PlusNarrative
A field guide to how businesses actually adopt AI today, and what to do about it.
plusnarrative.com · AI Adoption Archetype
AI Adoption Archetype
Officially absent, unofficially exposed
At a glance
Officially, nothing's happening. Unofficially, it almost certainly already is, on someone's personal account, with no policy, no visibility and nobody accountable if it goes wrong. This is the one archetype where doing nothing is the risky option.
On paper, the Bystander hasn't really started with AI. In practice, it almost certainly has, just without anyone's knowledge, policy or protection. This is the archetype with the least to show for the most exposure.
Ask leadership about AI adoption here and you'll likely hear that it hasn't really started. Ask an average employee whether they've ever pasted something into ChatGPT and the answer is usually yes. Both things are true at once, and the distance between them is where the risk lives.
Nobody set out to create an ungoverned situation. It happened by default, because nobody was assigned to own it. The absence of a policy isn't neutral, it's a decision by omission, and it has exactly the same consequences as a bad policy would.
Of all six archetypes, this is the one where the first move costs the least and buys back the most risk. A baseline policy and a named owner close most of the gap in weeks, before any of it has had the chance to compound.
By the numbers
Only 31% of organisations have a formal, comprehensive AI policy in place.
34.8% of all corporate data employees put into AI tools is now classified as sensitive, more than triple the rate two years ago.
Cyberhaven, 2025 AI Adoption and Risk Report (7 million workers, April 2025)
Individual employees are quietly experimenting on their own initiative, usually with free tools and personal accounts, with no idea whether that's against any rule because no rule has ever been written.
Leadership genuinely believes AI is not relevant here yet, which is usually the last place shadow use gets discovered, often through a client question or a data incident rather than an internal review.
There's no vendor relationship to speak of, officially, which means there's also no one checking what free-tier terms and conditions employees have already agreed to on the business's behalf.
If there is no official AI initiative or budget line anywhere in the business, but you would not bet against an employee having pasted something into ChatGPT this week, you are almost certainly a Bystander.
It combines the least official adoption with real, unmanaged exposure. Employees are already experimenting on personal accounts with no policy and nobody accountable, the same risk a Sprinter carries without any of a Sprinter's adoption to show for it.
A policy baseline and a single named owner for AI risk, both achievable within a month. Of all six archetypes, this is the one where the smallest effort buys back the most risk.
Recommended engagement
AI Readiness & Risk Baseline: an urgent, lightweight engagement to close the gap between official policy and actual use.
plusnarrative.com · AI Adoption Archetype
AI Adoption Archetype
Disciplined, cautious, standing still
At a glance
Every instinct here is sound: careful data handling, real risk awareness, no reckless experiments. What's missing isn't caution, it's a first move. Standing still has a cost too, it's just quieter than the ones you're used to watching for.
The Watchtower has done the responsible thing: waited, thought carefully, avoided reckless experiments. The instincts are sound. The risk now isn't a bad decision. It's the compounding cost of no decision.
The instincts that would make a governance rollout easy elsewhere are already present here. Data handling is taken seriously. Vendor terms get read properly. What's missing isn't capability, it's a use case anyone has actually agreed to try.
Every AI risk framework accounts for the cost of moving too fast. Almost none of them account for the cost of not moving at all, the hours and money competitors are already saving while a cautious business keeps deliberating. That cost is real, it is just harder to see because it never shows up as an incident.
Every mistake other archetypes are learning from in public is already visible from here. That's a genuine head start, provided it gets used before the gap becomes too large to close in one move.
Teams aren't asking for AI tools yet, mostly because nobody's shown them a sanctioned way to use one. That's a low bar to clear, not a cultural resistance to overcome.
Leadership has usually discussed AI risk at least once, in the abstract, without yet turning that conversation into a decision. The appetite for a first move exists; what is missing is a deadline.
Existing vendor and procurement standards are already strong enough to vet an AI tool properly, which means the usual blocker, nobody trusts the review process, doesn't apply here the way it does almost everywhere else.
If leadership has discussed AI risk seriously but has not yet approved a single pilot, and your data-handling and vendor-review standards are already solid, you are almost certainly a Watchtower.
The caution itself is not the problem. The absence of a deadline is. Every risk framework accounts for moving too fast; almost none of them account for the cost of not moving at all.
A short strategy sprint to find two or three low-risk, high-confidence pilots that fit inside the risk appetite you already have. Existing governance strength makes this an easier internal sell than it would be for almost any other archetype.
Recommended engagement
AI Strategy Sprint & Pilot Design: a low-risk entry point that turns strong governance into a confident first move.
plusnarrative.com · AI Adoption Archetype
AI Adoption Archetype
Real pockets of use, no map
At a glance
A few teams have quietly figured out how to make AI work here. The rest of the business doesn't know it yet, and neither does anyone responsible for what data those teams are sending where.
The Tinkerer has proof that AI works here. A few teams have quietly figured it out. The question is whether that good practice spreads on purpose, or the risk spreads first.
Somewhere in the business, a team has already found a genuinely good AI use case and made it part of how they work. Nobody planned that. It happened because someone was curious and nobody stopped them. That's proof the value is real, it just hasn't been given anywhere to go.
Ad hoc pockets of good practice are easy to miss precisely because they're pockets. There's no shared inventory of what's in use, so the business can't tell the difference between a well-run experiment and an unmanaged one just by looking.
Every month without a baseline policy makes the eventual retrofit harder, because more tools, more habits and more informal norms get set in the meantime. The fix is cheap now and gets more expensive the longer good practice is left to spread on its own.
By the numbers
Only 31% of organisations have a formal, comprehensive AI policy in place.
The team using AI well rarely gets asked to teach anyone else, so the business ends up with one pocket of expertise and several teams reinventing a worse version of it independently.
Leadership usually hears about the successful pocket anecdotally, in a meeting or a hallway conversation, rather than through anything that resembles a report. There's no structure yet for good practice to travel upward.
Tool choices get made team by team, often without anyone comparing notes, so the business ends up quietly paying for three overlapping subscriptions that do roughly the same job.
If one or two teams have genuinely figured out AI on their own, while most of the business has not started or uses something completely different, you are almost certainly a Tinkerer.
A simple, company-wide policy baseline, before the next team adopts a tool on its own initiative. It is the cheapest fix available at this stage and gets more expensive to retrofit every month it is delayed.
A Sprinter has AI embedded everywhere with no governance. A Tinkerer has it working well in a couple of pockets with no governance and no adoption everywhere else. Same missing policy, much smaller footprint to fix.
Recommended engagement
AI Policy Baseline & Tool Rationalisation: a focused engagement to get ahead of governance before ad hoc use scales further.
plusnarrative.com · AI Adoption Archetype
AI Adoption Archetype
Careful progress, solid foundations
At a glance
Every foundation is in place: policy, review, spend discipline. What's missing is pace. The caution that's kept you safe is now the thing standing between you and the value everyone else is chasing.
The Builder does the unglamorous work properly: clear policy, deliberate rollout, no shortcuts. The cost of that discipline is pace, and that's a fixable problem, not a character flaw.
Nothing here was rushed. Every AI tool in use went through a real review before anyone touched it, which is precisely what most companies wish they had done before they scaled. That discipline is rare, and it is the reason nothing has gone wrong yet.
The same review process that protects you from a Sprinter's mistakes also means teams outside the initial rollout are still waiting their turn. At some point, careful stops being a virtue and starts being an excuse, and it's hard to tell from the inside exactly when that happens.
Nobody decided to move slowly. It's just what careful default behaviour produces when nobody sets a target for how fast careful should be. That's fixable with a deadline and a shortlist, not a culture change.
Teams outside the first wave know exactly what has been approved and what has not, and mostly respect the process, even while quietly wishing it moved faster.
Leadership trusts the process, which is valuable, but that trust hasn't yet been converted into a target. Nobody's asked how much faster careful could be.
Every vendor relationship has been through the same proper review as the internal use cases, so there is no separate blind spot for external tools the way there is in less disciplined archetypes.
If every AI tool in use went through a real review before anyone touched it, spend is modest and tracked, and teams outside the first rollout are still waiting their turn, you are almost certainly a Builder.
Pace, not capability. The same review process that keeps a Builder safe also means good use cases wait longer than they need to. A short strategy sprint with a deadline usually fixes this.
Yes, within the same governance model that already works. The fix is a measurable adoption target and a shortlist of use cases, not a change in how carefully new tools get reviewed.
Recommended engagement
AI Strategy Sprint: a short engagement to convert strong foundations into a prioritised, time-boxed roadmap.
plusnarrative.com · AI Adoption Archetype
AI Adoption Archetype
Moving fast, eyes off the road
At a glance
You've won the hardest battle already: your business actually uses AI every day, without anyone having to be talked into it. Now you need to catch up on the boring bit: who's watching what it costs, what it touches and what it gets wrong.
Of the six AI Adoption Archetypes, the Sprinter is the one moving fastest, and the one most likely to get hurt doing it. If your organisation recognises itself here, the good news is the hard part (getting people to actually use AI) is already done.
Most companies spend a year trying to get their teams to actually adopt AI. You skipped that step entirely. People reach for AI tools the way they'd reach for a spreadsheet, without asking permission first, which is exactly the behaviour every AI-enablement consultant is trying to create everywhere else. The problem is that momentum arrived faster than the guardrails did.
Ask five people which AI tools the business pays for and you'll get five different answers, plus at least one tool nobody remembers signing up for. That's not a hypothetical, it's the default state for a fast-adopting, ungoverned organisation, and it's exactly the condition that turns into a data-leak headline or a surprise invoice.
Governance is a much easier problem to solve than adoption. You can hire your way out of ungoverned enthusiasm with a policy, a dashboard and a review process, in weeks. Getting a reluctant organisation to actually use AI, on the other hand, can take years. You're one engagement away from being a Pathfinder.
By the numbers
34.8% of all corporate data employees put into AI tools is now classified as sensitive, more than triple the rate two years ago.
Cyberhaven, 2025 AI Adoption and Risk Report (7 million workers, April 2025)
Individual teams have quietly become expert in tools nobody officially rolled out. That's a genuine asset, but it also means expertise is siloed. The person who figured out the best prompt pattern for contract review has no formal way to share it with the person doing the same job in another department.
Leadership often finds out about a new AI habit after it's already been running for months, usually when a cost report lands or a client asks a pointed question about data handling. The business is ahead of its own reporting.
Shadow subscriptions to AI tools rarely get the same procurement scrutiny as a proper software purchase, so vendor risk, data-processing terms and renewal costs go unmanaged. It's the same discipline gap as the internal one, just pointed outward.
If AI is genuinely part of daily work across multiple teams, and nobody could give you a clear answer on what data leaves the business or what it is all costing, you are almost certainly a Sprinter. The free assessment confirms it in about 3 minutes.
A security and data-handling audit, run in weeks rather than months. Governance is a much faster problem to solve than adoption, and adoption is the part you have already solved.
No. Real, daily AI use across the business is the hardest outcome for most companies to achieve, and you already have it. The risk is neglect, not the adoption itself, and neglect is fixable.
Recommended engagement
AI Governance & Security Retrofit: a fast engagement to put guardrails under adoption that has already outrun its controls.
plusnarrative.com · AI Adoption Archetype
AI Adoption Archetype
Scaled, safe and ahead of the pack
At a glance
AI is embedded and it's genuinely governed. That combination is rarer than it should be. The risk from here isn't a mistake, it's standing still at the top while the ground keeps moving underneath you.
The Pathfinder is the rarest of the six archetypes: an organisation that has scaled AI adoption without losing control of it. If this sounds like you, the challenge isn't catching up. It's not coasting.
Scaling adoption is hard. Doing it while keeping governance intact is harder, because most organisations trade one for the other. You didn't. That's a genuinely unusual achievement, and it's worth treating as a competitive asset rather than a box that's already ticked.
Success can be its own risk. A setup that works well enough tends to stop being questioned, and the next tier of AI value, the use cases that need real workflow redesign rather than a tool swap, gets left on the table because nobody's looking for it any more.
The tools, the regulation and the attack surface you built your governance around this year won't be the same next year. Being controlled today doesn't guarantee being controlled in twelve months unless the process for reviewing it is as deliberate as the process that got you here.
Teams treat AI tools as normal working infrastructure, not a novelty or a workaround. That maturity is exactly what makes it possible to hand them more ambitious use cases without a governance conversation starting from zero.
Leadership can already answer the questions that trip up every other archetype: what's it costing, what's the risk, what's it worth. That credibility is a genuine asset when the next investment case needs making.
Vendor and data-processing terms get proper scrutiny before anything goes live, which means the business isn't the one calling a partner to ask an awkward question after the fact.
If AI is used daily across most teams, a written policy is actually followed, and spend and risk are both visible to leadership, you are almost certainly a Pathfinder. Take the free assessment to confirm it.
The next tier of use cases, the ones that need real workflow redesign rather than a tool swap, plus a formal ROI framework so the board sees AI as a line item that pays back.
Complacency. A setup that already works well tends to stop being questioned, which is exactly when competitors close the gap or new regulation catches a policy that has not been reviewed in a while.
Recommended engagement
AI Scale-Up Roadmap: an engagement to identify and sequence the next tier of high-value, well-governed use cases.
plusnarrative.com · AI Adoption Archetype
About PlusNarrative
PlusNarrative is an AI-enablement consultancy that helps businesses adopt AI effectively, securely and cost-efficiently. We work alongside your team to close the gap between using AI and actually governing it, whichever of the six archetypes you start from.