Scaling Content 12x Without Breaking Quality

The challenge

When I joined Tekion, the customer-facing eLearning library had 50 modules. Mostly 15-minute recorded screen shares with narration, created ad-hoc as products launched. The company was growing fast, rolling out new platforms monthly, and the support team was fielding questions that training should have prevented.

Leadership said: scale this. Fast. The instinct was to hire more instructional designers. But I looked at the existing 50 modules and saw a deeper problem: no production system. Each module built differently by whoever had bandwidth. No templates. No style guide. No quality standards. Adding more people to a broken system would create more inconsistency faster.

The approach

I treated this as an operations problem, not a content problem. Three decisions drove everything:

Standardize formats first. Before creating content, I established templates, style guides, and quality standards. Every module followed the same structure, making creation and maintenance predictable.

Build the team to match the workflow. I hired specialists for each production stage rather than generalists who did everything. Writers, designers, reviewers, and LMS administrators each owned their step.

Integrate AI where it reduces friction. I spent the first three weeks mapping the existing workflow and found the average cycle time was 9 weeks — most of that was waiting: for SME review, stakeholder approval, LMS upload. Actual content creation was maybe 15% of the total cycle.

The execution

When AI tools became viable in mid-2024, my team was nervous. They assumed AI meant job cuts. I learned quickly that the messaging matters more than the technology. "AI handles the parts of your job you don't love, so you can focus on the parts that require your expertise" changed the dynamic. But I had to prove it, not just say it.

So I sat in check-ins with the teams that were falling behind. Made them open ChatGPT and Claude side by side. We experimented in real time. Some prompts worked. Others gave us absolute nonsense. We kept going until we found what was useful. That wasn't a strategy. It was instinct. I learn by doing, and I model that.

We also couldn't skip governance. During the pilot with two team members, we caught three instances where AI pulled language from competitor materials. If we'd rolled out without guardrails, those would have been compliance violations.

The three-phase rollout:

• Pilot (2 months): First drafts, metadata generation, translation assistance. 2 team members, 3 use cases.
• Expand (3 months): Full team adoption with documented workflows and custom prompt libraries for each content type.
• Govern (ongoing): IP compliance framework, audit trail, regular review. Every AI-assisted deliverable through the same quality gates as human-only work.

The results

The insight

Scaling content is a systems problem. The temptation is to throw more people at it, but without standardized workflows and quality gates, more people just means more inconsistency faster. The AI integration worked because we treated it as a workflow optimization, not a technology experiment. And because we learned it together rather than mandating it from above.

I now approach every scaling challenge by asking: what's the system, and where are the bottlenecks? The answer is almost never "we need more people." It's usually "we need better workflows, clearer standards, and the right technology at the right integration points."