Sales, Marketing, and AI Readiness: Voice of the Customer

There’s a question that’s been floating around offices for years, and it’s gotten sharper recently: why is my technology life at home so much better than my technology life at work? It used to be about interfaces – why do my work apps look like they were designed in 2003 when my phone is beautiful? Now it’s about AI. People go home and have fluid conversations with ChatGPT, ask their phone to summarize a twenty-page document, use AI to plan a vacation in thirty seconds. Then they come to work and get handed an “AI-powered” internal tool with a training deck and a change management workshop.

The gap is real, and it’s getting wider. And the frustration isn’t just aesthetic – it’s a credibility problem. Every employee who uses AI fluently at home and then hits a clunky internal implementation starts to wonder whether the organization actually understands what’s possible. They don’t say this in the feedback survey, of course. They just quietly route around the tool and keep doing things the old way. And who in your organization understands that gap better than anyone? Sales and Marketing. These are the people who spend their entire professional lives studying what customers expect, how they experience products and services, and what it takes to get someone to change their behavior. They live in the space between what people want and what organizations deliver. That’s not a peripheral skill for AI readiness. That’s the core challenge.

But when organizations talk about Sales and Marketing and AI, the conversation almost always goes in one direction: how can AI make S&M better? AI-powered lead scoring, personalized campaigns, predictive analytics for pipeline management. Fine – all useful stuff. But it completely misses the bigger question, which is what Sales and Marketing contribute to the organization’s AI readiness as a whole. The data they sit on, the change management skills they’ve built, the ability to make new ideas feel personal and valuable to skeptical audiences – these are capabilities the entire AI transformation needs, not just the marketing department.

The Data Nobody’s Counting

Before we talk about skills, let’s talk about data – because Sales and Marketing might be sitting on the most valuable AI asset in your company, and nobody’s thinking about it that way.

Your sales team generates an enormous volume of unstructured data every single day. Call notes. Email threads. Proposal feedback. Objection logs. Competitive intelligence gathered one conversation at a time. Your marketing team adds another layer – campaign response patterns, customer engagement data, content performance metrics, social listening, event feedback. This information lives in CRM systems, email archives, shared drives, and – let’s be honest – people’s heads. It’s messy. It’s distributed. It’s inconsistent. And for most of the history of enterprise technology, it’s been nearly impossible to do anything systematic with it.

That’s changed. Modern AI – particularly large language models and the tools built on top of them – is designed to work with exactly this kind of unstructured, language-rich, context-heavy data. Customer conversations contain buying signals, competitive positioning, product feedback, and market trends that no structured database captures. The objections your sales team hears every week are a real-time map of what the market thinks about your products, your pricing, and your company. The questions prospects ask during demos tell you more about unmet needs than most formal market research.

Think about what becomes possible when you actually point AI at this data. Your sales team has hundreds of call notes from the last quarter – and buried in those notes are patterns no individual rep can see. Which objections cluster by industry? Which competitor keeps showing up in deals you lose, and what specifically are buyers saying about them? Which product questions signal a prospect who’s about to expand their scope – and which ones signal a prospect who’s about to stall? A good rep has instincts about these patterns from their own territory. AI can surface them across the entire sales organization, in real time, and connect them to outcomes.

Marketing’s data tells a similar story from a different angle. Campaign response patterns reveal what messages resonate with which segments – not in the aggregate way that marketing dashboards already show, but in the granular, language-level way that tells you why something worked. Customer support tickets, product reviews, social mentions – all of it is signal about what your market actually thinks, expressed in their own words rather than filtered through survey questions that somebody in market research designed. The gap between what customers say in a structured survey and what they say in a frustrated email to support is enormous, and it’s in that gap where the most valuable insights live.

The problem isn’t that this data doesn’t exist. The problem is that when organizations plan their AI strategy, they start with structured data – ERP transactions, financial records, sensor readings – because that’s what the technical team knows how to work with. The rich, messy, deeply human data that S&M generates doesn’t make it into the conversation, because nobody thinks to invite S&M to the data conversation. Which circles back to the theme of this series: when you treat AI readiness as a technology initiative, you miss the contributions that every functional area brings. S&M’s data isn’t a nice-to-have. For any AI initiative that touches customers, products, or market positioning, it’s the foundation.

Your Internal Customers

Successful sales and marketing teams are already tuned in and listening to the customer – understanding requirements, designing solutions, iterating based on feedback. This isn’t just a set of activities. It’s a way of thinking, a discipline of empathy that good S&M organizations build into everything they do. And here’s the question that doesn’t get asked nearly enough: why can’t we apply that same thinking internally?

Think about what happens when your organization rolls out a new AI tool. Someone in IT or the AI team builds it, tests it, and deploys it. There’s a training session. Maybe a video. An email from leadership explaining why this matters. And then everyone wonders why adoption is slow, why people keep running the old process alongside the new one, why the operations manager still trusts the spreadsheet more than the AI recommendation.

Now think about how your sales team introduces a new product to a skeptical buyer. They don’t lead with the technical specifications. They start by understanding the buyer’s current situation – what’s working, what’s painful, what they’re afraid of losing. They frame the new product in terms of the buyer’s specific problems, not the product’s generic features. They address objections before they become deal-killers. They make the change feel manageable, even desirable, by connecting it to something the buyer already cares about.

That’s exactly what AI adoption needs internally, and your S&M team already knows how to do it. The operations manager who doesn’t trust the AI recommendation? That’s a customer with an unmet need – they need to understand how the recommendation was generated and what happens when it’s wrong. The finance analyst running the old spreadsheet in parallel? That’s a customer who hasn’t been convinced of the value proposition yet. When we treat our internal peers as customers – listening to their concerns, designing the AI experience around their actual workflow, meeting them where they are instead of where we wish they were – adoption stops being a compliance problem and becomes a value proposition. It doesn’t matter that we’re not charging the folks in Operations for AI services. You don’t have to be a paying customer to be treated like a person.¹This is one of those ideas that sounds obvious when you say it out loud but gets forgotten almost immediately in practice. The internal rollout plan almost never includes the kind of customer empathy that the external sales process demands.

Selling the Change

At the start of every day, Sales and Marketing helps external customers understand products and the value they bring. This is a process of education and effective communication – introducing new capabilities, getting customers to change their minds and processes, convincing them to invest their time and money differently. It’s difficult work. And it’s powerful stuff when it’s done well.

AI transformation requires exactly the same thing, except the customer is internal and the stakes feel more personal. When you ask an external customer to adopt a new product, the worst case is they say no and buy from someone else. When you ask an internal colleague to adopt AI in their workflow, they hear something different. They hear: this thing might replace part of my job. This thing might make my expertise less valuable. This thing might expose how much of my work was routine all along. The resistance to AI isn’t irrational. It’s deeply human, and it requires a more sophisticated approach to change than a training video and an executive memo.

S&M knows how to do this. They know how to reframe resistance – taking a customer who says “we’ve always done it this way” and helping them see what they’re leaving on the table. They know how to make change personal – not “AI will improve organizational efficiency by 15%” but “you’ll stop spending three hours every Tuesday reconciling data that the system can match in seconds.” They know how to build momentum by finding early wins and making them visible. And they know that change doesn’t happen in a single conversation – it’s a process of building trust, demonstrating value, and following up.

This isn’t just a nice parallel. It’s a practical opportunity. Your Sales and Marketing team can coach the rest of the organization on how to sell AI adoption to itself. Not as a mandate from leadership, but as a value proposition that each team and each individual can evaluate on their own terms. The same people who can convince a skeptical buyer to change their purchasing process can absolutely convince a skeptical warehouse manager to trust an AI-generated picking recommendation – if anyone thinks to ask them.

Here’s what this looks like in practice. Say your warehouse team has a new AI-driven demand forecasting tool, and the planning manager isn’t buying it. She’s been doing demand planning for years, she’s good at it, and the AI’s recommendations don’t match her experience in ways she can’t explain. The typical approach is training and mandates – show her how to use the tool, tell her leadership expects adoption, measure compliance. Your S&M team would approach this completely differently. They’d start by listening. What specifically doesn’t match her experience? Which product lines does she trust the forecast on, and which ones feel wrong? They’d find the places where the AI and her judgment actually agree – because there are always some – and use those as the foundation. See, it’s confirming what you already know here. Now let’s look at where it disagrees and figure out why. They’d make her the expert in the conversation, not the student. And they’d follow up next week, and the week after that, because they know from selling externally that trust builds over multiple conversations, not in a single training session. That’s not a hypothetical approach. That’s what good selling looks like – and it works on internal customers for exactly the same reasons it works on external ones.

The Discipline of Profitable Revenue

There’s one more contribution that S&M brings, and it complements the financial discipline we explored in the previous article. At the end of every day, Sales and Marketing has to make sure that products and services generate more revenue than the cost required to deliver them. Every new dollar is not as good as the last – most companies understand that steady earnings growth and predictable cash flow matter more than a single impressive quarter.

That instinct – the discipline of profitable revenue – applies directly to AI investments. Not every AI-generated lead is a good lead. Not every personalization engine delivers enough lift to justify the platform cost. Not every chatbot interaction creates more value than the customer service representative it replaced. S&M lives in this world of marginal value calculation every day. They understand what it takes to set expectations such that a sustainable business can be built, not just an impressive demo.

Where Finance brings the cost discipline (what are we spending, and is it working?), S&M brings the revenue discipline (what’s the maximum value this will actually create, and what’s the offsetting cost in time, effort, and organizational disruption?). Together, they provide the complete business case framework that AI initiatives need. Without both perspectives, you get either AI projects that look good on a spreadsheet but don’t connect to real customer value, or AI projects that excite customers but hemorrhage money. S&M’s instinct for where value actually lives – in the customer relationship, in the buying experience, in the competitive positioning – keeps AI investments grounded in what the market will actually pay for.

Transformation is change. And Sales and Marketing is arguably the team in your organization with the most experience introducing change successfully. They do it for external customers every single day – educating, persuading, overcoming objections, making the unfamiliar feel valuable. The question is whether your organization is smart enough to point those skills inward.

The data that S&M generates – all those messy, rich, deeply human customer conversations – is exactly what AI needs to understand your market. The change management skills that S&M has built are exactly what AI adoption requires to take hold. And the revenue discipline that S&M brings every day is exactly what keeps AI investments connected to real value instead of impressive demos.

In the first article of this series, we argued that AI readiness is an organizational capability. Finance brings the facts. Sales and Marketing brings the voice – of the customer, of change, of value. Next up: Product Development, and the shift from selling what you make to making what your data tells you to build.

If you’re rethinking how your teams contribute to AI readiness – or looking for practical ideas on leading change from the inside out – join our mailing list and we’ll keep the conversation going.

Related Articles

• Reconfiguring Work: Change Management in the Age of Gen AI – McKinsey’s deep dive on why AI adoption requires redesigning how work happens, not just distributing new tools
• Transforming the Enterprise Through AI-Powered Workflows – McKinsey research on scaling AI in marketing, with emphasis on cross-functional collaboration and change management as the critical success factors
• Change Management Is the Key to AI Success – Fast Company piece on a 60,000-person Copilot rollout that succeeded by treating adoption as selling, not training