Over the past two years, generative AI has taken center stage with promises to improve productivity by accelerating software development, streamlining marketing content, enhancing support solutions, and reducing administrative burdens. Despite the enthusiasm, most companies haven’t unlocked these benefits at scale or seen meaningful gains in cost efficiency or revenue growth.

Now, agentic AI is stepping in with self-directed agents that can follow a complex workflow, set goals, plan, execute, and learn on the fly—all with minimal human input. The potential? Smarter systems, faster outcomes, and more room for people to focus on what really matters.

But truly successful results remain rare. Many companies are logging small productivity improvements in a few areas such as software development, but only a few can measure their success in double digits.

That’s because most companies haven’t cracked the formula yet on implementing AI at scale—and sales represents a more difficult challenge than most activities for a handful of reasons: 

• One use case rarely moves the needle because a seller’s day is fragmented across dozens of tasks. Most companies haven’t stepped back to map the end-to-end selling journey, so efforts remain piecemeal.
• Bottom-up experimentation doesn’t work because the objectives are inherently unclear.
• Applying AI to existing processes often results in only small productivity gains (micro-productivity) because new bottlenecks emerge. Without process redesign, companies end up automating inefficiencies instead of removing them.
• AI needs massive data context and cleanliness but sales and go-to-market data are spread across many systems with little quality control or governance.
• Sales teams are stretched and distracted, and this is one more tool in a long parade of tech promises. Unlike functions such as engineering, in which workflows are relatively standardized, sales processes vary wildly by team, region, and individual.
• Frontline teams are often reluctant to change their behavior. Making quota is seen as “good enough,” and AI training is typically static.

The upside, however, is too promising to ignore. Sellers may spend only about 25% of their time actually selling to customers. AI could double that by taking on much of the work that surrounds selling but doesn’t add much value, leaving more time for customer service (see Figure 1). And that’s only half the picture: AI also helps teams improve conversion rates at every step in the selling funnel—step-change improvements that add up to more than a 30% increase in win rates.

Figure 1

Mapping AI across the sales life cycle

Sales teams looking at this potential from AI need to determine where AI can deliver the biggest gains and where to start. Bain’s work with business-to-business and business-to-consumer technology and consumer companies deploying AI in sales has identified 25 use cases across the various steps in the sales life cycle that leaders should explore to capture maximum benefits from deploying AI (see Figure 2). Some of these started as more traditional software automation and were enhanced by AI/machine learning. Many of them have been further enhanced by generative AI, and now we’re seeing agentic AI deployed in several use cases.

Realizing agentic AI’s potential

The deployment of agentic AI promises to unlock even more value in sales. The technology is moving quickly, but most companies are still crawling. Vendors are likely to deliver more capable applications over the next 6 to 18 months, but already we’re seeing targeted results at scale—for example, among companies using no-code workflows (see Figure 3). The biggest hurdles remain cleaning the data, standardizing the process, making difficult governance decisions, and changing the way work gets done (which must include shutting down the old ways of working as well as access to old tools/data).

Figure 3

Identifying where to get started

Many companies struggle with where to begin given the wide range of viable AI applications. The domains in Figure 2 illustrate use cases that are often interdependent, making it hard to move forward without first addressing foundational elements such as data architecture and business alignment.

Take lead generation and prospecting. Without clean, connected data, sellers don’t know why an account is hot, who to engage, what to pitch, or how to tailor the message. While many firms jump ahead to guided selling, reps first need insights that are trustworthy, easy to act on, and genuinely new.

The most effective pilots focus on one or two domains at the front end of the sales life cycle, in which sellers need the most help identifying, informing, and acting on leads. Leading companies build from there, prioritizing use cases based on business value and process readiness. That approach lays the groundwork for lasting gains in sales efficiency, stronger customer engagement, and seller confidence in AI tools.

Landing the full potential of AI in sales

In our work helping companies experiment with AI in sales, we’ve seen a consistent set of lessons emerge that separate the pilots that fizzle from those that scale.

• Adopt an end-to-end view of a process. Generative or agentic AI may be the headline, but the real value lies in the combination of agentic and generative AI with traditional AI and automation, process redesign, data cleanup, top-down target setting, and focus of execution.
• Reimagine processes. Automating mediocre processes only accelerates mediocre outcomes. Rethink selling activities and develop best-practice workflows.
• Narrow the scope to scale. Trying to do everything at once slows momentum. Start with high-impact slices of the sales process (for example, one or two domains out of the six in Figure 2) and build a roadmap that reflects your commercial motion.
• Focus on the data, with a bias toward speed over perfection. Data matters, but perfection isn’t required. Focus on what’s good enough to move fast and what’s needed to clean up the data to reach that point. The first step is eliminating old, inaccurate, or confusing data and content—sometimes as much as 80%. It takes time and resources; don’t underinvest here.
• Test, learn, iterate. Rapid proofs of concept are your best tool to identify where value exists. They also build conviction around the vision and the steps to get there.
• C-level sponsorship and execution. Solid change management is table stakes; a true AI transformation will also require sustained focus from the executive suite. A dedicated implementation team with real capabilities should be given accountability for setting targets and reaching goals.

AI has huge potential to transform sales, but most companies aren’t seeing meaningful results yet. To turn promise into performance, teams need to identify and prioritize high-value use cases, reimagine critical processes, and clean up their data. It all hinges on a clear, top-down commitment to deploy AI at scale. When done right, leaders can dramatically improve life for frontline sellers and build a durable edge over competitors still stuck in wait-and-see mode.