"Prove, accelerate and scale": how AI supercharges the impact of internal tools

In large organizations, B2E (Business to Employees) products make or break team performance. Yet they are often poorly designed, riddled with hidden costs, and struggle with adoption. Artificial intelligence isn’t a magic bullet, but it has the potential to multiply the impact of internal tools… If used correctly. In this article, Jordan Ngata, consultant and expert in B2E products and organizational design, shares his method for strengthening the foundation of your internal tools before amplifying their impact with AI.

As a product organization consultant, I’ve spent years helping large companies transform their internal tools. At a leading sports company, I saw firsthand how solutions like PLM, CAD, ERP, WMS, HR, or CRM weigh on the daily performance of product engineers, designers, pattern makers, material managers, and store managers.

They’re the ones who imagine and develop the bikes, clothing, and equipment that end up on the shelves. Their challenge is easy to state but tough to solve: innovate quickly and meaningfully, while keeping budgets and regulatory constraints under control.

That’s why B2E products play such a crucial role. And yet, they’re often poorly designed. Everywhere I go, the same reasons come up:

• Internal users are “captive”: it’s assumed they’ll make do with whatever tool they’re given, so UX efforts are often minimal.

• Because value is diffuse and cross-functional - productivity gains, error rates, time-to-market – it’s hard to attribute benefits to a single budget line.
  
  

• Because the drive for a one-size-fits-all solution ignores the diversity of jobs and processes: each team handles different data, with specific practices, and the result is often ill-fitting tools and fragmented information.

• Because every new tool is sometimes met with suspicion or fatigue, especially given the pace of changes and lack of support.

When these tools are poorly designed, the hidden costs are real: lost productivity, shadow IT, costly mistakes, delayed launches.

AI amplifies what’s already working. Without a solid foundation, it amplifies the flaws too. First, treat your internal tools like real products (measurable business impact, clear user value, data-driven decisions). Only then should you use AI to prove, accelerate, and scale.

Here’s a practical, field-tested guide with three actionable levers to put this method into practice.

End opinion debates by measuring what matters

Without the right metrics, decisions are made on gut feeling and politics. Sponsors eventually disengage.

I rely on CASTLE (from the Nielsen Norman Group), a framework designed for B2E products, whereas HEART is more suited for B2C goals (engagement, retention). CASTLE tracks key dimensions tied to business productivity and ease of use, which can be translated into savings and risk reduction.

*CES (Customer Effort Score): effort perceived to complete an action—the lower it is, the smoother the journey.

**CSAT (Customer Satisfaction Score): satisfaction after an interaction.
CES captures ease of use; CSAT captures how it feels.

Here’s how to put this into practice to identify and prioritize issues in your business tools:

1. Choose a critical user journey (e.g., a bike product engineer validating a record in the PLM) that heavily impacts productivity or quality.
   
   

2. Shadow a small group of users (around 5) over 1–2 days. Record where they hit errors, frustrations, or excessive wait times.
   
   

3. Use micro CES surveys at each key step to quantify perceived effort and satisfaction.

4. Cross-analyze your field observations with logs, incident tickets, and, if possible, AI-driven analysis to surface the biggest bottlenecks objectively.
   
   

5. Quantify these pain points with simple indicators: time lost, error rates, CES scores, ticket volumes.

6. Produce a clear, data-backed report summarizing your findings.

This approach ends conviction-based debates and allows for fact-based prioritization.

To make the CASTLE diagnosis resonate with decision-makers, structure your findings around scope, methodology, key figures, estimated business impact, and recommendations. Framed this way, what might have been dismissed as “technical” becomes a decision-making tool for finance and business leaders, speeding up prioritization and investment.

AI in action: accelerating the analysis

On a recent project, working with UX and User Research teams, we automated the aggregation of real feedback (support tickets, interviews, workshop transcripts, PLM/CAD logs, micro CES surveys).

An AI agent indexed this data, grouped it by themes, and mapped each theme to CASTLE dimensions (e.g., “multiple re-entries” → Task Efficiency & Cognitive Load). Within hours, we had actionable one-pagers: frequency, impacted teams, cost in lost hours, prototype hypotheses.

This helped prioritize 5 issues covering 80% of pain points, convert 3 into quantified business cases, and engage design and engineering centers of excellence to launch targeted prototypes.

In short: AI didn’t decide for us—it accelerated discovery, grounded the debate in facts, and enabled faster, more defensible budget and product decisions.

Prove the value

A solid diagnosis isn’t enough. Sponsors don’t want a demo; they want proof.

Three pillars must be secured before launching a pilot: the business promise (expected benefit), a committed operational sponsor, and clear success/failure criteria. Without this alignment, no experiment will lead to a concrete decision.

Start by forming a small cross-functional team (Product, Tech, Business, Data) to steer the project with regular check-ins. Focus on clear metrics based on audience:

• Task Efficiency and error rates to convince finance

• Cognitive Load and user satisfaction to ensure long-term adoption

An AI prototype can be a good way to demonstrate tangible gains on these dimensions.

My step-by-step AI pilot method

1. Prepare the ground: Ensure access to operational sources (CAD, PLM, drives). If direct integration is too complex, start with CSV exports of key metadata. Identify a sponsor who will back the project and future budget.
   
   

2. Minimal, relevant prototyping: Deploy an integrated widget, for example in PLM, that pre-fills key fields (material, weight, reference) via lightweight indexing (RAG approach). Show the source and confidence score. Users validate or correct in one click. All edits are logged to refine the model. Goal: quickly prove time savings and reliability without heavy IT investment.
   
   

3. Test on a controlled scope: Pick 5–10 power users and 20–30 representative product sheets. Measure CASTLE baselines (time per task, CES, error rate). During the pilot, track validations, rejections, corrections, and actual time to assess adoption and reliability.
   
   

4. Present fact-based proof: Calculate hours saved and their euro value (hours × cost + avoided costs). Produce a one-pager with scope, baseline, quantified gains, success thresholds (e.g., adoption ≥30%, acceptance ≥60%, error reduction ≥40%). With this, the decision to scale, iterate, or stop becomes objective and easier.

AI in action: proving value faster

RAG (Retrieval-Augmented Generation) combines an internal search engine with a generative model to deliver contextual, sourced suggestions. In practice, it pre-fills fields or suggests actions with visible provenance and confidence scores—key for user trust.

But RAG isn’t a magic black box. It requires strict governance (data cleaning/anonymization, access control, validation logging) and a “human-in-the-loop” approach. Start small (CSV exports, 5–10 power users), show source and confidence, log every validation/correction, and iterate. That’s the pragmatic way to prove value without adding risk.

What you prove to the sponsor: measurable time savings, fewer errors, controlled risk, and the ability to stop quickly if value doesn’t materialize.

Secure field adoption

You’ve quantified the pain with CASTLE and proven value with the AI MVP. Now comes the real test: turning pilot success into lasting adoption. In design environments—where designers, engineers, and material managers share workflows—onboarding often makes or breaks adoption.

1. Engage key users early

1. Run a co-design workshop with 5–10 power users (engineers, designers, offer managers). Have them test the prototype, collect precise feedback, and adjust together.
   
   

2. Identify ambassadors among them, train them quickly, and make them go-to references for their teams.
   
   

3. Expected benefit: build a core group of engaged users who drive adoption.

2. Roll out progressively, with control

1. Start with one department or representative team.
   
   

2. Expand in waves (by team or site), iterating as you go.

3. Set up regular follow-ups (weekly or bi-weekly) through ambassadors to capture feedback, issues, and requests.
   
   

4. Expected benefit: limit risks, maximize impact, adapt continuously.

3. Train and support where it matters

1. Create short micro-trainings (2–3 min video capsules) embedded directly in the tool, targeting complex tasks identified by CASTLE.
   
   

2. Provide a 24/7 AI chatbot for instant answers without waiting for human intervention.
   
   

3. Use ambassadors for in-person support and to spread best practices quickly.
   
   

4. Expected benefit: reduce friction and usage barriers from the start.

4. Let the numbers speak regularly

• Track key metrics: time saved, errors avoided, satisfaction (CES).
  
  

• Share simple, visual monthly updates (newsletters, quick meetings, accessible dashboards) on tangible gains.

• Highlight success stories, even small ones, to build momentum.
  
  

• Expected benefit: reassure, motivate, and reinforce collective engagement.

AI in action: driving adoption

Deploy a proactive, contextual AI assistant that detects when a user is stuck (blocked records, repeated edits) and offers targeted help at the right moment.

Gamify usage with personalized feedback, badges, points, and leaderboards to boost engagement in a fun way.

Go further with predictive analytics to flag at-risk users for quick ambassador intervention, or create a continuous field lab where users and tech teams test AI evolutions together, ensuring rapid, needs-driven improvements.

Expected benefit: make the tool feel alive, adaptable, and motivating—part of daily work.

From project to project, I’ve seen that transforming B2E products and unlocking team potential isn’t just about change management. It’s a discipline—precise and demanding: measure what matters, prototype fast, tailor onboarding, and drive change rigorously. Stick to this, and usage takes root, with impact visible both in numbers and on the ground.

And the effects go far beyond operational teams: aligning Tech with real business needs secures investments, measurable gains are delivered, and opinion-driven debates are shut down. Activate these levers and you maximize your chances of success, reduce risks, and lay a solid path to scaling your AI-powered B2E solutions.

That’s where AI truly shines: accelerating and amplifying what’s already solid—not working magic. Your transformation stops being “just an IT project.” It becomes a value engine for the business, provided you treat your internal tools as real products.

Tired of investing blindly in artificial intelligence? Download our AI Product Canvas, a framework that brings together all the key questions you need to ask before starting an AI project.