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The trap: a flat dashboard looks like resistance

When adoption stalls, the easy story is that people are dragging their feet. They are not. What looks like resistance is almost always a rational response to a question the organisation has left unanswered.

Think about what AI does to a role. It absorbs the very work a person was hired for, promoted on and measured against. Leave their targets and incentives pointing at that same work, now that a model does it in seconds, and you have quietly asked them to make themselves redundant on paper while still hitting the old numbers. No training session resolves that. A flat dashboard isn't your people refusing to change. It is the organisation changing what its tools can do without changing what it asks of, or rewards in, the people using them.

What looks like resistance is almost always a rational response to a question no one has answered.

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What's in the full paper

Everything above is the argument in outline. The paper is where it turns operational. Inside, you'll find:

• The three forces that keep the dashboard flat, identity, incentives and leadership, and why each one survives every training programme you throw at it.
• Worked examples from Finance and Operations, following real roles across all three stances, down to how a single job title can describe two completely different jobs.
• A way to measure change instead of activity. Licences, prompts and self-reported "hours saved" look the same whether AI is transforming the business or sitting idle. The paper sets out the questions that tell the two apart, at the level of the business, the team and the individual.
• Why culture is the precondition the rest of it depends on, and the one call leadership cannot delegate to anyone else.
• How Dragonfly reads the stance and the friction beneath the usage: which teams have genuinely changed how they work, where everyday practice and company policy are pulling apart, and where the people driving your progress are being backed or quietly lost.

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Read the summary and you'll have a sharper way of talking about AI. Read the paper and you'll have a sharper way of running the business.

The companies that reach Reconfigured won't be the ones with the biggest AI budgets. They'll be the ones that built the new way of working into how work actually gets done, rewrote the roles before the market forced their hand, and backed the right people while they still had them.

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Where this sits: the three pillars

This is the second of three. Dragonfly's AI Readiness Framework stands on three pillars, and they only pay off together. Technology asks whether your tools are actually doing the work, or whether your people are quietly holding them together. People, this paper, asks what happens once those tools are in. Strategy, still to come, is where the two get aligned: where to point AI, what it should be solving, and which trade-offs to make as the ground keeps shifting from one week to the next.

They're built to be read together. If you haven't yet, start with the Technology pillar, the ground everything here is built on. Read all three and you see the whole machine, not a single gear of it.

The trap: everyone thinks they're at Level 4

Almost every leadership team we sit with believes they're already operating at an advanced level. They have a CRM. They have dashboards. They've rolled out a copilot, maybe experimented with agents. So they assume they're AI-ready.

But when we map the real flow of work, the picture changes fast. Data is still being copied between systems by hand. Decisions still live in people's heads. Automation exists, but it's brittle. A patchwork of if/then rules duct-taped across tools that were never designed to work together.

That's Level 2 and Level 3 behaviour wearing a Level 4 badge. It's also exactly where most AI projects quietly fail.

AI doesn't fix broken processes. It exposes them.

Layering intelligence on top of fragmentation doesn't transform anything. It just makes the cracks faster and more visible. The real work isn't choosing better AI. It's seeing your processes clearly enough to know which ones can actually carry intelligence, which ones need to be redesigned first, and which ones are being held together by nothing but human patience.

That diagnostic, applied to your business, is what the rest of the Technology paper delivers.

What's in the full paper

What you've just read is the framework in outline. It's the part that fits in a website summary. The paper itself is where the framework becomes a tool you can actually use, and that's the part that changes how you spend the next twelve months. The summary makes the case. The paper makes the difference. Inside, you'll find:

• The full anatomy of each of the five levels: what AI can and can't realistically do at each, why the ROI equation flips between Level 3 and Level 4, and where most companies actually sit, which is rarely where they think they sit.
• The decision framework for when Level 5 is the right call and when chasing it is a mistake. Moving every process to autonomy isn't the goal, and treating it as one is as misguided as buying tools without a strategy.
• Worked examples for outbound sales and customer support, mapping the exact stages, tools and metrics that shift as you climb the framework. 
• How Dragonfly's AI Readiness Platform turns the framework into a live benchmark of your specific stack against the best alternatives in the market, so you stop guessing and start comparing.

If you stop reading at the summary, you'll have a sharper way of talking about AI readiness. If you read the paper, you'll have a sharper way of running the business.

The next generation of companies won't win by owning more software. They'll win by evolving faster than their market, and that journey starts with brutal clarity about how your business actually runs today.

Download the full paper to see where yours stands.

King and Sabas have assembled a team of 12 with several former colleagues from Zego, Peppy and Permutive joining the founders in their new venture. The pre-seed funds will be used to develop a range of new features in support of the startup’s mission to build a comprehensive ‘Automated Solutions Architect’, further enrich Dragonfly’s data, and recruit select new hires as needed.

Hector Mason, General Partner at Episode 1, said: "Before Dragonfly, it was almost impossible to gather enough context about software tools to truly understand their capabilities, and whether they fit seamlessly within an existing stack. By solving this critical problem, made even more urgent with the rapid adoption of AI, Dragonfly has the potential to make every company on the planet operate more effectively.

Sam Bourton, co-founder and CTO at QuantumBlack, said: “I first met Sean and Sven after having spent three days with 150 CIOs and CTOs, witnessing firsthand the mounting complexity of the modern tech and data landscape. The explosion of GenAI and agentic systems has amplified the problem - leaders face a firehose of requests for new AI tools, with a thousand flowers blooming across their organisations, each demanding attention and resources. Taking stock of their existing tech stack, evaluating options, and managing security, dependencies, and integrations has never been harder.

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