Does Manager Experience Matter In An AI-Powered World?
Knowing your worth.
Background
There is a rush for middle and senior managers in organisations across various business domains to upskill themselves to stay relevant in an already digitalised world. Data literacy was the big theme, but before that job could get done, they are now confronted with AI literacy. “Older-generation” managers are being sent back to the classroom to learn about data, statistics, and coding. Many do not succeed; they simply can’t cope with the technical curriculum.
Since leaving my job as a regional chief analytics officer for a global bank and co-founding my own boutique consulting practice, I have witnessed much of this phenomenon over the last decade as an adjunct professor designing and delivering various adult learning curriculum in the data and AI domain. I find myself wondering: How should managers cope in an AI-powered world? What competitive advantage can they bring to the table with their present and past work experiences? Does experience even matter? And so I dedicate my 50th weekly article to answering these questions.
(I write a weekly series of articles where I call out bad thinking and bad practices in data analytics / data science which you can find here.)
The Manager
The role of the manager can be simplistically summed up as directing, connecting, and correcting the work activities of subordinates. Directing is about translating organisation objectives into relevant functional domain objectives and vision. Connecting is about coordinating and consensus-building. Correcting is about clarifying, reinforcing objectives, and bringing folks back on track. This may seem overly simplistic, but the purpose is not to trivialise the work of the manager, but rather to abstract it into broader principles and attributes so that we can better investigate the implications of AI.
Each role requires knowledge, experience and finesse. To effectively direct the work, the manager needs to have clarity on the destination and the routes to get there. To effectively correct the work, the manager needs to be able to see the deviations and understand their implications. To connect the work, the manager needs to be aware of the “assets” across the organisation network as well as the “constraints” (both non-negotiable and negotiable) of all stakeholders involved.
AI & Manager Experience
I would argue that manager experience in correcting will be most devalued in an AI-powered world. Meanwhile, the manager experience in directing will still have some value. And finally, AI would result in manager experience in connecting to be even more valuable. The table below summarises this.
All activities within an organisation will increasingly incorporate elements of AI, be it implicitly or explicitly. In my prior article, I talked about background vs foreground AI, and so I won’t repeat myself here. But suffice to say, the manager needs to be aware of where the AI is “integrated” into the workflow. The workflow is very likely re-engineered to exploit the benefits of AI’s computation and decision automation capabilities. So the introduction of AI will clearly impact the manager’s role in correcting. The manager’s past experience in correcting will be sorely inadequate in these AI-powered scenarios. The manager will struggle to understand if work is truly deviating from expectations or is it just regular variation. Any upskilling will have to enable the manager to recognise when the AI-powered workflow is not conforming to expected parameters. This means having decent knowledge of statistics. This means having an understanding of the limitations of AI. And this is indeed going to be a tall order for many managers.
Consider the example of a credit card product manager. In the days before credit scoring, the product manager would work closely with the Credit Risk function and distribution units to create rules and policies to grow their portfolio profitably. Today, a product manager needs to have a fairly in-depth understanding of data-driven credit scoring to have an equivalent conversation with their Credit Risk counterparts; otherwise they would be run roughshod over. In the near future, I can imagine banks developing a bunch of deeply integrated AI solutions for identity authentication, financial affordability, economic resilience, character up-worthiness (i.e. whether a person is committed to repaying their debts when they find themselves in a pinch; current credit scorecards don’t model for this), making it even harder for the credit card product manager to figure out if things are truly operating as per assumptions.
What aspects of the manager experience in directing would still be valuable in an AI world? Firstly, the ability to ask good questions is chief amongst them. (I’ve written a piece about this skill in the past if you would like to check it out.) A good manager must have the ability to make the right enquiries when translating broader organisation agenda into the context of their own functional domain. The ability to ask the right questions allows the effective manager to continuously learn enough about the attributes of new technology and strategies to stay relevant; but it also starts with a deep understanding of their own work domain. You can tell when a manager doesn’t have this skill — they will insist on being taught explicit use cases applied to their work domain for every single new piece of technology. Nonetheless, where the manager will likely slip up is in their ability to augment their functional domain KPI to incorporate the change in performance value brought on by AI.
Finally, an effective manager will find that their experience in connecting actually becomes more valuable in an AI world. Through the use of AI, we are in fact exchanging complications with complexities; we are introducing more inter-dependencies and brittleness. An effective manager’s experience in stakeholder management will be much sought after in such a scenario.
Conclusion
There are those that believe that the role of the manager will not be disrupted by the advent of AI because the manager does not undertake in primary “hands-on” work that the machines and software can foreseeably replicate. But the act of directing, connecting and correcting will change, even if it’s not directly replaced by AI. To stay relevant, an effective manager must be able to excel in all these 3 competencies. They need to recognise where experience matters and where it will be devalued. In those areas where experience will be devalued, managers need to expose themselves to get new replacement experiences — e.g. ask to be part of an AI implementation taskforce so that they can re-build their experience in correcting. And in those areas where manager experience is more highly prized, they should make themselves more visible.
