What is Task Engineering?

A new discipline for organizations serious about AI transformation.

Every organization is made up of thousands of tasks. Not processes. Not workflows. Tasks—the atomic units of work that human beings perform every day to create value.

When someone asks “how do we adopt AI?” they’re really asking: which of those thousands of tasks should change, and how?

Nobody has a good answer. That’s the problem.

The Gap Nobody’s Talking About

Companies have invested billions in AI. ChatGPT has over 800 million weekly users. Claude has captured 32% of the enterprise market. The technology works.

But the adoption doesn’t.

Seventy-eight percent of organizations have deployed AI. Only thirty-one percent of workers have received any training on how to use it. Seventy-four percent of companies can’t move AI past the pilot phase. And just twenty-three percent can tie AI initiatives to measurable business outcomes..

The workers getting real value from AI—frontier users—engage with AI tools at six times the rate of their peers. Same tools. Same capabilities. Six times the usage.

This isn’t a technology problem. It’s a visibility problem. Organizations can’t transform what they can’t see.

What Task Engineering Actually Is

Task Engineering is a new methodology. It’s the discipline of analyzing how employees perform units of work today, and reimagining how AI and humans can work together for higher efficiency, higher quality, and entirely new value creation.

Here’s what typically happens when a company decides to “adopt AI”: They buy licenses. They roll out tools. They send people to training. And then... nothing much changes. The tools sit unused. The training doesn’t stick. Leadership asks where the ROI is, and no one has a good answer.

The problem isn’t the tools. The problem isn’t the people. The problem is that no one has looked at the actual work—the tasks—and figured out which ones should change, how they should change, and what “better” actually looks like.

Task Engineering goes upstream of everything else. Before you write a prompt, before you build a workflow, before you train anyone on anything—you need to understand the tasks. What are people actually doing? Where does AI genuinely help? Where should humans stay in control? What new things become possible that weren’t
possible before?

Why This Sits Upstream of Everything

Think about prompt engineering. It’s the discipline of writing effective prompts to get good outputs from AI. But prompt engineering assumes you already know what you want to automate. Task Engineering answers that question first.

Think about AI training platforms—LinkedIn Learning, Coursera, internal programs. They teach generic skills. “Here’s how to use ChatGPT.” That’s useful, but it doesn’t tell anyone how to apply AI to their specific tasks, with their specific tools, in their specific context. Task Engineering identifies what people need to learn, for which specific tasks, in what order of priority.

Think about process mining. Tools like Celonis analyze system workflows—how data moves through enterprise software. But that’s system-level flows, not human work. Task Engineering analyzes what people actually do, not what the systems record.
Because Task Engineering sits upstream, it doesn’t compete with these approaches. It determines the “what” before others handle the “how.”

What We’re Seeing in the Market

Over the past several months, we’ve been working with some of the largest asset management firms, private equity groups, and AI-focused consulting partners. Here’s what we’re observing.

Deals are falling through because of AI readiness.

We’re seeing large transactions stall or collapse because the portfolio company doesn’t have AI-native capabilities. Staff isn’t trained. Products haven’t evolved. When buyers conduct due diligence, they’re finding organizations that made AI investments but can’t demonstrate adoption or results.

AI readiness is becoming a critical factor in exit preparation—as important as financial performance or operational efficiency. PE firms preparing portfolio companies for market are realizing they need to show more than “we bought Claude licenses.” They need to show transformation.

Incumbents are being disrupted faster than portfolios can adapt.

Harvey AI came out of nowhere and became a multi-billion dollar unicorn, directly challenging every legal tech company in every PE portfolio. Yesterday, OpenAI acquired Torch for $100 million and announced ChatGPT Health—a dedicated healthcare AI that unifies lab results, medications, and medical records. Over 230 million people already use ChatGPT for health questions weekly. If you’re a healthcare portfolio company, that’s not a future threat. That’s today.

These aren’t hypothetical disruptions—they’re well-funded, AI-native challengers moving fast. They’re building products from the ground up around AI capabilities. They don’t have legacy workflows to transform. They don’t have thousands of employees to retrain. They’re just... faster.

Portfolio companies need to move at a pace that traditional transformation approaches can’t deliver.

Generic training isn’t solving the problem.

We’ve talked to operating partners who’ve rolled out enterprise AI tools, funded training programs, brought in consultants. Utilization stays flat. Nothing changes. The challenge isn’t motivation—people want to use AI. The challenge is knowing which tasks to transform first, and how to transform them in context.

That’s the gap Task Engineering fills. We analyze the organization—every role, every task—and show exactly where to focus. We also analyze the product itself: the market, competitors, hiring patterns, news reports. Our Ignite platform identifies the top opportunities where AI can be embedded directly into products to create new experiences or enhance existing ones.

The firms getting this right are moving fast.

We’re working with some of the largest PE firms in the world, alongside partners in AI tool automation and consulting. The ones seeing results aren’t waiting for perfect conditions. They’re getting visibility into their portfolios now, prioritizing the highest impact transformations, and building the proof points they’ll need for their next transaction.

The Moment We’re In

Yesterday, Anthropic launched Claude Cowork—and it changes everything about how we should think about AI at work.

Cowork isn’t a chatbot. It’s an AI agent that can access your files, make a plan, and execute tasks autonomously. Give it a folder of receipts, and it builds your expense report. Point it at scattered notes, and it drafts your presentation. It works with “more agency than a chatbot,” Anthropic says—”much more like leaving messages for a coworker.”

This is the inflection point we’ve been anticipating. AI is no longer just answering questions. It’s doing work.

But here’s what Anthropic can’t tell you: which tasks should your AI coworker handle

They’ve built an incredibly powerful tool. They’ve made it accessible to non-developers. They’ve created an architecture that can execute complex, multi-step workflows. What they haven’t done—what they can’t do—is analyze your organization and tell you where to deploy it.

That’s Task Engineering. We sit upstream of tools like Cowork. We determine the “what” before anyone builds the “how.”

Why Task Engineering Unlocks These Tools

The same principle applies across the entire AI agent ecosystem.

Consider the workflow automation platforms: CrewAI, n8n, Make, Zapier. These tools are extraordinarily capable. They can orchestrate complex multi-step processes, connect dozens of applications, and automate workflows that previously required manual intervention at every step.

But every one of them faces the same adoption challenge: organizations don’t know which workflows to automate first. They don’t know which tasks will generate the highest ROI. They don’t have visibility into where automation creates value versus where it creates risk.

Task Engineering solves that problem. We analyze every task in your organization and identify exactly where these tools should be deployed. We don’t compete with Cowork or CrewAI or n8n—we make them dramatically more valuable by ensuring they’re applied to the right problems.

This is why we’re actively building partnerships across the AI ecosystem. We’re working with consulting firms focused on AI transformation. We’re collaborating with workflow automation platforms. And we’re open to partnering with Anthropic, OpenAI, and anyone else building tools that transform how work gets done.

If you’re building in this space, we’d love to talk: partner@taskeng.ai

The organizations that win won’t just have the best AI tools. They’ll have clarity about which tasks those tools should handle. That’s what Task Engineering provides.

What Comes Next

We built Task Engineering to solve this problem. Our platform analyzes every task in an organization— thousands of them, not a sample—and identifies exactly where AI creates value. We do it in days, not months, using an 80-agent pipeline that operates at a scale traditional consulting cannot match.

Then we prove it worked. Audit-ready measurement. Before-and-after snapshots. Real transformation, not a deck.

If your organization has invested in AI and you’re still asking “where do we start?”—that’s the question Task Engineering answers.

We don’t add complexity. We reveal clarity

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