Utopia or Disaster? AI’s Transformation of Sales

If there’s one thing that can be said with certainty about artificial intelligence in the field of sales, it’s this: It’s coming.

Solutions for sales leaders, account executives and business development reps tout various AI-enabled features and the benefits they offer to sales. Writers breathlessly forecast the loss of millions of jobs due to AI, including the decimation of most professions, sales included.

Beyond the hype, what is true? What are the real positive and negative implications of AI on the sales profession?

To separate hype from reality on a topic like artificial intelligence, we need to get beneath the Hollywood view of sentient robots taking over the world and understand what AI truly is and what it’s capable of. With that understanding, we can begin to break apart the discipline of sales and identify the areas where AI likely will have a profound effect.

What Really Is AI? What Truly Is Sales?

So what, really, is artificial intelligence? Fundamentally, it’s a set of software tools that can be trained to recognize patterns. Whether we are talking about self-driving cars, image recognition, or natural language processing, each is all about recognizing a pattern — even if the style of the tool might change. Once trained, an AI system can recognize examples of a pattern quickly and easily.

When applied to the real world, AI is good at tasks that can be carefully trained once, and then repeated many times. As with all software, it scales astoundingly well when compared to humans performing the same task. However, it does poorly at tasks that are new, unique or unprecedented.

From a sales perspective, we can identify pattern-oriented, repetitive tasks that might be replaced by AI. However, this is harder than it seems, as some tasks that appear to be repetitive and pattern-oriented are actually quite complicated when viewed more closely.

Sometimes we might sell more effectively if we could repetitively perform a task that is too burdensome when done manually. Only when we break down what it means to sell can we tease out the positive and negative implications of AI on the sales process.

To get buyers to buy, we need to do two things: inform them that a new approach is possible, and convince them that the new approach is what they should do. This, of course, is much harder than it sounds, as anyone in marketing or sales knows well.

Buyers are bombarded with information relentlessly and have equally rich access to information that they can look for on their own. Gone are the days when information on a solution was enough to secure interest or even a meeting. Attention from the buyer, not information, is the rare commodity.

Similarly, everyone peddling a new approach, service, supplier or solution is trying to convince the buyer that their option is the best. The competition includes different options for solving the same problem, as well as different problems that are fighting for prioritization.

Convincing buyers to deal with the problem you solve — urgently, right now — is often a more difficult task than convincing them to use your solution over another competing solution.

A Human Model of Excellence

So how do great sales people cut through this noise? They focus relentlessly on two things.

First, they deeply understand what the solution means for the specific buyer at that specific company and in that specific situation. Using this insight, they can deliver a message that truly resonates.

Second, they focus on building the one thing that cuts through all the noise: trusted relationships. Sometimes they build relationships with the ultimate decision maker directly, and sometimes it’s with trusted staff members. Buyers are far more open to shifts in viewpoint and perspective when their source of information is a trusted confidant.

With this as a model for selling, we can start to understand how AI will change sales. As a first pass, let’s look at all the tasks that are pattern-oriented and repetitive. Sales outreach, sales research, mapping buying committees, and sales forecasting are obvious starting points. They are all very repetitive tasks, and they follow quite predictable patterns. As it happens, each offers a unique model for ways in which AI will affect sales.

Transforming Each Task

Sales outreach is an interesting example to start with. The current trend of highly automated, generic outreach (“Did you get my email?” or “Just following up on my past email,” etc.) is clearly ripe for automation. It is repetitive and follows a very obvious pattern.

In fact, vendors in the sales automation space already have been automating this with various levels of AI. The challenge here is that the more the task gets automated, the lower its value. If you view this through the lens of the buyer, clearly the deluge of “Did you get my email?” outreaches will add less and less value the more automated it becomes.

Sales research provides the opportunity for a better form of outreach. Knowing what’s happening with an individual company or buyer allows the outreach to be deeply personalized to reflect the challenges of that one buyer’s reality. However, research takes time. When it needs to be repeated for each account at least a few times per week to find new and interesting nuggets of information, it quickly becomes impossible at scale.

AI can help with the research, at massive scale, in a way that tees up interesting insights for sales people on all their accounts. Then comes the creativity involved in translating news about an executive change, new product, or acquisition into a powerful sales outreach. This involves the uniquely human ability to understand what likely has been motivating each individual, given the recent news. In this way, AI and human salespeople together can make outreach efforts much more effective.

Understanding and guiding the dynamics of a buying committee is another unique challenge of sales. Many buyers are involved in any significant decision. Sales teams need to be aware of who is involved, how they are leaning, and who might be able to influence them. An exercise to map out these relationships can be done manually, but the effort involved makes that prohibitively expensive on all but the largest deals.

AI is able to model an understanding of organizations, buyers and relationships effortlessly, and to enable single-threaded deals (where there is only one established relationship into the account) to be flagged, potential introductions to be identified, and relationships across the organization to be measured and visualized. This allows every deal cycle to be understood with the rigor that historically has been reserved for seven-figure deals.

Sales forecasting is another area of sales that serves as an interesting case study for the positive and negative changes AI will bring. Forecasting a quarter’s performance is an exercise in comparing deals in the current pipeline with what successful deals typically look like at the same point in the quarter. This often involves deal reviews in which each deal is explored in depth to understand which relationships are in place and which are missing.

With AI to model the buying organization and understand the relationships, this in-depth review of each deal becomes much easier. This, of course, simplifies the act of forecasting, and it also opens up new ways to improve sales performance. With this view of an account available at the click of a button, sales leaders can focus on guiding deals that are at-risk, coaching reps who are weak at developing certain relationships, and accessing new opportunities.

A Transformed Discipline

When we look at sales through the lens of individual tasks, an interesting picture emerges. AI will make some tasks irrelevant while it greatly enhances others. It will make advanced techniques available to broad market sales teams, and it will provide insights to allow leaders to be better coaches and mentors. It is hard to think of any of these transformations as inherently good or bad — they are just different.

However, avoiding the transition clearly can be categorized as bad. Sales teams who rely on undifferentiated sales automation clearly will see their teams’ results continue to plummet as AI reduces the value of automated outreach to zero.

Account executives in the mid-market who continue to manually map out buying committees will be outmaneuvered by sales teams with that level of insight at their fingertips, utilizing AI. Sales leaders who manually review deals will continue to be plagued by inaccurate forecasts and deal slippage. They also will fail to transition from reporting on sales performance to coaching toward better performance.

Sales will change dramatically over the next decade as AI permeates all aspects of the process. The day-to-day lives of sales professionals and leaders will be nearly unrecognizable by today’s standards, but the challenge of convincing buyers to adopt a new mindset will remain.

The winners of this transformation will be those who leave to AI the tasks and tactics that are best done by AI, and who invest heavily to gain a deep understanding of how to guide buyers to think differently about the problems they face.

Steve Woods

Steve Woods is cofounder and CTO of, a relationship intelligence platform that helps businesses find and grow the right relationships that drive sales. He previously served as cofounder and CTO of Eloqua, a company he helped guide to a leading position in marketing automation while growing it to a $100M revenue run rate, shepherding it through its IPO on the Nasdaq, and ultimately positioning it for acquisition by Oracle.

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