There's an old saying in sales that the best salespeople are often the laziest.
It does not mean they avoid work. It means they are relentless about removing work that does not improve the customer conversation. They want to spend less time updating customer relationship management (CRM) records, searching meeting notes and assembling background research, and more time understanding customers and winning business.
Customers do not reward effort. They reward outcomes.
That helps explain why sales teams have adopted artificial intelligence so quickly. They are not looking for shortcuts. They are looking for leverage.
I use Microsoft Copilot every day. Before an important customer meeting, I might use it to bring together the history of the relationship, summarise previous discussions and challenge my assumptions. When working on a proposal, it can critique the approach, identify gaps and pressure-test my thinking. For outreach, it can connect industry developments, customer priorities and earlier conversations into something far more relevant than a generic prospecting email.
The work still depends on experience and judgement. AI simply helps me apply both more effectively.
For me, AI is probably the most significant change in selling that I have seen in my career, largely because it changes the economics of time. Salespeople are expected to absorb a significant amount of information before speaking to a customer. Previous opportunities, support history, commercial discussions, proposals, stakeholder changes and market developments may all influence the conversation.
AI compresses the time needed to make sense of that context. Research can become deeper, meeting preparation more thorough and follow-up more consistent without taking hours longer.
For sales leaders, choosing not to embrace AI is increasingly becoming a competitive decision in its own right. A salesperson with access to a researcher, business analyst, proposal reviewer and coach throughout the day would once have had an extraordinary advantage. AI is beginning to make elements of that support widely available.
But its usefulness depends heavily on context. Generic information produces generic answers. When AI can work with relevant account information, customer communications, meeting notes and organisational knowledge, it can help people understand relationships and recognise patterns that would otherwise remain scattered across different systems.
That is where the value increases, and where the risk begins.
Most discussions about AI risk focus on data. For sales organisations, the more valuable asset may be the knowledge contained within it.
Every successful sales organisation develops its own understanding of customers, opportunities and buying decisions. That knowledge can be found across CRM platforms, account plans, proposals, emails and meeting notes, as well as in the experience of individual salespeople. It reflects years of conversations, successful decisions, mistakes and lessons learned.
Much of it is intellectual property. It is part of how the organisation competes, differentiates itself and wins.
This is why shadow AI, the use of AI tools outside an organisation’s approved systems and oversight, matters. Salespeople are unlikely to share information because they intend to be careless. They do it because richer context tends to produce a more useful result.
Someone preparing for a board-level customer meeting does not want generic advice. They want advice grounded in the history of the account. A proposal review becomes more useful when AI understands the customer’s requirements, while opportunity planning improves when it can consider previous interactions and stakeholder dynamics.
The information supplied to generate those results may contain far more than customer data. It can reveal commercial strategy, organisational knowledge and hard-earned insight into how the business competes and wins.
The relevant question is therefore no longer whether salespeople are using AI. It is whether the organisation understands what context is being shared, where it is going and what controls apply to it.
The way salespeople use AI can reveal weaknesses in the organisation around them.
If they rely on it to bring together information from several systems, that information may be too fragmented. If they use it to produce the first version of every proposal, the existing proposal process may be too slow. If meeting preparation depends on AI searching across multiple sources, finding reliable information may take longer than it should.
AI does not necessarily create these inefficiencies. In many cases, it makes them visible.
That distinction matters because treating AI adoption solely as a governance problem can obscure the operational reasons behind it. Organisations can introduce policies and restrictions, but employees will still be drawn towards tools that remove friction and help them perform.
The safer option therefore has to be genuinely useful. If approved tools cannot match the experience people can access elsewhere, policy will continually compete with productivity.
AI helped shape this article.
The central argument began with opinions I already held. AI helped challenge those opinions, test different angles and refine the narrative. I could have written the article without it, but structuring and revising it would almost certainly have taken longer. Instead, I reached the same point faster and moved on to the next thing that needed my attention.
The views and judgement remain mine, as does responsibility for every word.
The same principle applies to selling. AI may help draft an email, but the salesperson decides whether to send it. It may contribute to a proposal, but the salesperson owns the outcome. It can summarise a customer discussion, but it cannot assume responsibility for understanding what matters to that customer.
Customers still buy from people they trust. Curiosity, empathy, credibility, commercial awareness and judgement remain central to successful relationships. AI increases the importance of those qualities because people must decide whether its output is relevant, accurate and appropriate.
Sales leaders need to understand what their teams are trying to achieve with AI before deciding how to govern it. That means considering:
whether approved tools are good enough to compete with readily available alternatives;
which information creates the most value when combined with AI;
what AI use reveals about fragmented information or inefficient processes; and
whether the safest route is also the easiest one for employees to follow.
Organisations will not get the best from AI by ignoring its risks, but attempting to suppress useful behaviour is unlikely to succeed either.
Salespeople are adopting AI because it helps them prepare better, work more efficiently and make more productive use of their expertise. The challenge is to create an environment in which they can gain that leverage without exposing the customer insight, commercial context and organisational knowledge that make the business distinctive.
That is not a reason to slow down. It is a reason to give people tools that are both safe and worth using.