The One Competency That Actually Matters

The fusion team concept promises to solve enterprise software’s biggest execution problem: the handoff tax between business intent and technical delivery. Instead of requirements traveling through multiple departments, fusion teams own outcomes end-to-end.

Yet for every fusion team that actually delivers, there are five that just coordinate better. The difference isn’t structure or process—it’s whether someone on the team can translate business outcomes into AI-powered execution.

This translation competency isn’t optional. It’s what separates real fusion teams from expensive alignment committees.

The Trap: Fusion Teams Without Translation

Most organizations assume that business and technical people can meet in the middle. In practice, they rarely do.

Business leaders describe outcomes in vague terms: “We need faster onboarding.”

Engineers ask for precision: “What’s the data source? Which API should trigger? What counts as complete?”

Without translation, you end up with coordination meetings instead of momentum. AI features get built, but adoption lags. Or business requirements pile up in endless backlogs, waiting for engineers to make sense of them.

This is the most common failure mode of fusion teams: they lack someone with the competency to bridge business context, workflows, and AI system design.

The New Play: Make Translation a Discipline

Instead of hoping business owners and engineers will “figure it out,” ensure someone owns the AI translation competency explicitly.

Think of it as part product manager, part workflow designer, part systems architect. The person with this competency:

• Grounds every discussion in outcomes, not functions.
• Understands APIs as the “railroads” of invisible AI and decides where those railroads should run.
• Reduces the cost of change management by designing with how people actually work.
• Keeps AI explainable enough to build trust, without drowning teams in technical detail.

Leadership’s job isn’t to provide the translation competency themselves. Leadership designs the container and trust loops in which this competency can operate effectively.

How Translation Competency Reduces the Change Management Tax

Organizations typically need a 5:1 ratio of change management investment to technology spending. Having someone with strong AI translation competency can cut this burden by 60-70% through three specific mechanisms:

Workflow-Native Design: Instead of forcing users to adopt new interfaces, someone with translation competency embeds AI into existing processes. When onboarding automation works through the same CRM screens teams already use, adoption feels like enhancement, not disruption.

Context-Aware Implementation: Strong translators understand how people actually work versus how processes are documented. They design AI that respects natural work patterns, reducing the cognitive load of behavioral change.

Progressive Trust Building: Rather than asking teams to trust AI completely from day one, effective translation creates validation loops where humans can verify AI decisions until confidence builds naturally.

The math is compelling: every hour spent on translation design saves 5-10 hours of adoption coaching, resistance management, and system rework.

What Translation Competency Looks Like

Some teams solve this with AI-literate PMs who understand both user needs and technical constraints. Others use business-fluent architects who can design systems with user adoption in mind. The key difference from traditional product management: while PMs focus on what features to build and why, this competency focuses on how to embed intelligence into existing workflows seamlessly.

Core Skills:

• Fluent in APIs and workflows - Knows how systems connect and where bottlenecks occur
• Strong process designer - Maps current reality and simplifies strategically
• Asks the right questions - “What decision are we trying to accelerate?” vs “What feature do you want?”
• Bilingual in culture - Translates between engineering precision and business outcomes
• Systems thinker - Sees how automation affects trust, guardrails, and human workflows

Key Responsibilities:

• Map workflows and identify AI integration points
• Define data contracts so engineers don’t guess at context
• Translate ambiguous business goals into executable system rules
• Build trust loops with transparency, validation, and learning mechanisms
• Coach team literacy without hoarding expertise

Case Study: Customer Onboarding in Practice

Consider a product where new customers took 30+ days to go live. Teams pointed fingers while friction persisted.

After embedding translation competency: Workflows were mapped end-to-end, duplicate processes eliminated, APIs automated provisioning behind the scenes, and guardrails prevented 48+ hour stalls.

Result: Onboarding dropped from 30 days to under 10. Customer satisfaction rose because the process matched how people actually worked, not how silos were structured.

Measuring Translation Effectiveness

You can measure the impact of strong AI translation competency:

• Reduction in signal-to-response time → Are signals acted on in hours, not days?
• Increase in decision velocity → Are key decisions happening faster without escalation?
• Adoption of AI-powered workflows → Are people using the AI, or working around it?
• Drop in shadow work → Has manual tracking and duplicate entry decreased?

Scaling Considerations

As organizations grow, the AI translation competency scales through:

• One person with translation competency per major workflow stream
• Translation competency evolving into AI Product Management at scale
• Strong translators coaching teams to build their own literacy

The end state isn’t one person doing all translation. It’s a network of people with translation competency embedded in teams, with shared standards and trust loops.

Momentum Close: Translation Competency and the 3Ps

Fusion teams only create advantage when business and technical execution stay tightly aligned. Without strong AI translation competency, they default to stalemate: impressive roadmaps, frustrated engineers, and adoption curves that never lift.

AI translation competency is the missing glue. It doesn’t just reduce friction; it creates trust, velocity, and adoption. And it embodies the 3Ps of Career Fulfillment:

Passion → People with translation competency thrive on solving meaningful workflow problems, not just building features.

Problem → They clarify the real business problems behind vague requirements, making AI useful instead of ornamental.

People → They connect engineers, operators, and business leaders into one cohesive unit, aligning incentives around shared outcomes.

If your fusion team doesn’t have someone with strong translation competency, you don’t have a team. You have a stalemate.