Finding the Right Starting Point for AI in Sales

21 Sep 23

The rapid evolution of artificial intelligence (AI) has created many opportunities for improvement across the organization, but there are considerations before such solutions can be applied to sales.

The present state of artificial intelligence (AI) development and the proliferation of use cases for this technology has gotten the whole world talking. Now that the conversation has reached sales, this raises the big question in the minds of every top sales leader: “How should we be thinking about AI? Are there ways we can use this technology to improve sales and sales effectiveness?”

No doubt many CEOs have already seen AI adoption in other parts of their organizations. AI has long been driving productivity improvements in finance with automated payment processes, while marketing has recently begun to explore generative AI for content idea generation, writing, editing, and tailoring. The highly complex data and nuanced processes involved in sales deals can make it hard for a blanket adoption of AI across sales. However, there are use cases where sales teams can create value with the technology now, if they have the necessary framework to take advantage of it.

What is (and isn’t) Generative AI?

Generative AI is:

  • Machines that exhibit human-like intelligence and capabilities.
  • Systems that learn, reason, and autonomously make decisions.
  • Machines that learn to understand, interpret, and respond intelligently to input.

But it is not:

  • Machine learning by another name. It encompasses a wider array of techniques that includes machine learning and natural language models.
  • Just task automation or beefed-up VBA. It emphasizes intelligent problem-solving and decision-making.
  • A replacement for human intelligence. It works best in tandem with humans and requires oversight in complex settings.

According to the IBM Global AI Adoption Index (2022), 77% of businesses have started using or exploring AI in their processes at the time of the study. Some 30% of companies report significant time savings from leveraging AI-enabled applications in their processes. However, as many as 24% of companies admit to having too much data complexity to adopt AI in the near term—a problem that could be highly applicable to sales teams. SBI research finds that only about a third of companies have done beyond "exploring options" in their adoption of generative AI in their go-to-market activities.

Graph AI

Possible Use Cases for AI in Sales and Revenue Ops

Based on some general observations on the sort of AI-enabled applications that some vendors offer today, SBI believes there are many use cases where sales teams could begin to explore—some reactive and some more proactive.

Some reactive examples include using more advanced chatbots to enhance customer service and seller service, as well as documenting sales interaction summaries. Generative AI has already created opportunities for improved email personalization and content customization. AI tools may also be applied to identify and generate next steps in sales interactions or gather insights on sales interactions to create actionable data for coaching purposes.

As AI technology continues to be developed, sales teams may find more proactive use cases for AI, like serving as a co-pilot for real-time sales interactions, analyzing forecasting and deal progression, or enabling data-rich prospecting motions.

Where Can We Start Experimenting with AI?

AI technology is still far from perfect, so most organizations seeking to try out AI applications will probably want to ensure that they start with areas that would create value while avoiding any potential risk or disruption to the business. When evaluating ideal use cases for AI to start with, it helps to figure out if a particular area:

  1. Has higher friction and presents more opportunities to solve problems using AI, and
  2. Has higher stakes from having such major changes implemented.

Transparent AI Table

Generally, areas with higher friction would make for prime opportunities for a company to experiment with AI, especially in areas where the stakes are low. But in low-friction areas, it may not be worth the trouble to try out AI and may potentially be harmful in areas where the stakes are higher.

To start out with AI, companies may need to lay different foundations depending on whether they decide to build or buy their solution. The ‘build’ option would require more robust data and advanced data science specialists, while the ‘buy’ option will need an ingrained learning mindset and culture as well as dedicated prompt specialists. Whichever route a company chooses, they will also need systems integration and “canonical” examples to start training models with the AI.

Four Key Takeaways from the SBI Perspective on AI Use

To companies curious to explore the value they might generate from leveraging AI technology, SBI has four key recommendations:

  1. Experiment, but recognize that you aren’t behind the curve yet. Most GTM professionals don’t yet trust AI enough to rely on it heavily and the technology is still evolving quickly, so there’s no pressure to make drastic changes.
  2. Identify the initial AI-impacted universe. The experimentation period is the right time to determine risk tolerance and what your initial AI goals are, whether it’s internal efficiency or customer experience.
  3. Pick 2-3 areas and track their road maps. Identify some near and farther-horizon opportunities as well as the current players in those spaces, then request a demo and monitor their progress so you can keep an eye on emerging strong players.
  4. Develop a far-horizon point of view to develop cultural acceptance of where things are going. Envision a future where AI has become ubiquitous to your organization and processes; use experimentation, monitoring, and this visioning exercise to build momentum for incremental investments.


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