How AI Is Actually Changing Commercial Teams

There is a version of the AI conversation that I find exhausting. It involves sweeping predictions, breathless superlatives, and a level of abstraction that makes it almost impossible to know what to actually do on Monday morning. I have sat through enough of those presentations to last a lifetime.

The version I find genuinely interesting is the one happening inside commercial teams right now, quietly and pragmatically, as people figure out what AI is actually good for in the context of their day-to-day work. That is the conversation I want to have here.

Because AI is changing sales and partnership strategies. Not always in the ways the hype would have you believe.

Let’s Start With What AI Is Actually Good At

The honest answer is that AI is very good at tasks that are high-volume, repetitive in structure, and that require processing large amounts of information quickly. For commercial teams, that translates into some genuinely useful applications.

Lead scoring and prioritization.

AI tools can analyse behavioral signals across a CRM and surface the leads most likely to convert, with far more nuance than a manually maintained scoring model. Sales teams that have adopted this are spending less time on low-probability conversations and more time on the ones that matter.

Content personalisation at scale.

One of the perennial tensions in commercial marketing is the gap between what personalisation promises and what it actually delivers when you are trying to reach thousands of prospects. AI is closing that gap. Teams can now produce tailored outreach, proposals, and follow-up content at a scale that was simply not possible before, without sacrificing the relevance that makes personalisation worth doing.

Conversation intelligence.

Tools that analyse sales calls and meetings are giving managers something they have always wanted but rarely had: objective, consistent insight into what is actually happening in client conversations. Which objections come up most often? Where do deals tend to stall? What language is resonating? These are questions that previously required a lot of manual review to answer well.

Market and competitive intelligence.

Partnership teams are using AI to monitor competitive moves, track category trends, and identify potential partners with far greater speed and depth than a manually curated briefing could provide. The research that used to take days now takes hours, and it is more comprehensive as a result.

What It Is Not Good At (Yet)

This is where the conversation gets more interesting, and where I think a lot of commercial leaders are still finding their feet.

AI is not good at building trust. It cannot replace the experience of sitting across the table from a client or partner and demonstrating that you genuinely understand their business, have carefully considered their specific situation, and are invested in their success. That quality of relationship is still a human endeavour, and I believe it will remain so for the foreseeable future.

AI is also not good at navigating ambiguity in high-stakes commercial situations. When a partnership negotiation gets complicated, when a major client relationship hits a difficult moment, when you need to read a room and adjust your approach in real time, those are still capabilities that sit firmly with experienced commercial leaders.

The risk I see most often is not that AI will replace these human capabilities. It is that teams will over-automate the parts of the commercial process that are high in volume but low in complexity, and underinvest in the relationship skills that actually differentiate their best people.

The Partnership Strategy Angle

One area where AI is having a particularly interesting impact is in how commercial teams approach partnership development. Historically, identifying and evaluating potential partners has been a labour-intensive process, heavily reliant on personal networks and manual research. AI is changing both of those dynamics.

Teams can now use AI to scan a much wider landscape of potential partners, filtering by criteria that would have taken weeks to work through manually: shared audience profiles, complementary product positioning, track record of successful co-ventures, and even sentiment analysis of how a potential partner is perceived in the market.

What this means in practice is that the initial discovery and qualification stages of partnership development are becoming faster and more data-informed. The human work, outreach, relationship building, negotiation, and ongoing management remain as important as ever. But it is being deployed more selectively and with better information behind it.

For agencies and consultancies that build commercial value through partnerships, this is a genuine shift. The competitive advantage is no longer just about who you know. It is about how intelligently you can identify, evaluate, and activate the right relationships.

The Talent and Culture Question

No conversation about AI in commercial teams is complete without addressing the people side. And this is where I think the most important leadership decisions are being made right now.

Some commercial leaders are treating AI adoption as a cost reduction exercise: fewer people doing more volume. That approach tends to produce short-term efficiency gains and longer-term capability problems, because the people who leave are often the ones who understood the nuance and context that AI cannot replicate.

The leaders who get this right treat AI as a capability multiplier. They are asking: what could our best people achieve if they were freed from the parts of their role that do not require their full expertise? The answer to that question is usually compelling, and it reframes AI adoption as an investment in talent rather than a replacement of it.

This requires a genuine commitment to upskilling, not just deploying new tools and hoping people figure them out. The commercial teams that will perform best over the next five years are the ones where people understand how to work with AI effectively, know where to trust its outputs and where to challenge them, and have been given the space to develop those skills intentionally.

A Practical Starting Point

If you are a commercial leader trying to work out where to start with AI, I would suggest resisting the temptation to chase the most sophisticated applications first. Start by mapping where your team spends most of its time on high-volume, low-strategic-value tasks. That is almost always where the clearest early wins are.

Then ask what you would do with the time and headspace that gets freed up. If the answer is genuinely strategic, invest in the AI capability that creates it. If the answer is unclear, it is a signal that you need to revisit your commercial priorities before redesigning your processes.

AI should be in service of a strategy, not a substitute for one.

The Bottom Line

AI is genuinely changing commercial teams. Not by replacing the people in them, but by reshaping where human expertise is most valuable. The teams that will benefit most are the ones that approach it with clear eyes, a grounded sense of what they are actually trying to achieve, and a genuine investment in helping their people grow into the new capabilities it requires.

I am curious how AI is showing up in your commercial team right now. What is working, what is not, and where are the conversations still getting stuck? Share your experience in the comments or reach out directly.

Ant Da Silva is a Business Director and Marketing Communications Expert with over 20 years of experience leading brand and marketing transformation for global creative agencies, including Wunderman, M&C Saatchi, Droga5, We Are Social, and Anomaly.