Walking a Tightrope: Can Gen AI help? A challenger perspective
Saar Ben-Attar
Helping leadership teams drive strategic collaborations for outsized impact | Published Author
Those of you who have worked with us, in one form our another, know how curious we get at Ascent Growth Partners Pte. Limited about co-discovering new insights with our clients. Discovering new insights requires us to slow down, to explore lesser-known fields and unfamiliar signals. We ask questions about phenomenon that don't seem to fit our current management paradigms or agreed business plans.
These questions can start with that uncomfortable feeling that despite our best plans, in navigating an unfamiliar environment (even just in part), we may have come across a navigational error. No need for alarm, such navigational errors are rather common. In fact, they may be a very good thing we have stumbled on.
Let me illustrate with a question that got us onto a new path. Could we develop scenarios at 10x the speed organizations currently do?
Some context first. A few months back, I was in conversation with a Generative AI expert. They are not easy to come by with, yet this person, sitting on the other side of the planet, seven time zones away, has been working with organizations worldwide to challenge their business models and develop AI-assisted ones. I was intrigued by his experience.
You see, in most organizations, scenario planning is a discrete, out-there activity. In the words of futurist Amy Webb, how we execute on our business plan is undermined by the pace of change we are experiencing.
Plans are out-of-date within mere months of being finalised and taking a team through a proper scenario planning exercise would be hard to sustain under such conditions, and plans kept up-to-date. Scenarios take days to prepare, often weeks to explore and more time to navigate a set of stakeholders wondering why our business plan have aged.
I posed this question to our AI expert - could we develop scenarios at 10x the speed we currently do? Could we test them in real-time for actionable insights, using generative AI?
For scenarios (and the broader practice of Strategic Foresight) to become a useful co-pilot, we need to throw away the notion of how they have been done so far. We must bring significant diversity into the discussion, often beyond the diversity that resides within our leadership teams. We need a way to test new scenarios quickly and with largely incomplete information, as our environment continues to shift. Every new week might bring with it a geopolitical fracture, a banking crisis or shifts in global capital flows (followed by talent migrations). With such changes, scenarios are needed more than ever. But we need them in essence ‘on tap’.
This is where our conversation took an unexpected turn. It turns out that Generative AI is rather good at something we think of as uniquely human - crafting a forward-looking story, a scenario if you will, based on two or more seemingly unrelated trends. Take a debt crisis hitting an emerging market (say a key market to your business, to illustrate), mixed with a growing trend of talent mobility, as digital nomads head in greater numbers to new destinations (nations where your business has no operations, again illustrating).
In using Gen AI, we can have such trends interacting with one another in new ways, and so new scenarios are formed. Gen AI does this well and faster than us. In fact, at 10x the speed, and with some remarkable narratives, that we can logically test (Here, Gen AI won't even get upset with you for persisting with 'Why' questions posed multiple times, to get to the core of its argument).
We have come to test scenarios that resemble current business plans, with subtle shifts added in, and discover new ways to make them more resilient. Each scenario adds new insights to the mix and helps to estimate the impact on our execution plans. Gen AI can also help us generate breakthrough scenarios, where changes are more pronounced and can result in significant impact over our planning horizon.
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In our Ascent AI Labs, we've been working with such CustomGPT co-pilots. Here you can:
?? Evaluate how trends and new signals affect the resilience of your plan to such forces (our co-pilot would even suggest case examples from recent years and insights into what you and your team may be experiencing).
?? Generate, in a live session, new scenarios - specific adaptations of your business plan or more speculative scenarios to test.
?? Experience how business plan can impact a broader ?ecosystem, where the business plan may be most susceptible to an opportunity upside or downside risks that one could still navigate around.
?? Probe these scenarios with questions and decide whether any changes are warranted - together with our team.
My conversation, some months back, with this Gen AI expert, turned out to be more than a curious exchange. It brought us into a truly creative collaboration, bringing what we know about how we Humans approach strategy execution with new insights into the contributions of Generative AI.
With every new week, teams have been spending time in our lab, refining these CustomGPTs. Imagine having a Fintech, aiming to disrupt consumer lending, working alongside seasoned bankers, in a scenario that is meant to test and stretch our resilience. We will report back on these scenarios, some imaginative, others 'business plan uplifts' in the coming weeks.
Is there an area you have in mind, where your business plan could benefit from being tested in new scenarios? No need to go offsite for this one. You can join us in one of these sessions or for an online demonstration instead.
Have a good week ahead.
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Former Recruiter, ? Now In-house Hiring Strategist | I Help SMB’s Leaders Hire and Retain Top Talent Without Costly Recruiters, Testing or Ineffective Technology
8 个月Saar Ben-Attar An interesting article. I like the idea, “What if we could….”
Mentor and Coach in Drug and Device Development and Regulatory Affairs
8 个月Help from AI, requires a sufficient use of insight and common sense. AI can not generate beyond the inner layers of the back-propagation of the neural network of the machines. Otherwise it could be GIGO (Garbage in Garbage out)!!