Why should companies leverage AI in their revenue enablement?

AI increases the speed and scale of development – enablement strategies traditionally rely on large teams to execute significant manual work. AI makes it possible to implement real-time, meaningful, and scalable enablement strategies at a fraction of the cost and organizational burden.

It unlocks value-driven personalization – AI powers customized onboarding paths, iterative training programs, and in-depth content creation that would otherwise be infeasible or prohibitively expensive.

Practice environments become more accessible – AI creates at-scale opportunities for sellers to practice and receive feedback in a risk-free setting, enabling them to improve faster and receive tailored input at every stage of their professional development.

Use Cases

What are common revenue enablement use cases that companies should consider when implementing AI?

Training use cases

Personalized onboarding and training paths – expedite the onboarding process by maximizing content that can be trained asynchronously. For example, AI can create a training plan to teach an important skill, then provide a live simulation to drive application of that skill. This approach allows you to train new hires or onboard large groups of individuals without facilitator availability becoming a bottleneck.

Real-time practice for specific scenarios – create AI customers that provide sellers with experience in hard-to-plan-for situations. For example, sellers can explore using different skills or techniques on AI customers with certain moods, buying stages, and priorities. This also allows sellers to hone their skills without relying on a human coach for every session.

Content use cases

Develop and keep content up to date – enablement content often gets shipped but doesn’t get refreshed as information changes. AI can help create and maintain content across multiple formats:

• E-learning content – onboarding, training, or general support resources
• Physical content – sales battlecards, facilitation guides, and handouts
• Video content – training or product videos  

Improve existing content – AI models improve as you feed them more information. They can help your team further refine a customized framework or identify opportunities to apply more general best practices that work well for similar products/ customers.

Live action/ front-line use cases

In-meeting coaching – AI chatbots can provide real-time feedback on factors such as tonality, quantity of questions asked, how much or how quickly you’re speaking, and whether you are using too many filler words. Instead of watching recordings, analyzing your performance, and then hoping to improve during your next call, live coaching allows you to make real-time adjustments. This improves seller self-awareness, skill development, and performance.

Note: the coaching use case is especially helpful for companies that lack the resources, capabilities, and/or funding to have dedicated coaches. AI coaches will not provide feedback with the same nuance as a human facilitator, but they offer a mental level of scale that is available to sellers at all hours of the day, including immediately before or during important client meetings. 

How can companies strike the right balance between AI automation and human touch in sales processes?

Customer-facing AI features should be additive – instead of trying to delegate tasks to an AI model, search for situations where AI can create value that wouldn’t be possible without the technology. For example:

• Live coaching – managers can’t sit in on every sales call or offer real-time advice to every seller. However, an AI chatbot like the one described above can provide tips at scale to help an entire team perform better.
• Developing unique versions of standard content – if your business could benefit from creating 100 versions of a single piece of content (e.g., tailoring certain elements of a presentation to each member of a customer segment), AI can easily do that for you. Without the technology, however, you are more likely to “make do” and utilize fewer versions that aren’t as effective.

Differentiate between low risk and high stakes activities – identify your organization’s comfort level with delegating tasks to AI. As a starting point, consider using AI to execute activities that are high volume and low risk. Any relationship building or high stakes activities should involve human involvement or ownership.

Example of AI-“friendly” activities		Example of non-AI-“friendly” activities
24/7 self-service support: • Additive – AI can offer always-on support, whereas live agents are constrained by schedule availability • High volume – common questions might have common answers that can be provided without human oversight • Low risk – AI can provide basic guidance to help resolve noncomplex issues without human oversight. If complex concerns arise during the interaction, the ticket can be escalated.		Closing a deal with a strategic account: • Non-additive – because humans can execute relationship building activities better than an AI model, delegating this type of task increases risk without offering a skills-based advantage. • Low volume – strategic partners are relatively rare, and handling them is a strategic use of time • High stakes – the consequences of mishandling an interaction with a valuable potential partner can extend for years

Where should you start? What considerations should impact your choice of use cases?

Identify the greatest area of opportunity – start with the biggest capability gap or pain point that you can’t scale without AI technology. By focusing on important issues that you can’t solve in other ways, you increase the likelihood of building a solution that is both additive and strategic for your organization.

Minimize risk by starting with internal use cases – external-facing AI applications often require more work and have more extreme consequences when critical errors emerge. Internal applications can be more forgiving (especially for organizations using AI for the first time) because they provide more opportunities to launch, iterate, and learn as you refine your model.

What do you need to have in place to get the most out of your AI implementation? 

Human-focused enablement goals should guide your AI strategy – follow the same 3-step process that would inform the use of any tool in your organization:

• Understand what you need – identify the knowledge, skills, and key milestones that contribute to your reps’ success. You should consider the entire upskilling process from onboarding through continuous learning and development later in a rep’s career. 
• Identify moments of impact – determine where intervention or support can have the greatest impact on a rep’s development journey (e.g., onboarding, preparing for a new product launch, etc.).
• Consider the potential role of AI – explore how you can build and deploy solutions that meet the requirements defined above, and decide if an AI solution is best suited to helping you meet those requirements.

Simplify training resources before your implementation – AI makes processes scalable and repeatable. If the information you feed into your model is chaotic and/or cluttered, you risk generating a massive amount of content that is poorly organized, not useful, or inaccurate.  

Tool / Model Selection

What types of off-the-shelf tools are available?

New AI tools are constantly emerging – if you plan to adopt an off-the-shelf tool in the near future, start monitoring emerging platforms that focus on content creation, learning paths, coaching, and general conversational intelligence.

Generalist platforms take an all-in-one approach – platforms like Letter.ai or Allego provide solutions for a variety of activities ranging from onboarding program development to responding to RFPs.

Specialized tools provide top-tier support for niche areas – call feedback platforms or AI-based coaching tools like Yoodli AI Roleplay can ingest key frameworks and help sales reps apply important methodologies through coaching sessions or by providing live feedback during sales calls and/or roleplayed practice opportunities to help prepare in a psychologically safe and scalable environment

Overall

How should you prepare your revenue function to adopt AI tools as their functionality improves? 

Make the shift away from the “human vs. AI mindset” – if you approach AI tools with a fear-driven mentality (e.g., Does AI threaten my career? How many jobs will AI replace?), it will be difficult to identify areas where AI can complement existing capabilities, unlock new opportunities, or drive scale.

Understand your business context to the greatest possible extent – it can be tempting to see AI tools as an inevitable solution to a multitude of problems. However, AI implementations can only succeed if you know exactly what you need them to do–and if you have the right data to train them. Don’t underestimate the prep work required to set an AI tool up for success.

What are the most important things to get right?

Have a clear set of inputs and goals – rigorously map out the frameworks you will use to train your model, the desired outputs of your AI implementation, and how those outputs should support reps across continuous stages of learning. If you don’t clearly define success, an AI tool could end up producing extensive content that doesn’t help you or your team achieve meaningful objectives.

What are common pitfalls? 

Using training as a compliance tool – AI enables you to create endless required trainings. While it can be tempting to err on the side of providing “too much” content, any required training or exercises should serve a clear business purpose and behavioral outcome.