How can you communicate with customers facing AI and ML challenges?

As a sales engineer, you often have to deal with customers who are facing challenges related to artificial intelligence (AI) and machine learning (ML). These challenges can range from technical issues, such as data quality, model performance, or integration, to business issues, such as value proposition, use cases, or return on investment. How can you communicate effectively with these customers and help them overcome their obstacles? Here are some tips to improve your sales engineering communication skills when dealing with AI and ML challenges.

1 Understand the customer's context

Before you start explaining your solution, you need to understand the customer's context. What are their goals, pain points, expectations, and constraints? What are their current processes, tools, and metrics? How familiar are they with AI and ML concepts and terminology? By asking open-ended questions, listening actively, and summarizing their answers, you can build rapport, show empathy, and identify their needs and priorities.

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Tim Cortinovis

I inspire your business event audience and make them feel fantastic | 🌍 Global Keynote Speaker on AI | Top Voice | Top 100 Thought Leader Artificial Intelligence | Bestselling Author of Four Books
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One thing I´ve found extremely helpful: Let ChatGPT assume your buyer´s persona and ask it all the necessary questions that come to your mind. You will get inspiring and eye-opening answers.

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2 Simplify the complex

AI and ML can be complex and intimidating for some customers, especially if they are not technical or have limited experience with these technologies. To avoid confusion, frustration, or resistance, you need to simplify the complex. Use analogies, metaphors, or stories to illustrate how your solution works and what benefits it provides. Avoid jargon, acronyms, or technical details that are not relevant or necessary. Use visuals, such as diagrams, charts, or screenshots, to support your explanations and make them more engaging.

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Faraz H.

Senior Solutions Consultant | $4MM+ in Enterprise/SMB deals | Bridging Business & Technology | SaaS, MarTech, IoT | NAASE & PMI Certified
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Whether it is AI or ML, it is important to understand the background of the audience/buyer/stakeholder to whom you are speaking to. To effectively communicate the value of AI and ML technologies in a business context, focus on explaining how these solutions integrate with and enhance existing processes. Use process-oriented visuals and a conversational approach to demonstrate decision-making capabilities and implementation strategies. Prioritize clarity and relevance over technical jargon, ensuring stakeholders comprehend and appreciate the solution's value at their level of understanding.

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3 Demonstrate the value

One of the main challenges of selling AI and ML solutions is demonstrating the value they can deliver to the customer. You need to show how your solution can solve their problems, improve their outcomes, or create new opportunities. To do this, you need to use data, evidence, and examples that are relevant and credible. Use case studies, testimonials, or references from similar customers or industries. Show how your solution can achieve specific and measurable results, such as increased revenue, reduced costs, or enhanced efficiency.

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Faraz H.

Senior Solutions Consultant | $4MM+ in Enterprise/SMB deals | Bridging Business & Technology | SaaS, MarTech, IoT | NAASE & PMI Certified
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Case studies have been extremely effective for explaining the value of the solution - it allows the buyer to see a "vision" for success when they implement it. For the AI solution, it is important that you couple the case study with perhaps a demo of how part of the case study challenge was solved live while reflecting it in the context of the current buyer's challenge. What is important, is to keep in mind that any case study or customer story you present has resonance with the prospects challenges instead of showing an example of a wholly unrelated problem being solved.

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4 Address the objections

Another challenge of selling AI and ML solutions is addressing the objections that may arise from the customer. These objections can be related to technical, business, or ethical aspects of your solution. For example, the customer may question the accuracy, reliability, or security of your solution. They may also doubt the feasibility, affordability, or scalability of your solution. Or they may have concerns about the impact of your solution on their employees, customers, or society. To address these objections, you need to acknowledge them, empathize with them, and provide counterarguments or solutions. Use facts, logic, or testimonials to support your claims. Use trial offers, guarantees, or incentives to reduce the risk or increase the value of your solution.

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5 Follow up and follow through

The last tip to communicate effectively with customers facing AI and ML challenges is to follow up and follow through. You need to maintain contact with the customer throughout the sales cycle, provide additional information or assistance, and confirm their satisfaction and commitment. You also need to deliver on your promises, ensure a smooth implementation and adoption of your solution, and provide ongoing support and feedback. By following up and following through, you can build trust, loyalty, and referrals with your customers.

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6 Here’s what else to consider

This is a space to share examples, stories, or insights that don’t fit into any of the previous sections. What else would you like to add?

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Krishnadev A

Cybersecurity Evangelist, Specialist & Strategist | Product Strategy | Building Innspark Solutions
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Sales Engineers should be able to bridge the gap between customers and AI. Due to the AI hype created by ChatGPT, identifying the accurate buyer persona is one of the most important task. Once the persona is identified, Sales Engineer should then talk in the most relatable way to the customer. For eg : Rather than venting out list of Algorithms the solution provides, Sales Engineers must educate and empower customers by providing more real world and relevant use cases which the customer can achieve. Usage of jargons must be reduced to the core, if you really want to bridge the gap.

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How can you communicate with customers facing AI and ML challenges?

1

2

3

4

5

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1 Understand the customer's context

2 Simplify the complex

3 Demonstrate the value

4 Address the objections

5 Follow up and follow through

6 Here’s what else to consider

Sales Engineering

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How can you communicate with customers facing AI and ML challenges?

1

2

3

4

5

6

1 Understand the customer's context

2 Simplify the complex

3 Demonstrate the value

4 Address the objections

5 Follow up and follow through

6 Here’s what else to consider

Sales Engineering

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