Beyond the Hype: Part 2. Productivity, Not Parlour Tricks

TL;DR

Most GenAI use still feels like entertainment – impressive demos that don't stick. The deeper problem? We're measuring the wrong things. Real advantage comes from mastering a new recipe for value: building repeatable processes that embed AI into your actual work, not chasing one-off outputs.

The Entertainment Trap and the Productivity Mirage

Picture this: your colleague shows you an amazing ChatGPT output. "Look what it wrote in 30 seconds!" Impressive. You try it yourself. The results are... a roll of the dice. Sometimes brilliant, sometimes rubbish, always unpredictable.

Sound familiar?

We call this the entertainment trap; where GenAI feels magical in the moment but frustrating in practice. But there's a deeper problem lurking underneath that most people haven't noticed yet.

Because generating content is now so easy, we feel incredibly productive. We're busy prompting, editing, refining. Yet at the end of the week, are we actually delivering more verified, high-quality work?

This gap between feeling busy and being effective is what we call the Productivity Mirage. A 2025 study of experienced developers found they felt 20% more productive using AI tools while actually taking 19% longer to complete tasks. The volume of AI-generated output creates an illusion of progress while the net time to deliver correct, finished work increases.

Meanwhile, a smaller group of professionals has quietly figured out something different. They're not chasing flashy demos or getting caught in the productivity illusion. They're building repeatable processes that compound over time.

The difference isn't the tool they use. It's how they think about the work itself.

The New Recipe for Value

When cognitive effort becomes abundant and cheap, the old rules break. The traditional equation—more time plus more effort equals more value—no longer works.

The professionals who are building real advantage have discovered that value comes from a combination of judgement, AI leverage, and quality inputs:

$$ Value = (Judgement × AI Leverage) × Quality of Input $$

Let me break that down:

• Judgement: The wisdom to know what problem to solve, which questions to ask, and why it matters

• AI Leverage: The skill of using AI efficiently to handle the heavy lifting

• Quality of Input: The unique context, expertise, and standards you bring to guide the AI

  
  

Most people focus only on Leverage—getting faster at using the tools. The real advantage comes from mastering all three variables.

What This Looks Like in Practice

Here is what happens when teams apply this new recipe, drawn from recent enterprise work where professionals moved beyond sporadic assistance to embedded capability.

Evaluation Without the Workshop Marathon

A mid-tier organisation faced complex technology procurement. Traditional approach: months of stakeholder workshops, inconsistent evaluation criteria, analysis paralysis.

Instead of ad-hoc assessment, the team built a reusable evaluation process. This worked because they applied the new recipe systematically:

Judgement: They spent time upfront defining what good evaluation actually looked like—which criteria mattered, what questions revealed vendor capabilities, how to assess risk consistently.

AI Leverage: They created prompt templates that could systematically analyse proposals, generate targeted questions, and produce comparable assessments.

Quality of Input: They fed the AI their institutional knowledge about past procurements, vendor relationships, and technical constraints.

The transformation: What previously required months of coordination became weeks of analysis. More importantly, they now owned a reusable asset—the same process could assess different vendors, different technologies, different procurement contexts.

Documentation That Actually Gets Used

An enterprise client needed comprehensive documentation of their existing IT systems to support a major migration. Critical knowledge was scattered across teams, existing documentation was complex and sometimes inconsistent, and the timeline was aggressive.

Rather than traditional discovery workshops, the team developed a repeatable documentation process using GenAI as a thinking partner:

Judgement: They mapped out what needed documenting and in what sequence, based on their understanding of how the migration would actually work.

Quality of Input: They started with their high-level context of the system architecture, then progressively fed more specific technical details to the AI.

AI Leverage: They used structured prompt templates to translate technical complexity into stakeholder-appropriate views, systematically drilling down from overview to detail.

The key insight: The process wasn't faster because AI writes better diagrams. It was faster because human Judgement clarified what needed documenting, and high Quality of Input ensured the AI had the right context to work with.

Content That Scales Without Losing Voice

A strategic consultancy needed to scale high-quality content development across multiple client engagements while maintaining consistent brand voice and strategic positioning.

Moving beyond ad-hoc copywriting, the team developed repeatable content processes:

Judgement: They codified what made their content effective—brand voice patterns, audience targeting frameworks, quality standards.

Quality of Input: They systematically captured their insights about different audiences, competitive positioning, and strategic messaging.

AI Leverage: They built prompt templates that could generate first drafts while maintaining their distinctive voice and strategic focus.

The productivity transformation: First-draft development time was cut in half while improving consistency. More importantly, junior team members could produce senior-quality content by following the established process.

As one team member observed: "We stopped caring about which specific AI tool we were using. We'd built thinking frameworks that transferred across platforms."

Two Paths Emerge

When I look at how different professionals are responding to AI, I see two distinct paths emerging:

Path 1: The Operator gets faster at doing the same tasks. Uses AI for one-off assistance, chasing speed and volume. Lives in the productivity illusion, celebrating output over outcomes. This path leads to becoming a highly efficient but easily replaceable commodity.

Path 2: The Architect steps back and designs better ways of working. Builds repeatable processes where AI acts as a reliable collaborator, focusing on the new recipe for value—not just leverage, but judgement and input quality too.

The teams building systematic processes don't just get faster. They become capable of work they couldn't do before because they're deliberately orchestrating all three variables of the new value equation.

Your Path from Experiments to Systems

How do you become an architect? You start by developing your Judgement and Quality of Input before you try to scale your Leverage.

Step 1: Identify Your Repetitive Thinking Work

Look for work that involves:

• Consistent analysis patterns (evaluation, assessment, documentation)

• Structured communication needs (reports, presentations, stakeholder updates)

• Iterative refinement processes (strategy development, planning, problem-solving)

Step 2: Document How You Actually Think (Your Judgement)

Before automating, capture your expertise:

• What questions do you ask? (Document your analytical approach)

• What sequence do you follow? (Map your workflow steps)

• What criteria do you apply? (Codify your decision-making frameworks)

Step 3: Build Reusable Prompt Systems (Codifying Your Quality of Input)

Create templates that capture:

• Context setting (role, constraints, objectives)

• Process guidance (analytical sequence, evaluation criteria)

• Output specification (format, depth, stakeholder targeting)

Step 4: Test and Refine

For each use case:

• Document what works (effective prompt patterns, useful outputs)

• Identify failure modes (where the system breaks down, quality inconsistencies)

• Iterate the methodology (improve prompts, refine process, strengthen frameworks)

Step 5: Scale Through Teaching

The ultimate test:

• Can you teach the methodology to others?

• Does it work across different contexts?

• Do results improve with practice rather than degrade?

The Real Productivity Question

The most effective professionals aren't asking "What can AI do for me?" They're asking "How can I build better processes for applying my judgement and expertise, with AI providing the leverage?"

This shift changes everything. You stop chasing clever prompts and start building durable thinking processes. You stop seeking impressive outputs and start developing reliable capabilities.

Most importantly, you stop treating GenAI as entertainment and start treating it as the foundation for systematic advantage.

Looking Ahead → Part 3: The Missing Skills

Building these processes sounds straightforward in theory. In practice, most teams struggle with specific capability gaps that have nothing to do with prompt engineering or tool mastery.

In Part 3, we'll explore these missing skills—the meta-capabilities that separate systematic thinkers from sporadic experimenters. Because building real GenAI capability isn't just about better workflows.

It's about thinking differently.

#GenAI #BeyondTheHype #SystematicThinking #ProductivityNotTricks #LeverageNotLayoffs

How are you moving from ad-hoc assistance to systematic integration? What thinking processes are you embedding AI into—and what's blocking the transition from impressive demos to compound advantage?

Ready to build systematic capability instead of chasing the next impressive output? This is where real productivity lives.