Artificial intelligence is already part of the daily life of many organizations, although its real impact in the commercial area continues to depend on its effective integration into sales and 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 previous year, although only about a third have begun to scale their AI programs at the corporate level.

In the commercial and sales area, this difference between adoption and maturity will be increasingly visible. Gartner predicts that, in 2027, 95% of salespeople’s research flows will begin with AI, compared to less than 20% in 2024. For the technology consultancy Arbentia, this change points to a fundamental evolution in the commercial and sales function, marked by the ability to analyze more information in less time, identify patterns that were previously dispersed among multiple sources and convert that knowledge into more precise actions on customers, opportunities and sales forecasts.

“AI should not be implemented superficially, its use goes far beyond generating emails or automating individual tasks within the CRM,” says Eduardo Aramburu, Leader of the AI ​​practice at Arbentia. “These uses are great, yes, but their differential contribution begins when they connect the information that already exists in the company and convert it into more precise decisions. Which opportunity to prioritize, which client needs follow-up, which operation begins to cool down, or which sales forecast requires a review.”

From commercial productivity to an intelligent sales system

Arbentia summarizes this change in six keys, from end to end, designed for companies that have already begun to use AI in sales, but that need to convert this use into concrete results in their sales:

  1. Connect the sale with the rest of the business: AI contributes more when it does not work only with commercial information, but with data from the entire company. An opportunity is not valued the same if the purchase history, customer profitability, open incidents, delivery times, pending billing or actual service capacity are known. The first step is to break the isolated view of CRM and connect sales and sales with the operational and financial information that conditions each commercial decision.
  2. Review the lead-to-order process before automating it: Automating a commercial and sales process without reviewing it can make what was already working poorly faster. Before applying AI, it is advisable to analyze how a lead comes in, when it becomes an opportunity, what criteria allow it to be qualified, how an offer is prepared, what validations it needs and at what point it goes on request. This comprehensive vision allows AI to be applied where there is a real impact on sales, such as reducing response times, avoiding poorly qualified opportunities or anticipating blockages before they affect the closing of sales.
  3. Use AI to qualify better, not just to sell more: One of the biggest losses in sales efficiency is spending time on opportunities that don’t fit. AI can help identify whether an account has real potential by crossing trading signals with business variables, such as recurrence, expected margin, purchase history, sector, size, previous behavior or cost to service. The objective is not to fill the sales pipeline more, but to improve its quality so that the team works on fewer irrelevant opportunities and more operations with a sales pipeline.
  4. Detect business risks before they appear in the forecast: Many deviations in sales are seen too late because the forecast is updated when the problem already exists. AI can anticipate signals such as stopped opportunities, unanswered proposals, changes in interlocutor, clients with less activity, operations with compromised margin or accounts with pending incidents. This reading allows you to act sooner, adjust sales forecasts and prevent sales management from depending only on manual estimates or last-minute revisions.
  5. Prepare offers and conversations with business context: A commercial and sales conversation should not rely only on contact history. To prepare a useful proposal, the salesperson needs to know what the customer has purchased, what margin they leave, what services they use, what problems they have had, what needs they can anticipate, and what conditions are viable for the company. AI can synthesize this context and help build a more accurate recommendation, avoiding generic proposals that do not take into account the operational reality or the profitability of the sales account.
  6. Measure AI by its impact on conversion, margin and forecast: The success of AI in sales should not be measured by how many users try it or by how many emails it generates. The relevant indicators are those that connect with the business, such as improved sales conversion, reduction of the sales cycle, increase in the average margin, higher quality of the sales pipeline, less administrative time, more precision in the forecast and better coordination between sales, operations and finances.

One of the greatest impacts is in the ability to analyze more information in less time

“Artificial intelligence does not change the essence of the commercial method, but it can greatly increase its quality,” concludes Aramburu. “When you work on a connected commercial chain, you evolve from one-time help for the seller to a more rigorous way of managing sales growth.”

Alex Chen
Alex Chen

Alex Chen serves as the chief editor of Asia Tech Journal, bringing over a decade of experience in technology journalism. Previously writing for several globally renowned publications, Alex is celebrated for his insightful analyses and in-depth reporting on tech trends across Asia. Passionate about innovation, Alex specializes in the dynamic technology ecosystem of China and its global impacts.