also be an issue for many organizations. This is not necessarily because business staff are unaware of these pain points but more because they are so busy and overwhelmed with their day to day workings that they become unable to see systemic opportunities for improvement.
A good approach is to run a full day SCRUM session with key participants. The SCRUM sessions, when conducted properly, can help provide a focused and systematic way to not only get a detailed understanding of the gaps and pain points in any given business area or topic but also help priorities which ones are likely to deliver the best benefit when AI is applied to resolving them. For example, in a typical SCRUM session, the first half day is focused on identifying the top prioritized set of problems and example use cases. The second half of the session then focuses on hypothetically applying various AI technology concepts and agreeing what level of AI depth is required, so as to pick the best combination of a business use case and the best AI approach on that specific use case. This is likely to give better return in terms of value for your AI efforts.
In summary, AI can be used in one of six key ways: (1) dealing with complexities, (2) probabilistic predictions, (3) learning, (4) acting autonomously, (5) understand and (6) reflecting on a purpose. Organizations should be prepared to take some risks and be particular about which problem areas they should focus on and then be clear of which type of AI technologies and concepts will help. If you are prepared to challenge your initial assumptions, tackle this with an open mind and take some calculated risks then you may uncover huge benefits from AI leverage.