Artificial intelligence is already part of the daily life of many organizations, though its real impact in the commercial arena still depends on its effective integration into the sales processes. According to McKinsey’s The State of AI 2025 report, 88% of organizations already use AI regularly in at least one business function, up from 78% the year prior, though only about a third have begun scaling their AI programs at the corporate level.
In the commercial domain, the gap between adoption and maturity will become increasingly evident. Gartner predicts that by 2027, 95% of sellers’ research flows will begin with AI, compared with less than 20% in 2024. For ARBENTIA, a technology-focused consultancy, this shift signals a fundamental evolution of the commercial function, marked by the ability to analyze more information in less time, identify patterns that previously appeared scattered across multiple sources, and turn that knowledge into more precise actions on customers, opportunities, and forecasts.
“AI should not be implemented superficially; its use goes far beyond generating emails or automating isolated tasks within the CRM,” says Eduardo Aramburu, AI Practice Leader at ARBENTIA. “These uses are great, yes, but their differentiating contribution starts when it connects the information that already exists in the company and turns it into more precise decisions: which opportunity to prioritize, which client needs follow-up, which operation is starting to cool, or which forecast requires a review.”
From Commercial Productivity to the Intelligent Sales System
ARBENTIA sums up this shift in six end-to-end levers, designed for companies that have already started using AI in sales, but need to translate that usage into tangible results:
- Connect selling with the rest of the business: AI delivers more value when it doesn’t work in isolation on commercial data, but draws on data from across the organization. An opportunity isn’t valued the same if you know the client’s purchase history, profitability, open incidents, delivery timelines, outstanding invoices, or true service capacity. The first step is to break the isolated CRM view and connect selling with the operational and financial information that conditions every commercial decision.
- Review the lead-to-order process before automating it: Automating a sales process without reviewing it can speed up what was already functioning poorly. Before applying AI, it’s wise to analyze how a lead enters, when it becomes an opportunity, what criteria qualify it, how a proposal is prepared, what validations it needs, and when it transitions to an order. This holistic view allows applying AI where it has real impact—reducing response times, avoiding misqualified opportunities, or anticipating bottlenecks before they affect the close.
- Use AI to qualify better, not just to sell more: One of the biggest efficiency drains in sales is spending time on opportunities that don’t fit. AI can help identify whether an account has real potential by cross-referencing sales signals with business variables such as recurrence, expected margin, purchase history, sector, size, prior behavior, or service cost. The goal isn’t to fill more of the pipeline, but to improve its quality so the team works fewer irrelevant opportunities and more deals with real potential.
- Detect commercial risks before they appear in the forecast: Many variances are seen too late because the forecast is updated after the problem already exists. AI can anticipate signals such as stalled opportunities, proposals without responses, changes of contact, customers with lower activity, margins at risk, or accounts with pending issues. This read enables action sooner, adjusts forecasts, and prevents sales leadership from relying solely on manual estimates or last-minute reviews.
- Prepare offers and conversations with business context: A sales conversation should not rely solely on contact history. To shape a useful proposal, the salesperson needs to know what the client has bought, what margin it leaves, what services they use, what problems they’ve had, what needs they can anticipate, and what terms are viable for the company. AI can summarize that context and help build a more precise recommendation, avoiding generic proposals that overlook operational realities or the account’s profitability.
- Measure AI by its impact on conversion, margin, and forecasting: The success of AI in sales should not be judged by how many users try it or how many emails it generates. The relevant indicators are tied to business outcomes, such as improved conversion, shorter sales cycles, higher average margin, higher-quality pipeline, less administrative time, more accurate forecasting, and better coordination among sales, operations, and finance.
“AI does not change the essence of the sales method, but it can greatly elevate its quality,” concludes Aramburu. “When it works on a connected sales chain, it evolves from a point-in-time aid for the salesperson to a more rigorous way of managing growth.”
