Context​

 

In large technology organizations, sales teams often serve as the primary interface between customers and deeply technical engineering groups. When customers ask detailed technical questions, sellers must quickly locate accurate answers to maintain credibility and momentum.​

 

In this organization, that process had become increasingly strained. Sellers often spent days or even weeks tracking down the right engineer who might know the answer to a specific question. Success depended heavily on personal networks, favoring tenured employees and leaving newer sellers at a disadvantage. Engineers, meanwhile, were inundated with ad‑hoc requests that pulled them away from core development work.​

 

The symptoms were clear:

• Knowledge fragmented across systems and people.

• Slow, inconsistent response times.

• Outdated documentation.

• Reliance on relationships rather than repeatable systems.

• Unsustainable engineering support.​

 

The organization needed a way to deliver fast, accurate, and consistent technical information to sellers, without scaling human support indefinitely.​

The Opportunity — and the Risk​

 

A centralized Sales AI team began developing an internal AI-powered knowledge assistant using retrieval-augmented generation (RAG) to pull answers from trusted internal sources in real time.​

 

The technical vision was sound, but leaders recognized a critical risk: if sellers didn’t trust the system, or if engineers quietly bypassed it, the tool would fail — regardless of its accuracy. Adoption, not capability, would determine success.​

Approach: Designing for Adoption from the Start​

 

A change and adoption team was brought in while the tool was still in development, allowing human factors to shape the solution rather than be retrofitted after launch. The work focused on five interconnected areas.​

1. Grounding the Tool in Real Seller Pain​

Rather than assuming use cases, the team activated an existing global change champion network to gather firsthand input from sellers across regions and segments.​

 

These conversations surfaced:

• The most common and time-sensitive technical questions.

• Where sellers lost momentum with customers.

• What “good answers” looked like in practice.​

 

This input sharpened both tool design and messaging. Sellers could see their real problems reflected in the solution, which built early credibility.​

2. Establishing a Clear Narrative and Expectations​

A consistent narrative answered the questions sellers and leaders were already asking:

• Why is this being built?

• What problem does it solve?

• How will it change day-to-day work?

• What can it do — and what can’t it do?​

 

This narrative helped prevent unrealistic expectations. The assistant was positioned as a trusted first stop, not a replacement for engineering expertise in every situation.​

 

3. Building Trust Through Guardrails​

Accuracy and credibility were non‑negotiable. Working with subject-matter experts, the team established clear guardrails:

• Defined accuracy thresholds before expanding access.

• Guidelines for when human validation was required.

• Clarity on what information could be shared with customers under NDA.

• Education on how RAG systems differ from general-purpose LLMs, and why hallucination risk was mitigated.​

 

These guardrails became foundational to responsible use and helped sellers trust the answers they received.​

 

4. Addressing the Hidden Adoption Risk: Engineering Behavior​

One insight proved pivotal: although engineers were not the primary users, their behavior would heavily influence adoption.​

 

If engineers continued responding to questions directly — as they always had — sellers would have little incentive to rely on the new system. The team worked with engineering leaders to shift expectations. Engineers were encouraged to redirect sellers to the AI assistant and reinforce its validity, helping establish it as the default path rather than an optional experiment.​

 

Adoption was treated as a system, not a single user group.​

 

5. Enabling Confidence Through Phased Rollout and Support​

Rather than launching broadly, the team advocated for a phased rollout, starting with data-center sellers before expanding into additional segments. This approach allowed the organization to:​

• Build trust incrementally.

• Improve accuracy and coverage.

• Demonstrate early wins.

• Incorporate feedback before scaling.​

 

Enablement supported this rollout through a coordinated ecosystem:

• Intranet content and short videos.

• Executive communications.

• Live demos and office hours.

• On-demand training and usage guidelines.

• Ongoing feedback loops through change champions.​

 

The goal was confidence, not speed.​

 

Results​

 

Within six months of launch:

• The user base grew from a few hundred early testers to roughly 5,000 global users.

• More than 90% of seller queries were resolved through the assistant.

• The organization realized an estimated $4M in annual savings.

• Sellers reported finding answers in minutes instead of days or weeks.

• Onboarding time for new sellers dropped significantly.

• Engineering teams reclaimed time for core work.​

 

The success of the rollout also established a blueprint for a broader, company-wide AI platform, enabling additional use cases to scale responsibly. As one seller put it: “This used to take me the better part of a week. Now I get answers in real time while I’m still in front of the customer.”​

What This Case Demonstrates​

 

This case illustrates patterns seen in successful AI initiatives:

• Technology creates potential, but adoption creates value.

• Trust must be designed, not assumed.

• Guardrails enable confidence, not constraint.

• Influence extends beyond the primary user.

• Early human-centered design prevents downstream resistance.​

 

The AI assistant succeeded not because it was the most sophisticated tool, but because it was introduced in a way that aligned incentives, behaviors, and expectations across the system.​

How I Help Leaders Do Similar Work​

 

This case reflects the kind of human-centered AI adoption work I support in large B2B organizations — especially where sales, product, and engineering intersect.​

 

In my consulting practice, I help leadership teams:

• Design AI use cases and rollouts grounded in real seller and customer pain.

• Build narratives, guardrails, and enablement that make AI tools trustworthy and usable in the field.

• Address “hidden” adoption drivers, such as engineering and leadership behavior, not just frontline training.

• Use early successes to create reusable blueprints for broader AI platforms and additional use cases.​

 

If you’re building or rolling out AI assistants for sellers or other knowledge-intensive roles, we can adapt this kind of approach to your context — so adoption, trust, and behavior change are built in from the start.

Return to case studies page →