AI augmentation: Re-engineering the sales engine

Sales process augmentation offers one of the shortest and most clearly defined routes to ROI from AI investment. Forget potential or promising use cases, sales is fertile ground for top-line and bottom-line benefits, without the pain of workforce displacement. 

Through AI augmentation, including selective automation, sales teams are empowered to make more lucrative deals, faster. Gartner goes so far to suggest, for example, that sales organizations that provide their teams with AI-enabled next best actions are 2.6 times more likely to achieve commercial growth than those that do not.¹

Crucially, however, such positive disruption can be deployed to amplify the effectiveness of the sales force while preserving individual practitioners’ autonomy when it comes to specific customer engagement and relationship building. With careful and determined change management, augmenting sales processes unites the strengths of both AI and humans-in-the-loop to deliver the tangible business value CEOs and CIOs are currently craving.

AI augmentation advantages

One of the clearest illustrations of AI’s transformational impact on sales is territory management. Sales organizations typically assign portfolios of accounts to representatives at the start of the fiscal year, based on static criteria such as client size or segment, and leave that allocation largely unchallenged until the next planning cycle. AI allows this segmentation to be reviewed continuously, analyzing and surfacing the accounts that carry the greatest potential and how territories should be redistributed.

The same logic extends to account preparation. Rather than relying on a representative's memory or hurriedly taken notes from a client visited six months earlier, AI solutions with predictive analytic capabilities can collate and analyze internal account histories, CRM data, and external signals such as public filings to recommend:

This last point is crucial, as it layers decision intelligence on top of efficiency gains to enable sales representatives to create better quality deal offers that they are more likely to close with the client. Work that once demanded a dedicated sales intelligence function, or hours of a sales professional’s own preparation, can now be done in a fraction of the time, cutting the manual workload burden of sales support teams.

Arguably the most commercially significant impact from AI-augmented sales, however, relates to deal velocity: the time between lead identification and revenue recognition. AI is collapsing the distance between insights and commercial action. BearingPoint research into sales transformation identified this as one of the outcomes most valued by surveyed executives.² AI shortens this cycle by prioritizing the right leads earlier and accelerating the administrative work, such as contracting and paperwork, that slows a deal's close. AI also plays a significant role in accelerating the preparation of the technical and commercial offer. A US telecommunications giant, for example, estimated that equipping its sales organization with an AI copilot saves representatives an average of four hours a week, worth an estimated $50 million annually.³

A Gartner study⁴, for example, found that B2B buyers were 28 percentage points more likely to report that a representative, rather than generative AI, helped them advance the purchase process, and 39 percentage points more likely to say a representative understood their needs. Furthermore, clients that spent more time with sales professionals were 13 times more likely to report a high-quality deal.⁵ 

Heterogeneity is not an obstacle

A common objection to sales augmentation is that no two sales organizations operate alike. A telesales team selling inbound and outbound to a broad base bears little resemblance to a key account manager selling industrial machinery, or a field representative building hospitality relationships over years. This diversity should be viewed as a design consideration, requiring AI to be adapted to the specific processes of each sales environment.

AI creates value by understanding how an organization's sales process operates and tailoring its recommendations to individual accounts. Applying AI successfully therefore starts on the ground, close to the specifics of a given sales motion – such as pricing quirks or account-level judgment calls – so that nuance is built into the operating model rather than designed around it.

Clearing the change management hurdle

The challenge of embedding AI augmentation in sales is typically driven more by cultural resistance than by technical limitations. Sales representatives, particularly in B2B environments, operate with a high degree of autonomy and often resist anything perceived as a constraint on it. Time away from active selling is viewed as time away from hitting targets, making even a short training session a hard sell.

Consequently, successful change management ahead of the introduction of AI capabilities should start with an initiative to form an understanding of how representatives currently work, including the parts of the process that never make it into a CRM system. Winning buy-in from the sales team is key. Change management programs need to index heavily on conveying how individuals personally gain from AI augmentation of their processes.

Organizations should make sure salespeople are part of the transformation journey from the start, while using their knowledge to help shape integration. As soon as AI is framed as an accelerator of deal velocity and a conduit to easier achievement of sales targets, suspicion of augmentation rapidly turns to advocacy.