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Robert Julian Smith
A salesperson on your team is preparing for a call with an important prospect. The CRM is open, but the last contact notes are three months old and incomplete. They open LinkedIn to find out who will be in the meeting, then dig through the ERP for the history of past quotes. Meanwhile, they ask ChatGPT to draft a follow-up email. The output is generic, lacks context, and the tone is off. They rewrite it manually. Twenty minutes lost to produce something mediocre.
Meanwhile, the buyer on the other side has already used AI tools to analyse three competing suppliers, compare proposals, and prepare their objections.
This is not a hypothetical scenario. It is the daily reality in most Italian B2B companies, particularly in manufacturing and industry. Salespeople have access to artificial intelligence, but no one has taught them how to use it inside their own work process. And the difference between having a tool and knowing how to integrate it into the commercial workflow is the difference between wasting time and generating results.
The GenAI Divide: why 95% of companies see no return
If that scene sounds familiar, you are not alone. The data confirms that the problem is systemic.
According to the “State of AI in Business 2025” report by MLQ.ai, based on the NANDA research project, 95% of organisations that have invested in generative AI are not achieving any measurable financial return. Analysts call it the “GenAI Divide”: the gap between those who have access to the tools and those who can actually turn them into commercial results.
The Microsoft and LinkedIn Work Trend Index 2024 reinforces the picture: 75% of knowledge workers already say they use AI at work. But 78% bring their own personal AI tools, not company-provided ones. This is the BYOAI (Bring Your Own AI) phenomenon, which reaches 80% in SMEs. Sales reps experiment on their own, without strategy, without guidelines, without training. The results are predictable: generic outputs, data risks, privacy issues, and no real impact on the pipeline.
The problem is not the technology. It is that companies treat AI like software to install, not a capability to build.
When AI is integrated with method, the numbers change
The picture changes dramatically when you look at organisations that have invested not just in tools, but in training and process integration.
The Gartner Sales Survey 2024 finds that B2B sellers who effectively partner with AI are 3.7 times more likely to meet their quota. A note of caution: this is a correlation, not direct proof of causation. Top performers tend to adopt efficiency-driving technologies first. But the directional signal is unambiguous: AI amplifies those who already have commercial discipline.
The Salesforce State of Sales Report 2026, based on 4,050 professionals across 22 countries, reinforces the picture: top performers are 1.7 times more likely to use AI agents for prospecting, 88% of those working with AI agents say they are more productive, and 91% believe AI improves sales planning.
On the productivity side, Bain & Company finds that B2B salespeople today spend only 25% of their time on active selling. The remaining 75% is absorbed by CRM updates, reporting, account research, and material preparation. AI has the concrete potential to double active selling time — but only if it is integrated into workflows, not used as an isolated tool.
And McKinsey’s research on “next-best-action” models shows that applying AI to personalise customer communications increases customer satisfaction (CSAT) by 15–20% while simultaneously boosting revenue by 5–8%. It does not lower the quality of the sales process. When properly implemented, it raises it.
The labour market is already pricing in this competency
The most concrete signal that AI training for salespeople is no longer optional comes from the labour market itself.
The PwC Global AI Jobs Barometer 2024, covering over half a billion job postings across 15 countries, finds that sales manager positions requiring AI specialist skills offer a 43% wage premium in the United States compared to equivalent roles without that requirement. Sectors with higher AI penetration show labour productivity growth 4.8 times higher than less exposed sectors.
The LinkedIn Work Change Report adds a directly relevant data point for salespeople: 70% of the skills required for standard roles will change by 2030. Demand for “AI literacy” in job postings has grown over six times in a single year. Two thirds of business leaders (66%) say they would not hire without AI skills, yet only 39% of employees have received AI training from their company.
The message is clear: the market rewards those who can work with AI and penalises those who remain on manual execution.
The Italian and European urgency
For Italian and European companies, the pressure comes from two directions: competitive and regulatory.
Data from ISTAT “Enterprises and ICT 2025” shows that 16.4% of Italian companies with at least 10 employees now use AI (up from 8.2% in 2024). Growth is strong, but the divide is significant: large enterprises are at 53.1%, while SMEs sit at just 15.7%. At a European level, Eurostat reports 20% adoption in 2025 across approximately 157,000 companies surveyed.
To understand how concrete this gap is in the world of sales specifically, I conducted a comparative analysis of 200 LinkedIn job postings for sales roles published in March 2026: 100 in Italy, 100 in Germany. I collected the data using Claude in Cowork mode, integrated with Apify as a connector for structured job posting extraction — a direct example of how AI can be used to produce market research quickly and with a replicable method.
The result is stark: 14% of German sales job postings explicitly require AI competencies, compared to 3% in Italy. A gap of almost 5 to 1. In Germany, AI requirements are concentrated in Mid-Senior (19%) and Director (33%) roles — a signal that German companies are beginning to treat AI as a managerial competency, not just a technical one. In Italy, 2 of the 3 postings with AI requirements came from multinationals headquartered abroad. The most significant finding: in neither country are specific AI tools such as ChatGPT, Claude, or prompt engineering mentioned as requirements. Companies know AI exists, but have not yet learned how to translate it into selection criteria for salespeople. That is a window of opportunity for those who act now.
The sample is exploratory (200 postings) and not statistically representative. But the directional signal is clear and consistent with the macro data.
The BIS Working Paper 1325 provides important causal evidence: AI adoption increases labour productivity by approximately 4%, with no adverse effects on employment in the short term. But the benefits are concentrated in medium and large enterprises and are tied to complementary investments in software, data quality, and training. Without these, AI amplifies divides rather than closing them.
There is also the regulatory dimension. The EU AI Act, in force since 1 August 2024, includes AI literacy obligations that entered full application on 2 February 2025. For many organisations, AI training is no longer just a competitive lever. It is a compliance requirement. Leaving the commercial team to manage BYOAI on their own is not just inefficient. It is a regulatory risk.
The MAPPA Method: from diagnosis to adoption
The critical point is this: most companies stop at the first level — tool access. They buy licences, integrate a copilot into the CRM, make a chatbot available. Then they wait for salespeople to “figure it out.” That is the level where 95% of organisations stay, and why they see no return.
What is needed is a structured path from access to real adoption. This is the method I use with B2B sales teams, and it unfolds across five phases. In Italian, the initials spell MAPPA — the word for “map” — which reflects exactly what the method does: it maps the commercial process before intervening.
Mappatura (Mapping). We start with the commercial process as it exists today: how the team works, where the friction points are, the quality of the data, what is already being done with AI and what is not, where time is being lost.
Analisi (Analysis). We identify the high-impact use cases. We do not start with the tool. We start with the activities where AI can concretely improve the work: prospecting, call preparation, follow-up, CRM management, coaching, objection analysis, commercial summaries.
Progettazione (Design). We define exactly where AI enters the workflow, with what inputs, what controls, and what limits. The priority is not “using ChatGPT” — it is building a more robust and governed way of working.
Pratica (Practice). Training is hands-on, using the client’s real work. We work on real accounts, real emails, real proposals, real processes. Not on generic slides disconnected from the actual job.
Adozione (Adoption). The goal is not to run a good session. The goal is to create lasting adoption. This means introducing operational prompt patterns, verification criteria, usage rules, and ongoing review points — so that AI becomes part of the team’s operating system, not an experiment that fades after two weeks.
This is exactly what happened with Manini Prefabbricati, where I ran an AI training programme with the company’s sales managers. Not a theoretical seminar on artificial intelligence, but an operational intervention to integrate AI into the sales process, marketing, and day-to-day commercial work. As the HR Manager summarised after the programme:
“With Robert we stopped talking about AI as a trend and started actually using it — in marketing, in sales, in daily processes. Practical, concrete, with immediate impact on the team.”
The time to act is now
AI in B2B sales is not a passing trend. It is a capability multiplier that is redefining who wins and who loses. But it only works if people know how to use it — and they only know how to use it if someone trains them with method.
AI training for the sales force is not tool training. It is the operational design of how the team sells, prepares, decides, updates the CRM, and learns in the field.
Those who invest in this today are not just improving their team’s productivity. They are building a structural competitive advantage in a market that rewards action and penalises hesitation.
Want to understand where AI can generate real impact in your commercial process?
I offer a free AI Sales Workflow Assessment: a structured initial conversation to analyse how your team works today, identify the priority use cases for AI, and define a concrete roadmap — what to implement, what to avoid, and how to set up training.
It is not a product demo. It is operational clarity.
Request your free Assessment →here
Sources
- Gartner Sales Survey, 2024
- PwC Global AI Jobs Barometer, 2024
- MLQ.ai / Project NANDA, “State of AI in Business 2025”
- Salesforce, “State of Sales” 7th edition, 2026
- Bain & Company, 2025
- McKinsey, “Next best experience”
- Microsoft + LinkedIn, Work Trend Index 2024
- LinkedIn, Work Change Report
- ISTAT / Quin, “Il Digitale in Italia 2025”
- Eurostat, ICT Survey 2025–2026
- BIS Working Paper 1325, 2026
- Gartner, Hype Cycle 2024
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