With CEOs focused on growth, where are they going to find it?
After a brief hiatus from posting, I am back in a new role with Analytic Process Automation company Alteryx. You might not know what “APA” is or means, but if you take the time to read on, I think you will appreciate the idea.
Of Walmart’s fiscal 2021 $559 billion, $11 billion was potentially attributable to forecasting and supply chain processes driven by machine learning and artificial intelligence.
Similarly, Procter & Gamble brands may have contributed $1 billion to fiscal 2020’s $71 billion by way of trade promotion logic and decisions informed by ML and AI.
I only guess this happened, because I really don’t know. These are among the most progressive analytics organizations in their industries. Data science and solving “wicked problems” are at the core of their cultures.
What I can say for certain, is that McKinsey research found among their clients that ML and AI applied to business processes like forecasting and promotion typically produce improvements in the 1-2 percent range of revenue, as opposed to less sophisticated analytic methods. No matter the specific figures for Walmart and P&G, they are probably impressive nonetheless.
That is exactly the kind of growth CEOs are looking for right now.
In May of 2021, Gartner announced that its survey of CEOs found “over half report growth as their primary focus and see opportunity on the other side of the crisis.” Technology change and investment followed and “when it comes to specific technologies, CEOs see artificial intelligence (AI) as the most industry-impactful technology.”
View a chart like the below and you can see why. Across all industries there are significant economic opportunities for the taking with advanced analytics and AI.
If not already, CEOs will be looking for their leadership teams to recommend ways to capture some of this value. Shown above by industry, are functional use cases behind each of the bubbles in this interactive diagram from McKinsey.
The challenges to achieving value with AI have been well documented by McKinsey, Tom Davenport, Doug Laney, MIT, Bernard Marr, and many others. From leadership and culture to strategy and project approach, the way AI and advanced analytics unfolds within an organization makes a huge difference to whether value happens and scales.
What I find universally true, is that a dual focus on scale and continuous improvement are features of those that get it right. Thinking too small in scope or fixating on an endpoint risks mediocrity at least or failure at worst.
If lucky or intentional enough to succeed, once that first opportunity for improvement is realized, it is only the beginning. With success and maturity comes quickly the realization that:
- Outcomes for some use cases can be improved further with new or alternative methods, or reach a point of standardization, automation and routine so they can (or should) be owned and operated by the business, and
- A pipeline of use case suggestions opens (or bursts) as the business learns to recognize what an AI use case looks like and how it creates value. Much like Walmart Chief Data Officer Bill Groves describes.
Now here’s the difficult part. For mature organizations with data science teams, scaling use cases in pursuit of more value is getting harder with a fixed amount of experienced people. You either need to improve productivity or recruit others in the organization, such as skilled analysts, to help as needed. Perhaps both. I am finding instances where APA can add a lot of value and scale AI use case execution.
Organizations without data science teams or just getting started sense urgency to act. Faced with hundreds of options across every category of the analytics process, they simply cannot execute a scalable AI roadmap if needing to master so many different tools and methodologies.
That is very challenging to accomplish when starting out, and without a good deal of automation and collaboration. APA intends to consolidate, automate and scale the analytics process up to and including AI and ML, alleviating these barriers.
This allows a focus on outcomes and improvement. Organizations shown to already have taken shares of the $15 trillion AI pie did so with use case portfolios targeting smaller but strategic projects with combined value that materially added to the top and bottom lines.
Ultimately APA is about democratizing analytics so that anyone needing or wanting access to data and insight of any variety – descriptive through prescriptive – has the means to do so.
For CEOs and their leadership teams focused on growth, I suggest they explore the many ways APA can help them capture the economic value potential of AI.
References:
May 11, 2021 – Gartner, Gartner Survey Reveals Most CEOs Anticipate an Economic Boom Rather Than Stagnation Over the Next Two Years
April 27, 2018 - McKinsey, Achieving business impact with data