Reflections and Suggestions for Sales Leaders

Javier Marcosa, Andy Houghb, Richard Brooksc

Artificial Intelligence is now ubiquitous and arguably the next major transformation in business and organizations.

First, allow us to clarify that artificial intelligence is the simulation of human intelligence processes by machines, especially computer systems. Specific applications of AI include expert systems, natural language processing, speech recognition and machine vision. Generative artificial intelligence (AI) describes algorithms (such as ChatGPT) that can create new content, including audio, code, images, text, simulations, and videos. For the purposes of simplicity, we will refer to “AI” indistinctively.

It is very likely that in your business, like in many others, including your competitors, there is a debate as to whether you should adopt artificial intelligence in your sales organization and, second, how to do it. Indeed our conversations at the recent Global Sales Science Institute annual conference in Toronto (7-10 June) addressed this theme[i].  AI is a big, transformational technology trend, and will become even more significant. Kevin Peesker, President Worldwide Small, Medium, Corporate & Digital of Microsoft, discussed how the leading technology firm is deploying 50,000 engineers to AI. Why? Generative AI will transform jobs within organizations and will often boost performance. McKinsey, estimates that generative AI could add the equivalent of $2.6 – $4.4 trillion annually, especially in areas such as customer operations, marketing and sales, software engineering, and R&D[ii].

We argue that if your organization has yet to start scoping the applications of AI to your sales operations, you may be too late. The cycles of major technology breakthroughs are not happening every few years, but every few months. Let’s examine how AI can be used in professional selling and account management.

1        Applications of AI in sales

A promising application of AI in sales is lead generation and qualification. Companies both in B2C and in B2B spend significant resources in evaluating and deciding whether and how to best to pursue deals[iii]. Chat GPT can perform sales research to provide valuable insights to assess the desirability and winnability of sales opportunities[iv]. The company Conversica offers digital assistants powered by a multi-model conversational AI platform that uses a number of large language models (LLM), including GPT, to interact with your contacts in human-like conversations designed to drive pipeline and convert customers.

AI offers the opportunity to provide new and enhanced forms of personalized sales engagement. For instance, RichRelevance enables businesses to create relevant and seamless shopping experiences across all touchpoints. They use a powerful engine to select the most contextually-relevant personalized product recommendations for each customer interaction. One of the advantages is the provision of a personalized experience across all touchpoints.

Another (already) established application of AI is sales forecasting and predictive analytics. One of the biggest challenges that sales leaders encounter when creating their forecasts is to estimate the proportion of deals in their pipelines that will never convert to adjust budgets accordingly. Matt Dixon and Ted McKenna estimate that between 40 percent and 60 percent of deals today end up stalled in “no decision”[v]. In their research, they used machine learning models to rate salespeople’s ability to address customer indecision and, therefore, better predict and enhance forecasting accuracy and, indeed, salesperson performance.

A related area, sales process optimization, is being redefined by the application of AI in large-volume sales operations. For instance, the ability to craft and direct e-mail content to your customers is now enabled by AI. The company Lavender AI has developed an innovative sales email coach. This service assists sales agents in real-time writing better emails faster, thus helping them get more positive replies.  In B2B sales, companies such as Qvidian, Proposify and Better Proposals provide solutions to optimize sales proposal creation. By using pre-built templates and machine learning algorithms, they automate the process of creating personalized proposals.

The world of sales performance analytics is in a state of flux. Peter Kerr, a leading sales academic in Canada, showed how organizations need to combine both subjective and objective performance measures to better capture salesperson performance[vi].  AI-powered tools such as Chorus helps sales teams capture and analyze customer calls, meetings, and emails to create more visibility, enhance sales processes, and enable behavior changes. This technology helps sales leaders better combine leading and lagging indicators of performance. This has fundamental implications for resource allocation, rewards, and promotion.

With better insights into why people perform (or otherwise), sales leaders can better direct sales training efforts. Again, AI has a role in this. For instance, in sales education, provides students with the opportunity to practice 24×7 voice-driven roleplay. Practice makes perfect, and early career sales professionals now have the chance to practice and develop their skills effectively.

We all know the substantial, direct impact of price on a company’s profitability, to the point that Warren Buffet claimed that “the single most important decision in evaluating a business is pricing power. If you’ve got the power to raise prices without losing business to a competitor, you’ve got a very good business. And if you have to have a prayer session before raising the price by 10 percent, then you’ve got a terrible business”.

The question for sales leaders is, how to optimize and ensure justified discounts are offered? Companies like Bubo AI offer pricing optimization tools, so businesses can fine-tune their pricing strategies and maximize profit margins by applying targeted pricing tactics.

Lead generation and qualification
Personalized sales engagement
Sales performance analytics
Sales process optimization
Sales performance analytics
Sales training
Pricing optimization

Table 1. Summary of applications of AI to professional selling and sales management

Looking at the wide-ranging applications of AI, one could be tempted to focus on its benefits. These are being increasingly realized by companies, but there are also risks we would like to highlight.

2      Benefits and risks of AI in sales

The gradual implementation of AI technologies will change some of the dimensions of the work of roles such as sales representatives, sales operations, account managers, sales enablement teams, sales analytics teams and customer success managers. The implementation of AI will enable these roles to improve upon the areas outlined above.  However, AI is not free from several risks.

First, overreliance on AI itself: these technologies are very limited when human judgment and intuition are required to perform a task and obtain an outcome. AI should be seen as a tool to assist sales professionals rather than a complete replacement for human decision-making.

Second, data bias: AI algorithms are only as good as the data they are trained on. If the training data contains biases or inaccuracies, is limited in scope, or highly context-specific, the AI system may produce biased recommendations or predictions. This can have unintended consequences for people and organizations. For instance, incorrect use of performance data may result in unfair treatment of customers or staff, suboptimal allocation of resources and flawed decisions[vii].  AI systems can have dysfunctional effects that may not be immediately apparent. For example, automated sales processes driven by AI could result in flawed interactions with customers, particularly if they do not adequately address individual customer needs or preferences.

Third, lack of transparency. Some AI algorithms, such as deep learning neural networks, can be complex and difficult to interpret to non-specialists. This lack of transparency can make it challenging to understand how the AI arrives at its decisions. Sales managers may face difficulties in justifying or explaining the reasoning behind AI-generated recommendations.

Fourth, AI has triggered privacy and security concerns: AI systems in sales often rely on collecting and analyzing large amounts of customer data. The improper handling of sensitive customer information can lead to privacy breaches and security vulnerabilities. Organizations must ensure they have robust security measures in place to protect customer data.

Finally, there are ethical matters that sales leaders need to consider: AI systems in sales must adhere to ethical guidelines to avoid unethical practices or manipulation. For instance, using AI to manipulate customers’ emotions or preferences in a deceptive manner could harm customer trust and brand reputation.

Overall, the advantages of AI technologies can be constrained by some of its limitations. In particular, the “approaches that work” in strategic sales and key account management are context-specific, and, thus, require laser focus and precision, characteristics that AI technologies are nowadays currently lacking.  

3      What’s next for sales leaders?

As Professor Ethan Mollick, the author One Useful Thing, the useful Substack that translates his academic research on AI and business into practical learnings [viii] claims that “general purpose technologies are these rare events like steam power, the computer, or electrification, or maybe the internet, where a new technology comes along that touches everything”.

We believe that AI will touch everything but will not change everything. Key account managers and sales representatives will specialize wherever AI is worst. They will use technologies to better discern the customer relationships that matter, and then, will devise specific plans to co-create customer value over the long term. They will very likely use automated profiling tools like Crystal Knows to better know their key contacts and then, target their approach and run more effective meetings and client interactions. Key account managers will continue to be the knowledge brokers that can solve complex, and unique problems. AI follows the rules and patterns and it’s not good at breaking them. Key account managers and B2B sellers will continue to challenge inertia and to co-create the solutions their strategic accounts require in times of uncertainty and disruptions.  

We encourage sales leaders to embrace AI technology and to educate themselves and their teams, to upskill their sales forces, and to equip them with relevant AI tools, instilling a sense of adaptability and purpose. We invite sales leaders to relentlessly pursue the highest ethical standards in the adoption and use of AI. 

The invention of the printing press led to mass literacy. We wonder whether AI will lead to a ‘mass’ of customer centricity and customer value creation in sales organizations. A key debate in AI is the potential impact on job displacement: our view is that AI will not replace the salesperson, but the salesperson who has knowledge and command of AI will replace the one who doesn’t.

We look forward to continuing this dialogue at our KAM Forum conference on the 23/24 November 2023 at Cranfield. Get in touch and check our website for the latest updates.

NOTES. We do not endorse or recommend any of the service providers mentioned in this article. We are just using them as examples of AI applications and potential benefits.

In addition to our own research and that of others which is cited below, we have, given the nature of this article used ChatGPT to provide some information about the risks of using AI.

a Javier Marcos is Professor of Strategic Sales Management and Negotiation at Cranfield School of Management and the Director of the KAM Forum.

b Andy Hough is Lecturer in Sales Leadership and Performance and the Founder of the Institute of Sales Professionals.

c Richard Brooks is a Visiting Fellow at Cranfield and advisor to companies in their growth strategies.

[i] Robert M. Peterson, Deva Rangarajan, Howard Dover, and Cindy Gordon (2023). Artificial Intelligence Tsunami Hits Sales. GSSI, Toronto 7-10 June 2023

[ii] McKinsey, 2023. The economic potential of generative AI – The next productivity frontier. June 2023

[iii] Guesalaga, R., & Kapelianis, D. (2015). When do salespeople pursue and win deals? a two-stage model of sales opportunity outcomes. Journal of Business & Industrial Marketing, 30(7), 817–829.

[iv] Ryals, L., Rackham, N., 2021. Sales Implications of Servitization The Implications of the Servitization Trend for Selling. Cranfield Key Account Management Forum.

[v] Dixon, M., & McKenna, T. (2022). The JOLT Effect; How high performers overcome customer indecision. Penguin Portfolio.

[vi] Kerr, Peter D., and Javier Marcos-Cuevas (2022). “The interplay between objective and subjective measures of salesperson performance: towards an integrated approach.” Journal of Personal Selling & Sales Management 42 (3) 225-242.

[vii] Franco‐Santos, Monica, and David Otley (2018). Reviewing and theorizing the unintended consequences of performance management systems. International Journal of Management Reviews 20(3), 696-730.