Beyond the Milkman: AI’s New Playbook for Medtech Commercial Excellence

Picture the AI-enabled future of medtech commercial models. Companies generate customer insights that reflect the full history of interactions across all channels, enriched by third-party data. Marketing teams automatically incorporate these insights to create personalized messages for individual buyers. Sales reps get tailored scripts and recommended next-best actions that leverage those insights, improving conversion rates. AI agents provide 24/7 customer service support. Patients get better outcomes, customers get a better experience, and medtech companies see improved top-line growth and margins.

Most medtech CEOs see AI as one of their top priorities, yet many companies still struggle to realize value from applying it. For a subset of commercial innovators, however, the AI-enabled future is becoming a reality, leading to tangible impact on the P&L.

Over the past 15 years, we have conducted four global medtech benchmarking studies, asking more than 10,000 employees to assess their commercial readiness in an evolving market environment. We also benchmarked 150 medtech businesses based on their P&L and organizational data and interviewed senior leaders to understand their strategic priorities and interpret the data.

Our goal was to help medtech companies modernize their commercial model and provide access to their products and solutions to more patients. The results are clear and consistent across all studies: companies that invested in transforming their commercial model and developing new capabilities have dramatically outperformed their peers in growth, margins, and ultimately shareholder returns. (See “The Milkman Series: A Growing Record of Market-Tested Commercial Insights.”)

AI is the next step in that evolution. As the technology advances, leading companies are setting their ambitions accordingly, seeking to use it to transform every aspect of their commercial model. (See the sidebar “A Global Diagnostics Leader Builds an AI-Native Commercial Model".) In that way, they are giving temselves a critical and lasting advantage over the competition.

Beyond Cost and Growth Trade-Offs

Overall, global medtech markets remain resilient, thanks to aging demographics in many countries, improved access, and the consumerization of health care. But the industry continues to face familiar headwinds such as stricter regulation, public cost pressure, and payer and provider consolidation, along with new challenges like geopolitical turmoil and tariff-related uncertainties. At the same time, innovative, low-cost players continue to take market share from incumbents.

In response, many medtech companies have started to focus on reducing costs to boost profitability. But such measures are not sufficient to deliver longer-term shareholder returns. The bigger priority is accelerating profitable growth. Moreover, while traditional measures typically either lowered commercial costs or accelerated growth, AI helps companies move beyond that trade-off. It makes commercial operations both leaner and more productive and thus improves margins while increasing revenue.

AI reinforces the winning moves from our earlier studies. But the rise of AI has the power to unlock significant, additional impact from these foundational strategies and to automate and personalize customer engagement. In that way, AI represents the next step on the journey. It does not replace sales reps. Rather, with the right governance and humans in the loop, it makes them stronger and more productive. And in our experience, it makes them happier and more engaged.

Gauging Next-Generation Commercial Capabilities

As in previous studies, we surveyed executives at leading medtech businesses around the world, asking them to assess their maturity across commercial processes. We also benchmarked the commercial productivity of leading medtech players. We combined this data with our insights from working closely with medtech clients and helping them transform their commercial model.

Our survey reveals that leaders at medtech companies worldwide continue to rate the maturity of their commercial operations, particularly their next-generation commercial capabilities, as only basic to intermediate. (See Exhibit 1.) This is consistent with our previous findings. The slow speed of commercial innovation, along with the slow adoption of new capabilities, continues to hold many companies back. But others are exploiting a first-mover advantage and treating commercial operations as a source of competitive advantage.

First Movers Outperform Peers

Despite limited progress for the industry in general, there is also good news: our proprietary database of companies that have undergone commercial transformations shows that those that develop a next-generation commercial model generate better financial returns. While shareholder returns of the medtech sector as a whole have come under pressure in the recent period of analysis, this group of companies delivered an annual total shareholder return (TSR) that is eight percentage points higher than that of the MSCI Health Care Equipment and Supplies Index. (See Exhibit 2.)

Over the 15 years we’ve been conducting our analysis, that broad point has held true. Those companies that are innovating their commercial models and improving their capabilities are winning in the market, unlocking revenue growth while also becoming more efficient. Those that have not yet acted face an increasingly urgent need for change.

Five AI-Powered Commercial Moves of the Future

The commercial moves we previously recommended are all still relevant. But AI can now enhance them. (See Exhibit 3.) The technology turns information into actionable insights, helping commercial teams move faster, operate more efficiently, create value in new ways, and support top-line growth.

Each of the five AI-powered moves warrants a closer look.

Continuous Prioritization and Dynamic Targeting. Commercially savvy medtech companies use deep insights to segment customers based on their needs and potential. Yet processes are still often manual, empirical, and dependent on moderate-quality inputs from ERP and CRM systems. Customers are often tracked at an account level rather than an individual level.

With AI and omnichannel distribution, the availability of customer intelligence data vastly expands, streaming in from every interaction an individual buyer has with the supplier, including conversations with sales reps, customer service and technical service touchpoints, website activity, and responses to e-mail campaigns. And processing that information into actionable insights such as next-best actions has never been easier.

At its core, AI can create a real-time, 360-degree view of each buyer, fusing the company’s own data with third-party information into a single source of truth. AI-enabled engines continuously analyze incoming data points to generate insights for commercial teams and channels: Which other products does this person typically buy? Is the buyer showing any signs of churning?

Using this information, buyers can be re-segmented, re-prioritized, and targeted in a hyper-personal way in real time. Insights can be converted into action across channels: fueling digital channels with personalized recommendations, enabling reps, or equipping agentic customer engagement channels. As a result, companies can often double the number of relevant leads for sales teams and increase conversion rates by 30% to 50%, on average. (See “Building the Technology Backbone.”)

Building the Technology Backbone

The five AI-powered commercial moves we describe may appear to be independent capabilities. But leading medtech companies are building them on top of the same underlying technical backbone.

Rather than developing dozens of disconnected AI applications, they are creating an integrated commercial model that continuously learns from every customer interaction and makes every function smarter over time. This platform consists of three tightly connected layers. (See the exhibit.)

Data Layer. Most medtech companies already possess decades of commercial interactions captured across ERP systems, CRM platforms, pricing tools, digital channels, customer service applications, databases, and connected products. But historically, this data has remained fragmented across functions and systems, making it difficult to leverage at scale.

Rather than waiting for perfect data integration across the enterprise—or replacing existing systems—leading companies take a more pragmatic approach. They establish a data layer that continuously consolidates information from these disparate sources into a common commercial view.

Intelligence Layer. Once this common data foundation exists, companies can move beyond isolated AI solutions toward a more integrated approach. Instead of building separate engines for marketing personalization, lead generation, pricing, forecasting, customer service, or churn prediction, these capabilities are bundled into one AI platform: a single intelligence layer that serves as the commercial “brain” of the organization.

These capabilities continuously reinforce one another. Marketing responses refine lead-scoring models. Pricing decisions strengthen negotiation recommendations. Sales outcomes improve future next-best-action recommendations. Customer-service interactions improve churn prediction. Every new application increases the value of every application already running on the platform.

As software development itself becomes increasingly AI-enabled, the technology will become progressively easier to build. Competitive differentiation will come less from the algorithms themselves than from superior commercial data, domain-specific business logic, and embedding AI deeply into commercial processes.

AI-Orchestrated Customer Channels. With the intelligence layer, companies can apply AI to processes and workflows where customers and commercial teams interact. Rather than existing as a separate application, the technology becomes an invisible layer augmenting every commercial engagement:

• Marketing organizations orchestrate highly personalized campaigns that adapt continuously based on customer behavior and engagement.
• Inside sales teams work from dynamically prioritized opportunity queues rather than static call lists.
• Field sales representatives receive AI-generated account summaries before customer visits, recommended talking points during meetings, suggested pricing strategies during negotiations, and automatically generated follow-up actions afterward.
• Customer service teams resolve routine inquiries through AI while equipping specialists with relevant technical knowledge for more complex issues.
• Customers interact through conversational AI agents capable of recommending products, answering technical questions, resolving service requests, updating order status, preparing quotations, scheduling demonstrations, and supporting them 24/7 across their entire purchasing journey.

Together, these three layers create a self-reinforcing learning system that becomes more valuable with every interaction. Better data creates better intelligence. Better intelligence creates better customer interactions. Better interactions generate richer data. Over time, this feedback loop becomes one of the strongest sources of competitive advantage.

Always-On Personalized Engagement. AI enables medtech marketing to shift from one-way, plan-driven campaigns based on predefined messages and static content to highly personalized engagement tailored not just to corporate accounts but to the individual buyer.

Drawing on the 360-degree customer view, AI-powered solutions follow the customer across every channel and select the combination of message, visual, and engagement mode most likely to drive interest and conversion. Companies deliver the right message to the right customer at the right moment, and the entire process happens much faster than before.

This is a fundamental change in the way marketing content gets produced. Messages and visuals are built in modules so that individual elements can be assembled, personalized, and—within defined boundaries—newly generated by AI for a single engagement that no other customer may ever see. Segmentation collapses from groups of customers to a true segment of one. For example:

• A surgeon researching a device online could see a campaign built around product imagery and technical specifications.
• A procurement-minded hospital administrator weighing economics for the same device sees material on interoperability and outcomes metrics such as reduced hospital stays.

Critically, this process demands rigorous governance of the AI engine. Every generated claim must stay within the clinical, scientific, and regulatory limits the company has set—the medtech equivalent of automated yet controlled medical, legal, and regulatory reviews. With the right governance, engagement becomes more personal, more compliant, and far more scalable than in the past, while empowering decision makers closer to the customer.

AI-Augmented and Autonomous Sales. In addition to its role in customer prioritization and marketing, AI strengthens the capabilities of every sales rep and, in selected situations, engages customers directly. With AI-augmented selling, teams receive a flow of prequalified leads from marketing, along with next-best-action nudges and an explicit game plan: who to engage, when, and how, grounded in deep customer insights and with actions that have been successful in comparable situations.

During sales calls, teams get real-time support through live nudges. New AI tools allow sellers to tailor customer materials through prompts in an ongoing meeting, thereby fine-tuning the conversation as it is taking place.

The next generation—autonomous selling—entails a GenAI-powered system that can execute commercial interactions on its own, potentially even working directly with a customer’s procurement agent. (See Exhibit 4.) The system perceives a customer situation, develops an appropriate response, drafts and sends communications, and updates the underlying systems while operating within explicitly defined boundaries of what it can say and do.

All these capabilities, along with AI-supported coaching tools and other solutions aimed at dramatically improving productivity, are embedded into simple workflows so reps can act swiftly. Throughout, reps remain firmly in the driver’s seat, free to adapt actions, re-prioritize leads, and own their customer relationships.

Through this approach, companies can simplify processes and deliver a materially better experience for sales teams. Managers get AI-based coaching tools, reps have AI support and real-time feedback, and administrative burdens and processes are reduced across the board. Customers benefit as well when agents resolve routine interactions without delay and free human talent to concentrate on what matters most for buyers, patients, and clinical outcomes. Augmented and autonomous selling initiatives can reduce turnover of critical sales talent and increase conversion rates by 30% to 50%, thus driving growth with the existing commercial organization. (See “Three Critical Factors for Agentic Marketing, Sales, and Customer Service.”)

Data-Driven Pricing. Pricing is the lever with the most direct impact on the bottom line, yet in medtech it remains strikingly underdeveloped. Pricing processes are bureaucratic and cumbersome. One BCG analysis found that B2B pricing teams across all industries typically spend three out of every five days working with pricing admin and management. In medtech, prices are typically set according to a list and negotiated down through discretionary discounting. Reps tend to move in coarse, five-percentage-point increments rather than fighting for every point of margin, and sales managers are frequently incentivized on top-line metrics alone, leaving profitability on the table. As a result, teams prioritize volume over value.

AI-based pricing support can help address these biases and make the process easier for everyone, especially given the complex data systems that many medtech companies have. Rather than investing in a new IT solution or pricing algorithm, companies can deploy a set of AI agents to access the most relevant data for a given customer, product, or order. From that point, a layer of AI pricing agents generates recommendations, and an orchestration layer makes these insights usable to front-line sales teams.

The same model prepares the rep for customer conversations, summarizing precisely why a premium is justified with data about clinical and economic benefits versus the competition and the value delivered beyond the product itself, such as service levels, uptime, and outcomes. And because the pricing and margin engine reveals the true profitability of each account, it becomes a critical input into how much commercial effort and cost a company should invest in serving that account—closing the loop between price realization and resource allocation.

Our work shows the magnitude of what’s at stake. At many organizations, mispriced like-for-like deals, over-discounting of small nonstrategic accounts, and unrecovered cost increases all erode profitability. But data-driven pricing transformations have delivered 7% to 12% gross margin improvements within the first year. Companies can invest those resources into innovation to improve their products, increase access to care, and ultimately deliver better health outcomes.

Seamless Service Experience with 24/7 Agents. The fifth move uses AI to support customers after the transaction through 24/7, agentic service. AI-enabled agents work hand in hand with human teams, augmenting their activities—and in well-bounded situations, engaging customers directly—to deliver a markedly better service experience and support overall customer success.

The decisive shift is that customers are no longer constrained by office hours or channel. They can interact when and how it suits them—voice, chat, app, web, e-mail. And because every interaction is fused into the same 360-degree database in real time, the engagement is always tailored to their preferences and always reflects the latest information, regardless of where the last conversation left off. Every interaction feeds the same intelligence layer that powers lead prioritization, pricing, and engagement.

Consider two scenarios:

• A customer calls after hours with a question on product specifications. The AI agent answers in real time and, monitoring the exchange, recognizes a buying signal. This triggers a high-urgency lead that the engine routes to the right human rep, who picks it up first thing in the morning to discuss the product and an offer.
• A device requires service. The AI service agent captures the request, resolves what it can, and hands it off seamlessly to technical service, with full context preserved.

BCG’s customer service work shows the magnitude of this move: turnaround times cut by 20% to 50%, queue times reduced by 30% to 50%, higher customer satisfaction scores, and double-digit reductions in the cost-to-serve. In a medtech context, where uptime and responsiveness can directly affect clinical workflows and patient care, an always-on, context-aware experience is not merely a commercial advantage; it is a tangible contribution to the outcomes that matter most.

The Playbook for Medtech Leaders

Our experience supporting companies with large-scale commercial transformations points to several success factors that companies should apply as they implement AI.

Make AI a CEO-agenda topic. AI has massive potential to change organizations and improve performance. Leading companies don’t treat it as a technology initiative; instead, they elevate it to a CEO-led business transformation, with the right level of resources, managerial attention, and ambition.

Be bold and start with value. Companies want to start with pilots, use cases, and incremental measures—baby steps that limit AI’s impact. That strategy almost never creates material impact. The companies that are achieving value are the ones that embrace the need for bold reinvention and focus on value. Real gains come when companies fundamentally reshape workflows, adapt management layers, and change incentives to reinforce new behaviors. Companies need to be willing to make a few—and strictly only a few—bold bets and grow over time toward an AI-first way of working.

Don’t wait for perfect technology or data. Leading organizations address data issues in parallel with AI implementation. They have a clear vision for future customer engagement and expect to adjust their commercial model over time as new technologies emerge, and they continually upgrade and enhance customer data. In this way, companies prevent data limitations from holding back AI adoption while progressively turning their data into a source of competitive advantage.

Inspire your teams. Transforming commercial models through AI requires more than just technology—the real benefit comes from how people use it. In BCG’s experience, most of the value of AI comes from changes to underlying employee behaviors. Only about 10% comes from algorithms. Twenty percent comes from data and technology, and 70% from applying an AI-first approach to reshape processes, redesign ways of working, and build the right governance.

Top-performing organizations apply this principle and allocate their efforts accordingly by focusing primarily on the people side of change. Proactive leaders can jump-start the process by setting and communicating a bold vision for how AI will transform commercial operations. At lower levels of the organization, marketing, sales, and customer service teams can help co-create solutions to ensure that AI augments rather than threatens their roles. Throughout, companies can inspire employees by showing how AI removes much of the administrative work they do and gives them better capabilities—and more time—to do the work they find more rewarding.

Build an AI-fluent commercial organization. Upskilling and training are essential to help teams become fluent in new tools, data, and modes of interacting with customers. Developing a clear vision of required commercial capabilities, aligned with the agentic commercial toolkit, gives leaders a sense of their organization’s starting point. From there, they can leverage AI-enabled, personalized training content, interactive formats and continuous recertification of required skill levels to build the required capabilities of the future.

Our 15 years of research into medtech commercial performance has revealed a consistent theme: the companies that transform their commercial models generate a better customer experience and stronger financial performance. AI is the next step in that evolution. Early data shows that companies leading in AI-driven commercial transformation are significantly outperforming their peers. They are redesigning their go-to-market models, building new capabilities, and setting new standards for employee satisfaction and customer engagement.

Yet these companies are still a small share of the overall industry, underscoring the fact that applied AI is difficult to get right. For the rest of the industry, the path is clear: put commercial transformation on the CEO agenda, define a bold ambition tied to clear financial impact, invest in the data and technology infrastructure to support it, and rewire the organization to embed new processes and behaviors. Through this approach, companies can build the AI-enabled commercial model of the future and realize a substantial first-mover advantage.

The authors thank the following colleagues for their support with this publication:

Nicolò Barchi, Jonathan Bateman, Alex Baxter, Elisa Bernabei, Jacopo Bonafè, Dimple C, Eros Carnieletto, Sam Cole, Lluch Garcia, Axel Griewel, Lisa Hartmann, Anna Heikkurinen, Bastian Hoppmann, Emily Hwang, Sanchita Jaiswal, Jan Jerratsch, Vandana Kalra, Mitch Krogman, David Langkamp, Doris Michl, Kerri Miller, Patrick Müller, Jen Progin, Zina Seibert, Roxana Tarnita, Maxime Überschlag, Marion von Krosigk, Martin Vriesen, Wolfgang Walther, Marcus Wittig, and Nikolaus Zeiss.