Future-Proofing for the Age of AI

The session that started the Consortium. Commercial excellence and transformation leaders from sixteen global enterprises, no speaker and no deck, working through two questions: what future-proofing means in practice, and how AI is being received inside their commercial organizations. Contributors are described by role and industry, under the Chatham House Rule.

The premise

The founding argument for the group was that the job itself is unusually isolating. Driving behavior change to produce growth across a large enterprise — through influence rather than authority — is done by a very small number of people worldwide, and the rooms where they might compare notes are usually convened by someone with something to sell.

• Invite-only, free, and closed to purchase. Membership follows peer referral rather than a fee.
• No vendors, sponsors or consultants working the room. Members are not there to be sold to, and pestering someone afterward ends a membership.
• Say yes only if you will attend. Places are capped so that everyone gets to speak, and a no-show costs someone else a seat.
• Cameras on and present, or say so. Half-attention from an inbox is worse than an empty chair.
• Small on purpose — around a dozen to fifteen, deliberately under the size where a discussion turns into an audience.

What membership was set up to provide was laid out in the same session: a monthly roundtable capped for engagement, a member directory and group so people can find and contact each other directly, benchmarking studies and member case studies, peer mentorship with introductions made on request, and a podcast built around members rather than the host — personal profile being one of the things the job seldom leaves time for.

The short version

• Future-proofing is a capability question before it is a technology question: what process, data, competencies and systems will be needed to win, and what is missing today.
• Run the exercise in both directions — forward from today and backward from a distant horizon. The two produce different answers, and the difference is the useful part.
• Plan for several scenarios rather than converging on one, and build against what they have in common.
• This wave of AI met little resistance because it assists. The reception when it starts recommending the price is the thing to watch.
• The gap in the room was between how exciting AI is and how little it had changed anyone's commercial model.
• The value showed up where AI unlocked action that would otherwise have gone untaken, rather than where it saved time.

What future-proofing means when you have to act on it

The term is loose enough to mean almost nothing, so the room was asked to define it. Four definitions came back and they fit together rather than competing.

A software and data CEO gave the structural version: build a picture of the environment at several horizons — the market, the industry, the forces pushing on you — and locate where the collisions and the openings are. Then look honestly at the organization today and work out what it needs to become in size, shape, skills and capability. Then write the transformation roadmap that closes the distance.

A head of commercial excellence in specialty chemicals reduced it to one sentence: building the capabilities to win in future. Which capabilities is answered the same way each time — the processes, the data, the people competencies and the systems. The market question is where to play; this is the internal half of the same exercise, and the half more often skipped.

A corporate sales and commercial excellence lead in chemicals brought a method the room liked. A workshop on global trends, filtered down to the ones that matter for this company, then a North Star: what does the organization look like in 2050? And then, working back from that, what should be started now. What made it useful was seeing the distance — in some places much further than expected, in others closer.

A commercial excellence and marketing lead in lighting added the correction that keeps the exercise honest. Future-proofing is read as a technology forecast, and the conditions that move a business are as much economic and political. Much of that is beyond prediction, which is an argument for preparing rather than forecasting.

1. 1

   Run it forward

   From today into one, five and ten years. This is the rational extrapolation, and it inherits today's assumptions along with today's constraints.

2. 2

   Run it backward

   Backcasting: start ten years out, imagine the state fully, then work back to what has to be started now. Imagination first produces a materially different answer from extrapolation first.

3. 3

   Compare the two

   Run both as separate streams and look at where they disagree. A head of commercial excellence in industrial machinery made the point that the divergence itself is the finding.

Why this wave met less resistance than the last

Asked how AI is being received inside their commercial organizations — opportunity, threat, excitement, doubt — the room reported far less resistance than earlier digital transformations had produced. One EMEA strategy and commercial excellence VP explained why, and then named the condition attached to it.

The current wave is chatbots and agents. It helps someone do what they already do, faster. Nothing about it challenges their judgment, which is why the reception has been warm.

A global commercial excellence leader in pharma described an organization that had gone further than her previous ones despite operating in one of the most regulated industries there is. Compliance is present at each step, and the company embraced it anyway, because a lean organization with ambitious goals has little alternative.

Seven AI projects were running inside her commercial excellence function alone. A commercial excellence manager at another pharmaceutical company described the enablement side of the same posture — a company-wide AI week of workshops on getting started, aimed at the whole population rather than a pilot group.

A commercial excellence lead in medical devices separated the two problems that were being discussed as one.

• The structural question: how AI gets integrated into the systems people already work in — next best action inside the CRM, and the rest of the workflow around it.
• The individual question: whether each rep and each manager personally uses it, which no amount of system integration settles.

His most promising ground was training. Role plays, competitor analysis and practice conversations were producing results with small tools and little investment, and matched how the newer intake prefers to learn.

The gap between how exciting it is and what it has changed

The most useful contribution of the session was a leader saying his industry's results were thin. A regional VP at a global ingredients business opened by disclaiming any expertise and then described what AI had produced for him so far.

• Call report efficiency — record the meeting, generate the summary, route it so the full write-up is left undone, linked into the key decision points.
• Pipeline prediction — a nine-figure euro pipeline of opportunities, with AI predicting how it lands against history, into the P&L.

His reason for being in the room was that he suspected he was missing something and wanted to hear what other industries were finding. It is worth noting how that question resolved: by the year-end session two months later, the same participant had built two of the strongest AI cases the Consortium has published — years of unread sales visit reports synthesized into proprietary market insight, and an eighteen-to-twenty-four-month innovation cycle collapsed into a single workshop.

Where it stops being efficiency

A global commercial excellence director in building materials had arrived at the same conclusion by a different route, and gave the session its sharpest framing. His organization takes any decent idea and runs a small project at it rather than waiting for a strategy — and what he learned from doing that was not about productivity.

1. 1

   Global reports that were previously unreadable

   Interaction and customer reports flow into the CRM daily in volumes beyond what a person can digest. People read their own scope; the global picture is the insightful one and the one few have time to assemble. With AI doing the assembly, decisions now get made at a global level that would otherwise have gone unmade.

2. 2

   Notifications that produce a call

   A small tool that alerts a salesperson when something shifts in their portfolio — a change in a buying pattern or a payment pattern. Modest technically, and it drives action that would otherwise not have been taken.

3. 3

   Hiring for the capability now

   Open positions in his scope are being filled with an eye to that skill specifically — recruiting the mavericks who will find the use cases, on the expectation that the capability spreads outward from the people who have it.

The two ends of the spectrum

On whether people fear for their jobs, the room split — and the split ran along industry lines more than seniority.

A general manager in specialty chemicals described his organization as bipolar on the question: people who had jumped in and were exploring it fully, and people with real anxiety about whether their know-how was about to be automated. The work in the middle is showing the second group how it helps them rather than replaces them.

A commercial excellence and innovation director in chemicals pushed the question further down the organization, to the sellers themselves. In his experience AI is still far from their daily lives — they see the job as visiting customers and having conversations, and the technology has not reached that. He drew the parallel that will be familiar to anyone who has run a CRM rollout: sales teams are seldom the people who love new technology, and this is unlikely to be different.

The counter from the host was that the case has to be made in terms of the seller's own day. In a technical industry, identifying where a customer's application creates a need is exactly what these tools are good at — which means the pitch is that it makes you look brilliant in seconds, not that it improves the company's data.

The medical devices lead offered the reframe most of the room had already reached.

He had been struck by how far it had spread among senior reps in particular, once a sanctioned tool arrived. The building materials director reported no fear at all in his organization — the reaction there was to the productivity on offer, and his summary of where they had got to was that they are scratching the surface.

The question left open

The session closed on the thing the participants had left unsettled: what is allowed. Whether company information can go into a public model, whether the work should be walled off, and who decides. Governance was behind the adoption in most of the organizations represented, and the gap was being filled by individual judgment.