Anand Shah is cofounder and CEO of Databook, a pioneer and leader in Strategic Relationship Management (SRM).
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AI is reshaping sales at a lightning-fast pace. According to McKinsey research on AI-powered marketing and sales, 90% of commercial leaders expect to utilize generative AI solutions "often" over the next two years. The same report found that organizations that invest in AI are seeing a revenue uplift of 3% to 15% and a sales ROI uplift of 10% to 20%.
In the race to embrace generative AI, sales leaders must assess both the immediate impact as well as the bigger, long-term picture. Yes, AI will supercharge tactical lead identification, lead scoring and marketing optimization immediately. But, with the right approach, it can also deliver exponential value by accelerating the change that desperately needs to happen for sales to claim their role as trusted, strategic advisors.
Done correctly, AI can elevate conversations to speak to more executive buyers and create new ways of managing long-term, strategic customer relationships. But every seller needs to take responsibility for updating, checking, verifying and distributing information—a more challenging task than one might think. In a recent MIT study on humans’ trust in AI, participants who had access to an AI’s answers for a test still only chose the correct answer 57% of the time.
That’s just basic fact-checking. Even more important is engaging customers with humanity, curiosity and intention. Establishing a deeper human connection—not blasting emails created by ChatGPT—is the Mission: Impossible facing every rep and leader.
This is easier said than done, especially with AI innovation moving much faster than company policies and use cases. Just as organizations codify business ethics to guide employees in their daily actions and decision making, sales leaders can do the same based on these three principles of accountable AI usage.
1. Trust And Accuracy
There is plenty of hand-wringing on the dangers of generative AI when it comes to the potential to proliferate inaccurate information, whether in the form of hallucinations, deepfakes or just plain wrong facts. These same fears extend to the corporate world, where revenue rises and falls on whether your insights are correct. This means every single business and user leveraging generative AI is responsible for fact-checking. Sales leaders must support this by vetting and prioritizing tools that are proven to be transparent, accurate and trustworthy.
As an example of accuracy coming into play, a basic question that sales executives ask in deal cycles is, "Who does my customer compete with?" As an exercise, I asked ChatGPT to weigh in on Nike’s competition, and it returned the following list: Adidas, Under Armour, Puma, New Balance, Reebok, Asics, Skechers and Vans.
While these are all worthy brand competitors, ChatGPT fails to understand this as both a business question and in a global context. First, Nike’s competitor isn’t Vans, but VF Corporation, which owns the Vans brand. And the tool completely missed two of Nike's largest competitors in its largest growth market, China: Li Ning and Anta Sports.
To be valuable for sellers, the answer needs to cover the companies that compete financially with Nike and whether they are doing better or worse. Mistakes like these undermine credibility and make it harder to win deals and retain customers.
2. Confidentiality And Security
No one would give their CRM data to a public search engine, and no seller should just feed their data into ChatGPT either. Every prompt goes to build more learning in ChatGPT’s private data model, and companies are increasingly wary of data leaks. As a few recent examples, Samsung banned the AI chatbot and other generative AI tools after finding that employees uploaded sensitive code to ChatGPT, and others like Amazon and Apple have blocked ChatGPT by employees.
Prioritizing data security keeps confidential information and trade secrets out of the public domain—and opens the door for new value from AI. The magic of generative AI happens when models can synthesize real-time proprietary insights based on merged public and private datasets.
The analysis to understand what happened, why it happened and what someone should do next, given the context of their previous actions, can be available instantaneously. For example, senior management identifies new advanced technology investments that automatically identify new use cases and stakeholders for the sales team to engage. And models can continuously learn from real-world data and user interactions (for example, won deals vs. account strategies vs. user activity) to attain a deeper understanding of customer needs and how to best meet them.
3. Strategy And Relevance
Nothing about ChatGPT or LLMs is inherently strategic. So using AI to supercharge tactical execution is counter-productive if you are just creating more spam. Especially when you need to compel executive buyers, sellers need to speak to business priorities and urgent needs. This requires highly accurate information on every business, their unique challenges and performance against peers—at scale and accessible to every seller on a team.
For example, I worked closely with a well-known cloud-native enterprise SaaS company that used generative AI to quickly identify a target account’s financial case for change, strategic and board priorities and pain and urgency for distinct buying groups—and then match them to relevant use cases and solutions. They were already working with this customer, but this helped them engage the CEO with a much higher value proposition.
This strategy resulted in an immediate response to connect with the CTO and identify ways to increase the partnership. The approach is now being replicated across dozens of accounts to drive multi-million dollars of pipeline growth within a quarter and ultimately drive better outcomes for customers who are looking for ways to improve performance and deliver better solutions.
Advancing Productivity—And Freedom In How Jobs Are Performed
Empathy, emotional intelligence and curiosity can’t be replaced by AI—and thoughtful and intentional AI usage frees sellers to do more of the things they find rewarding and satisfying. Buyers, in turn, can quickly narrow down and purchase exactly the right solution to drive their business forward in partnership with salespeople whom they find helpful, knowledgeable and trustworthy.
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