Why AI will elevate instead of replace commercial teams

As AI rapidly reshapes commercial organizations, leaders are grappling with a fundamental question: What will distinguish winning sales organizations in the years ahead? Monique Buch, chief commercial officer at Covestro, believes the answer lies in using technology, especially AI, to elevate commercial teams to help build stronger customer relationships, support cross-functional collaboration, and create value. In this conversation with McKinsey Partner Alexander Dierks, she discusses why implementing AI should be a business transformation instead of an IT transformation alone, and why extracting value requires fostering a culture of embracing data, experimentation, and continuous learning.

What follows is an edited version of that conversation.

Alexander Dierks: As you think about commercial organizations over the next several years, what will define success?

Monique Buch: If I look at the next years of commercial success—and for me that really goes back to our B2B environment, that’s my home turf—it will be increasingly defined by our capability to deepen customer–partner relationships and specifically to deliver solutions, or solution-oriented offerings, that go beyond the product and beyond the molecules.

Our customers are facing increasingly complex challenges around sustainability, efficiency, speed, and supply chain resilience. They are expecting us to contribute to solutions as well.

So what is commercial success then? It’s really about strengthening long-term relationships. It’s delivering integrated solutions rather than stand-alone products. And it’s leveraging data and digital tools to improve commercial effectiveness and increase speed.

Ultimately, for us, this means value over volume. Success is measured by how much value we create for our customers and how much of that value we as an organization can capture as our fair share.

Alexander Dierks: Looking a few years into the future, to 2028 or 2029, what will the ideal commercial organization look like?

Monique Buch: If you take a step back in history, you had the traditional salesperson, traveling with a box under their arm and a story to tell. That has already diminished quite a bit. Instead of trying to sell out of the box, you try to provide a solution to your customer. That means you listen more than you talk. My grandmother always said you have two ears and one mouth—and there is a good reason for that.

Going forward, I expect commercial organizations will become significantly more data-enabled and customer-centric, with information at people’s fingertips. Also, they will have to become much more cross-functional. There is more complexity that needs to be digested, and you need to do that efficiently in order to be fast and create value.

I think the biggest difference will be in how commercial teams will spend their time. Today, a large share of time still goes into administrative and analytical tasks. In the future, these will increasingly be automated through the tools and AI that are becoming available. This change will allow our teams to focus on what really creates value: engaging with customers more deeply, codeveloping solutions from an earlier stage, and driving innovation together with them.

As a result, commercial organizations will feel less like traditional sales functions and more like integrated customer solution partners. We’ve already seen the shift from hard-core selling to consultative selling. This will take it another step further.

Alexander Dierks: Where do you believe AI will have the biggest impact on performance for the future commercial organization?

Monique Buch: I think it’s about decision-making. With data and analytics, you can drive better decision-making in commercial teams. That starts with deeper customer insights, but it also includes better forecasting, more dynamic pricing, and identifying commercial opportunities much earlier because you have more data that you can analyze and utilize much faster.

One example is our work on identifying new customer opportunities. We use AI to identify relevant prospects and potential opportunities at a scale that would be impossible through traditional approaches. The result is that sales teams can focus their efforts much more effectively.

Another example is the customer service and supply chain interactions. We increasingly automate them so we can respond faster to transactional requests. This improves the efficacy of our processes and enhances the customer experience we deliver. Interestingly, we see a spillover effect, where tools are increasingly used beyond what we originally targeted them for. People work faster, with better information, and as a consequence create additional commercial impact.

Underlying all of this is the inherent value of speed. Speed is more than a linear function; it is exponential. When we go quickly through a cycle, we learn, and the next cycle already benefits from that learning. That’s where the exponential effect comes in.

Alexander Dierks: What changes are required in the operating model to capture the full value of AI in sales?

Monique Buch: I think you need to have a fundamental redesign of the operating model. It goes beyond incremental changes. I see three major shifts. The first is from functional silos to cross-functional teams. Sales can no longer operate in isolation. It needs to work much more closely with marketing, IT, supply chain, R&D, operations, and other functions. Those teams need to work seamlessly together to create value for the customer.

The second shift, I think, is that the whole operating model is going from traditional sales roles to more hybrid capabilities. I think the T-shaped model of broad skills and deep expertise in one area will become more pronounced. Commercial teams will need to combine their customer-facing skills with sufficient data literacy and digital fluency to be capable of using and capturing value from AI. One example: being capable of writing the right prompts. New roles will emerge at the intersection of business and technology.

And then third, I think we will have to shift from fragmented data to shared platforms. AI only delivers value when it’s built on consistently high-quality data. That means people need to pay attention to what goes in. If you put nonsense in, you may get faster but you get more nonsense out. That creates more problems than value. We need to establish a common data foundation and platform that is accessible across the organization and that enables real-time insights and aligned decision-making.

Overall, this represents a shift toward a much more integrated, agile, and insight-driven commercial ecosystem. The organizations that succeed will be those that both adopt AI tools and embed them into new ways of working and decision-making across the entire function.

Alexander Dierks: What sort of tasks will AI take over, and what will remain uniquely human?

Monique Buch: I don’t think AI will take over the commercial role. I think AI will increasingly take over analytical and administrative tasks, and the role of commercial teams will fundamentally shift to higher-value, more human-centric capabilities. In fact, three areas become even more critical.

The first is the ability to build deep, trust-based relationships. In complex B2B environments, decisions are rarely purely rational. We like to think they are, but quite often they are built on trust, credibility, and long-term partnership. AI can support that, but it cannot replace it.

The second is contextual and strategic understanding. AI can generate a lot of insights, but it is still the human who interprets them and puts them into the context of a customer’s industry, business model, and strategic priorities to translate them into meaningful actions.

The third is cocreation and problem-solving with the customer. The future of sales will increasingly be about working alongside customers to solve complex problems—whether with sustainability, performance, or supply resilience. That requires judgment and collaboration. You can’t outsource that, and I don’t see AI taking that over.

In my view, AI will elevate the role of sales rather than diminish it. It removes a lot of the routine work—which, in all fairness, most salespeople don’t like anyway—and equips teams with better insights. The real differentiation will come from how effectively people use those insights to build relationships, create relevance, and drive impact for customers.

Alexander Dierks: On a scale of 1 to 10, if you were to look at it, how far along would you say your organization is on that journey?

Monique Buch: I think we’re close to a five, maybe a six. On the one hand, we see very promising AI use cases delivering real value already. On the other hand, the main challenge is scaling those across our organization and embedding them in our daily workflows so that this really becomes the new normal for doing things. That transition is critical because without successful scaling across the organization, AI risks become a hurdle not an enabler.

Fundamentally, most people want to move away from painful situations and toward a brighter future. I often say that sales is simple but not easy. It is about how many opportunities you have, how much value is there per opportunity, and your chance of winning each of them.

I think many AI offerings play into just that by creating more opportunities, making sure we maximize the value per opportunity, and increasing our chances of winning. People will adopt these offerings in their ways of working if they make their life easier and bring them value—and if there is an incentive system in place to encourage them to use it, a chance to collect more of their bonus. I think we need to play to both.

Alexander Dierks: What is the most important lesson you’ve learned so far?

Monique Buch: For me, the most important lesson is: It starts with a real business problem, instead of technology. In that sense, this is very similar to what we saw in previous waves of technology transformation.

AI initiatives only create value when they are clearly linked to specific commercial objectives. That requires a strong data foundation, investment in skills and capabilities, and very clear ownership and accountability. Leaders should avoid treating AI as a purely technical initiative. It is a business transformation topic.

As a mechanical engineer, I’ve always been fascinated that it took decades to fully optimize electric motors because people continued designing factories as if they were still operating steam engines. That’s what can happen with AI as well. If we continue to conduct business the old way and simply add a new gadget, the impact will remain limited.

Alexander Dierks: What aspect is most often overlooked in such a transformation?

Monique Buch: Organizational change. AI adoption requires new skills, new ways of working, and strong leadership commitment. It is about bringing people along on the journey, fostering a culture of experimentation, allowing people to make mistakes, and creating psychological safety. It is about enabling data-driven decision-making across the organization. True transformation requires more than technology alone.

Ultimately, AI will change how our commercial teams work. The real competitive advantage will come from combining human expertise with AI-powered insights and treating data not only as a technical asset but as a strategic capability for the entire organization.

Alexander Dierks: Any final thoughts for commercial leaders?

Monique Buch: AI will drive a significant transformation on its own. If you add quantum computing, you put it on steroids. One question I think we need to start addressing is how we psychologically deal with that. I don’t have the answer yet, but I think it is one of the important questions ahead of us.

The challenge goes beyond the pace of change. It is also about the number of changes people need to cope with in a certain period of time. How do we get comfortable with lifelong learning at high speed? How do we continue adapting? In our industry, you still sometimes hear the view that knowledge is power. In all fairness, that probably stopped being true a long time ago, but some people still hang on to it.

The organizations that succeed will be the ones that embrace continuous learning and adaptation as core capabilities.