Beyond the Chatbot: How Winning Sales Organizations Convert AI Into EBIT
- Jul 8
- 9 min read
A few days ago, we were in the office of a sales director at a food manufacturing company, reviewing the quarterly results with him. At one point in the conversation, he mentioned, almost in passing, that his team "was already making extensive use of artificial intelligence."
I asked him what for, to which he replied: Drafting emails, summarizing meeting minutes, preparing presentations, etc.
None of that is wrong. But while that company was using AI as an efficient secretary, other companies in the same sector were using it for something different: deciding which cities to expand coverage in, anticipating which distributor is about to let them down, or building the financial argument they will use to negotiate the next price increase with a chain.
That is, today, the true dividing line in the mass consumer goods sector. It's no longer about who has access to technology—virtually everyone does—but about who has integrated it into the way they think and make business decisions.
According to McKinsey & Company's State of AI 2025 report, marketing and sales, along with IT, are now consistently reporting the highest use of artificial intelligence within organizations. Regular use of AI in at least one business function rose from 55% to 72% and then to 78% in just two years, and the most recent edition of the study puts that figure at 88%.
Access to the tool is no longer the problem. The problem is something else, and McKinsey documents it with unsettling precision: only 39% of organizations manage to attribute any impact on their EBIT to the use of AI, and among those, most report that it represents less than 5% of that EBIT. In other words:
...almost nine out of ten companies already use artificial intelligence, but less than one in ten is seeing a financial result worth mentioning in a board meeting.
That is the space where the competitive advantage of the coming years is being decided.
What's really happening in the market
Let's be clear about one thing: artificial intelligence is not a passing fad that consumer goods companies can afford to wait for to "mature." The purchasing behavior of the end consumer has already changed, and this change is pushing back the entire supply chain, all the way to the manufacturer.
A recent study by NielsenIQ in collaboration with Kearney, The New Growth Frontier, documents that:
74% of shoppers already use artificial intelligence tools to discover products,
54% to research prices and features, and
20% off for direct purchases.
The same analysis found that, in the United States, niche brands gained 1.5 percentage points of market share between 2022 and 2025, while large and medium-sized brands fell back 2.1 points.
This fact should worry any established manufacturer: the size and historical strength of a brand no longer guarantee category leadership on their own. The agility to adapt to these new discovery channels, many of them AI-driven, is becoming as important as physical distribution.
On the internal business side, McKinsey estimates that around 20% of sales activities could already be automated with currently available AI tools, and calculates that the potential economic value of generative AI is concentrated precisely in functions such as customer operations, marketing and sales, software engineering and research and development, with an estimated annual impact of between $2.6 and $4.4 trillion globally.
These are not figures that one can ignore in the strategic planning of a mass consumer goods company, regardless of whether it bills one hundred million pesos or one hundred million dollars a year.
Why are most companies missing out on this opportunity?
If the technology is available to everyone and the potential value is so high, why are most mass-market consumer companies barely scratching the surface?
In our experience advising manufacturers, distributors and chains in Mexico, Central America, Venezuela, Brazil, Spain and the United States, I find basically three causes that are repeated time and time again.
The first reason is structural: most organizations have not yet updated their business processes by integrating AI. Winners don't just slap AI onto existing processes; they rebuild those processes—sales scripts, negotiation protocols, the sales planning cycle—so that AI can operate reliably within them. In the consumer goods sector, this translates, for example, into continuing to prepare for a negotiation with a retail chain in the same way as ten years ago, only now with a chatbot open in a separate tab, instead of redesigning the entire negotiation preparation process around what AI can offer.
The second issue is that AI is being asked to be a search engine, not an advisor. A sales manager types "How do I increase my sales?" and expects a transformative answer. Instead, they get a list of correct but useless generalities: know the customer, improve service, train the team. Not because the tool is limited, but because the question itself is. It's exactly the same as what would happen to a senior consultant if we sat them down at a table without giving them a single business statistic.
The third factor is talent and methodology, not technology. The difference between companies that are capturing value and those that aren't has less to do with the AI model they use and more to do with the organizational discipline behind it: committed leadership, objectives that go beyond simply cutting costs, and management processes that demand measurable results. Only 6% of organizations today qualify as "high performers" in AI, with an impact of 5% or more on their EBIT. The rest—the vast majority—are experimenting, not transforming.
The risks of falling behind
For a mass consumer manufacturer or distributor, remaining in the "experimenting with AI" phase for the next two or three years is not a neutral position.
It is a concrete commercial risk, with three fronts.
The first is losing market share. If the end consumer is already using AI to discover and decide what to buy, and niche brands—typically more agile and with fewer decision-making layers—are already gaining ground against established brands, every quarter a company takes to adapt its competitive approach in that environment is ground it will likely not easily recover.
The second is the loss of clients and opportunities. A Key Accounts team that still takes four or five days to prepare a negotiation with a chain, while the competitor resolves it in a fraction of that time with a better quality financial argument, is not competing on a level playing field, even if both "use AI".
The third is talent loss. The most capable sales professionals—those you want to retain—quickly notice if the company requires them to continue manually performing tasks that should no longer be done manually. Sales productivity and talent retention are more interconnected than is typically acknowledged in management committees.
Where is the real opportunity implementing AI in sales organizations?
The good news is that the areas where artificial intelligence generates the most value in mass consumption are precisely those that a sales director manages every day.
In business planning, AI allows for the analysis of sales trends with a level of detail that previously required days of work from an analyst: detecting products that are losing market share before the data appears in the income statement, building different growth and budget scenarios, and anticipating trend breaks by channel or region.
In Route to Market, you can evaluate actual versus potential territorial coverage, identify underserved or overserved areas, and simulate alternative service models for distributors, wholesalers, and chains before committing a single investment. This is, incidentally, one of the exercises where we most frequently find untapped growth opportunities in the RTM diagnostics we conduct at TMC.
In trade marketing, it allows you to compare promotional investment between clients and evaluate, with data and not intuition, which promotions generated real incremental growth and which simply brought forward purchases that were going to happen anyway, destroying margin without building value.
In key account management, it reduces the work of preparing a negotiation with a large chain from days to hours: organizing scattered information, anticipating likely buyer objections, and building several negotiation scenarios with their respective financial arguments before sitting down at the table.
And in sales force productivity, it helps to design finer indicators, better calibrated incentive plans, and customized training programs, freeing up administrative time so that teams can focus on the one thing an algorithm still can't do well: building trusting customer relationships.
The detail that separates a generic answer from a useful analysis
Here, in our experience, is the point that makes the difference between a company that takes advantage of AI and one that barely touches it: the quality of the context that is delivered to it.
Compare these two ways of using the same tool.
The first one: "How do I increase my sales?" Any AI model will respond with correct but generic recommendations—know the customer, improve service, invest in marketing—because that is exactly the level of specificity of the question.
The second: "Act as a consultant specializing in consumer goods manufacturing companies. My company sells food in Mexico through distributors and supermarket chains. In the last twelve months, we grew 4% in sales, but our gross margin fell 6%. The traditional channel represents 58% of sales and the modern channel 42%. Analyze the possible causes of this situation, develop five hypotheses ordered by economic impact, indicate what additional information you need to validate each one, and propose an action plan for the next six months."
The difference isn't in the tool itself. It's in treating it as what it can be: another member of the executive committee, to whom you must present real figures, concrete constraints, and a clear objective, instead of just asking a question out of the blue.
This same principle applies in every business area.
To prepare for a negotiation with a chain, it is not enough to ask "help me negotiate a price increase"; it is advisable to specify the percentage of the increase, the chain's share of total sales, the market share of the category, and explicitly request a strategy with anticipated objections, value creation arguments, acceptable concessions, and risks to avoid.
To evaluate distributors, it is advisable to request a methodology with weighted variables—coverage, execution at the point of sale, inventories, service level, growth, profitability, compliance with KPIs—and a rating scale, rather than a general opinion on "how well" the business relationship is going.
And perhaps the most underrated use of all: before presenting an annual business plan to the CEO or board, ask the tool to first ask all the necessary questions to understand the business, rather than directly requesting a recommendation. This sequence—letting the AI ask questions before responding—typically produces a considerably more robust analysis than demanding an immediate answer with incomplete information.
What leading companies are doing differently with artificial intelligence in sales
Organizations identified as "high performers" in AI are not distinguished by the sophistication of their technology, but by three quite specific management practices.
They pursue objectives that go beyond reducing costs — they also seek growth and innovation — which makes it easier for them to obtain budget and commitment from other areas.
They redesign entire processes instead of automating individual tasks.
And they have managerial leadership that not only approves of the use of AI, but models it by example : high performers are three times more likely to report that their senior leaders demonstrate real ownership and commitment to the organization's AI initiatives.
In mass consumption, this translates into something very practical: the sales director who personally uses the tool to prepare his own negotiations and analyses —not just requires it of his team— tends to lead organizations where adoption becomes genuine, rather than a compliance exercise.
What no company should do
Along with the opportunity comes the responsibility to use it wisely:
Do not upload confidential customer information , prices, or contracts without clearly understanding the privacy policies of the platform being used.
Do not automatically accept every recommendation without verifying it against real business data: AI can make mistakes, especially when the context it received was incomplete.
Do not use it as a substitute for managerial judgment or the accumulated experience of the sales team, but as an accelerator of that judgment.
And not settling for generic questions when the problem—a margin drop, a complex negotiation, a hedging decision—demands real and specific context.
Artificial intelligence expands the number of options a sales manager can analyze before making a decision and accelerates the time it takes to arrive at those options. The responsibility for making the decision, however, remains entirely human.
Four indicators that should be measured
Before declaring a commercial AI initiative successful, it is worth measuring, at a minimum:
The time the team spends on administrative tasks versus sales and negotiation tasks;
The accuracy of demand forecasting before and after incorporating AI into the process;
The actual incremental return on trade marketing promotions or investments compared to what was previously estimated;
The time required to launch promotions or prepare for negotiations with a key account.
The right question for a board of directors isn't "How many people on my sales team use artificial intelligence?" It's "What business decision do we make better, faster, or with less risk because of it, and what's that worth in pesos or dollars?"
The advantage is no longer in having the tool
Just a couple of years ago, having access to generative artificial intelligence was an advantage in itself. Today, with adoption nearing 88% among organizations, that argument no longer holds water.
The competitive advantage has shifted elsewhere: to those who manage to incorporate AI into the way their business organization thinks, decides, and executes, supported by a well-designed Route to Market model, a solid category strategy, and an execution discipline that technology alone can never replace.
Artificial intelligence isn't going to replace sales directors. But it's becoming increasingly clear that sales directors who learn to use it judiciously and in real-world context will gain an advantage, year after year, over those who choose to continue treating it as a mere office curiosity.
Does your company face similar challenges?
TMC Consultores helps manufacturers, distributors, and retail chains improve their business strategy, strengthen their sales channels, and increase profitability through practical solutions based on real market experience.
If you would like to know how we can help your company, please contact us.
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