Comparison

Tenhaw vs Internal taskforce

The cheapest option, and the one that most often stalls at pilot.
Cost. Internal taskforce: Effectively free
£20k–£55k for a PoC
Domain knowledge. Internal taskforce: Already deep
Acquired in weeks
Can change roles and decision rights. Internal taskforce: No mandate
Yes, it is the engagement

The short answer: Tenhaw or Internal taskforce

An internal AI taskforce is the cheapest and most common starting point, and for early exploration it is the right call: nobody knows your business better than your own people. It stalls at a predictable point. The taskforce proves agents work in a pilot, then cannot scale, because scaling means changing roles, decision rights and governance across functions it has no authority over. Tenhaw is usually brought in at exactly that moment. Run the taskforce first, and call us when it hits the wall.

That is the short answer. The call is where it gets specific to your decision.

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On this page
Credit where it is due

The case for running an internal AI taskforce

Weigh these first. They are real advantages, and on some programmes they are the deciding factor.

  • Effectively free: uses people already on payroll
  • Unmatched institutional and domain knowledge
  • Builds internal enthusiasm and capability
  • Discovers the real friction points faster than any external party can
  • No procurement, no contracts, no supplier onboarding

If their case is the stronger one for you, we will say so on the call.

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The decision

Which should you choose?

There is a real answer here, and it is not always us.

Tenhaw

is the right call when:

  • Pilots succeeded and then went nowhere: the classic signal
  • Adoption plateaued somewhere around 30% and stopped, which is the pattern we observe rather than a published statistic
  • The taskforce cannot change roles or governance because it has no mandate to
  • Different functions are building incompatible things in parallel
  • Your risk and audit functions have started asking questions nobody can answer
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Internal taskforce

is the right call when:

  • You are still exploring what agents can do: run the taskforce, it is the right first move
  • Your organisation is small enough that one team's decisions affect everyone anyway
  • You have not yet hit the scaling wall and cannot justify spend before you do

If that is you, say so on the call and we will tell you the same thing. It is cheaper for both of us than finding out in month three.

Still weighing it? Thirty minutes usually settles which way it goes.

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Tenhaw or Internal taskforce?it will say when it is not us
Describe your situation and I will tell you which way to go. If what you need is what running an internal AI taskforce do better, I will say so.

Prefer to talk it through? Ask us on a discovery call →

If you would rather ask a person than a panel, the call answers the follow-ups too.

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Side by side

Where the models differ

The differences that change what you get, rather than adjectives.

  1. 01

    Why taskforces stall at exactly the same point

    Tenhaw

    We arrive with a mandate that spans functions and an operating model engagement that changes roles, decision rights and governance. That is the specific work a taskforce structurally cannot do.

    Internal taskforce

    A taskforce is usually staffed part-time by enthusiasts from one or two functions, with no authority to redefine anyone's job. It proves feasibility brilliantly and then hits a wall made of other people's org charts.

  2. 02

    The 30% adoption ceiling

    Tenhaw

    Across the engagements we have run, adoption plateaus somewhere around 30% of the target population. It is a pattern we have observed rather than a published statistic. It plateaus there because the people who adopted first were always going to, and everyone else needs their actual role, incentives and measurement to change. That is operating-model work, not enablement or training work.

    Internal taskforce

    The standard taskforce response is more training and more internal comms, which reliably fails, because the barrier was never awareness.

  3. 03

    Governance arriving late

    Tenhaw

    Governance, auditability and human-in-the-loop design are built during operating model design, before scale rather than after an incident. Risk and audit are brought in as designers, not as blockers.

    Internal taskforce

    Taskforces typically ship pilots first and meet the risk function when something goes wrong or when audit notices. The retrofit is far more expensive than designing it in.

If one of those differences is the one that decides it for you, put it on the call.

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At a glance

Tenhaw and Internal taskforce, dimension by dimension

Comparison of Tenhaw and running an internal AI taskforce across engagement dimensions
DimensionTenhawInternal taskforce
Cost£20k–£55k for a PoCEffectively free
Domain knowledgeAcquired in weeksAlready deep
Proves feasibilityYesYes, often faster
Can change roles and decision rightsYes, it is the engagementNo mandate
Cross-functional authorityGranted at kickoffRarely
Governance designed inBefore scaleUsually retrofitted
Right first moveNot alwaysFrequently yes

Some rows in that table go against us. They stay in it, because a comparison you cannot lose is a comparison nobody should believe.

A table cannot weigh these against your situation. A call can.

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Procurement and assurance

What a small supplier can evidence

Scale buys an assurance position that clears legal without a conversation. We publish ours in full: what is in place, and what is not yet.

In place now
  • UK GDPR and Data Protection Act 2018 compliant, as a UK-registered company
  • DPA with sub-processor annex available for every engagement
  • 24-hour personal data breach notification, committed in the Data Processing Agreement
  • UK data processing by default, with EU residency available where an engagement requires it
  • Engagement sub-processor list published on the security page and annexed to the DPA
  • BS7858-standard personnel screening before client access
  • Delivery teams are two or three senior people, each one someone James Rooney has already delivered alongside
  • Named-tool-only policy for AI systems touching client data
  • Professional indemnity £1m, employers' liability £10m, public liability £1m, cyber £25k, legal expenses £100k
In progress rather than held
  • Cyber Essentials Plus: certification in progress
  • ISO 27001: gap assessment complete, certification targeted for 2027
  • ISO/IEC 42001 (AI management systems), under assessment, and increasingly the one clients ask for
  • SOC 2 Type II: will follow ISO 27001 where clients require it

If your supplier floor requires certification we do not hold today, that is a real reason to buy elsewhere. The full position, including the DPA and the sub-processor annex, is on the security page.

If procurement needs something this page does not evidence, ask and we will tell you whether we can produce it.

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Board paper

How to put this to your board

Four things you can lift straight into a paper. None of them is an adjective, and every one of them is published on this site before you ask for it.

  1. 01

    The price is published before the first conversation

    £44,000 fixed for the AI Readiness Audit, against the £150,000 to £500,000 a large firm typically prices an equivalent assessment at, our estimate rather than a published figure. The rate card behind our figure, and the large firms' own published framework rates, are on the pricing page, so the arithmetic can be checked.
  2. 02

    The engagement is contracted to end, and to leave a permanent team behind

    The exit date is agreed at kickoff rather than negotiated at the end, recruiting your permanent team is a stated deliverable, and you own all work product and code on payment.
  3. 03

    The people are senior, screened and not substitutable

    Everyone on the engagement is someone James Rooney has already delivered alongside, screened to BS7858 standard before any client access, and not substituted without your written agreement. A squad of three, so the people you meet are the whole team rather than the top of a pyramid.
  4. 04

    The assurance position is published, including what is not yet held

    Professional indemnity £1m, employers' liability £10m, public liability £1m, cyber £25k, legal expenses £100k, with certificates shared during onboarding. Cover levels can be increased for a specific engagement where your supplier standard requires it. Name the limit your supplier standard requires, on any call, and the increased cover is in place at that limit within three working days, with the premium priced into the engagement. Cyber Essentials Plus is in progress and ISO 27001 is targeted for 2027. The full position is on the security page.

Board optics is the one row in the triage table where we do not come out ahead. Ours requires a case, which is why the case is written down here rather than assembled on a call.

We will help you build that board paper on the call, whether or not you pick us.

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book a call

Let's talk about where your organisation is headed.

A 30-minute discovery call with James Rooney. We'll cover where your organisation sits on the agentic curve and which rung to start on. You'll leave with a rough scope whether you engage us or not.

most start with a fixed-price AI Readiness Audit · £44,000 · 4 weeks · working prototypes

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Questions buyers ask us

Why do internal AI taskforces stall?

Because scaling an agent pilot requires changing roles, decision rights and governance across functions the taskforce has no authority over. A taskforce is typically staffed part-time by enthusiasts from one or two teams. It can prove agents work; it cannot redefine other people's jobs, and that is what scaling actually requires.

Why does AI adoption plateau around 30%?

The first third adopt because they were always going to, they are curious and self-directed. Everyone else adopts only when their actual role, incentives and measurement change to assume the new way of working. More training does not move this number because awareness was never the constraint. The 30% figure is a pattern we have observed across the engagements we have run rather than a published statistic.

Should we run an internal taskforce before hiring a consultancy?

Usually yes. A taskforce is cheap, fast, and discovers real friction points better than any external party. Run it, learn what agents can do in your context, and bring in outside help at the point where scaling requires authority the taskforce does not have. Engaging a consultancy before that point tends to buy analysis you could have generated yourselves.

How do we know when to bring in outside help?

The reliable signals are: pilots succeeded but did not spread; adoption plateaued and more enablement is not moving it; different functions are building incompatible things; or risk and audit have started asking questions nobody can answer. Any two of those together mean the constraint has moved from capability to operating model.

What does an internal AI taskforce need to succeed?

Three conditions do most of the work. Protected time rather than side-of-desk goodwill, so the pilot survives a busy quarter. A first workflow that sits inside one function, because anything needing three departments to agree will outlast the enthusiasm behind it. And a stated point at which the taskforce says what it has proved, so exploration does not quietly become a year of pilots. Risk and audit belong in the room early too, since taskforces typically meet them only when something goes wrong. Get those right and a taskforce gets further than most consultancies will admit, because nobody knows your business better than your own people.

How long before an internal taskforce hits the scaling wall?

It is not a duration, it is an event, and it arrives the first time success depends on a function the taskforce does not sit in. Feasibility is quick. A workflow can be proved in weeks, which is why our own fixed-price proofs of concept run two to four weeks. What consumes the calendar is everything after, because spreading a working pilot means changing roles, decision rights and measurement across an org chart the taskforce has no authority over. The signal is not months elapsed. It is that pilots stopped multiplying while enthusiasm stayed high.

Who should be on an internal AI taskforce?

Start with the people whose work actually changes, not only the volunteers from technology and innovation. The standard shape is part-time enthusiasts from one or two functions, and that shape predicts the stall precisely because the group proves agents work and cannot redefine anybody's job. So add an executive sponsor who can change how a role is performed, someone from risk and audit while the design is still moveable, at least one engineer who will build rather than evaluate, and a named person from the function that has to operate the thing afterwards.

Who should own AI once the taskforce has proved it works?

Whoever will run the workflow afterwards, with an executive accountable for the change in how the job gets done. The commonest mistake is leaving ownership with the taskforce. It proved the thing, so it looks like the natural home, but it sits outside the function whose roles, targets and measurement have to change and it cannot change any of them. Moving ownership properly means the operating function owns the outcome, a named executive owns the decision rights, and risk owns the governance. The taskforce then keeps what it is genuinely good at, which is finding the next thing worth proving.

Why pay for a proof of concept when the taskforce costs nothing?

You pay when the question has changed from whether agents work to whether this workflow can be built properly and run afterwards by your own people. Free is accurate on cash and misleading on time. A taskforce is usually staffed with your busiest people, and the meter running is elapsed quarters rather than invoices. A fixed-price proof of concept is £20,000 to £55,000 over two to four weeks, built inside your estate, with your engineers paired on it and the code yours at the end. If the taskforce has not hit a wall yet, keep the money.

What happens to our taskforce if we bring you in?

It keeps going, and it becomes the most useful thing in the room. The taskforce holds the institutional knowledge and the map of where the real friction sits, which is exactly what an outsider spends weeks acquiring. In practice its members pair with ours on the build, with skill transfer measured rather than assumed, and the permanent team we help you recruit is usually built around them, because recruiting that team is a stated deliverable. We leave on an exit date agreed at kickoff. The taskforce is what is still standing afterwards.

Is the work our taskforce already built wasted?

Usually not, and the parts that get rebuilt are rarely the parts people worry about. The pilot proved the workflow, exposed the data problems and showed which exceptions matter, and none of that has to be discovered twice. What tends to be redone is governance, because approval points and evidence bolted on afterwards rarely satisfy the people who have to sign them off, plus anywhere two functions built incompatible versions of the same thing. Bring the working pilot to the first conversation. It is the fastest briefing you can give us.

Why do separate teams end up building incompatible AI tools?

Because each group is solving its own problem correctly and nobody owns the seams between them. A taskforce can convene teams but it cannot tell a function that its version has to give way, which is one of the reliable signs it has run out of mandate. The parts worth settling once are the shared ones, identity and access, retrieval, evaluation and the audit trail. What should stay local is the thin layer holding each function's exceptions and policy. Deciding that split above the functions, and making it stick, takes authority a taskforce does not have.

What do we do when risk and audit start blocking our AI pilots?

Take it as information rather than obstruction, since the constraint has moved from capability to governance, and shipping more pilots will make it worse. The pattern is familiar. Taskforces ship first and meet the risk function when something goes wrong or when audit notices, and retrofitting the evidence costs far more than designing it in. The move that works is to stop scaling for a moment and bring risk and audit in as designers rather than blockers, settling the human-in-the-loop points, the decision trail and what gets logged while the design can still change. Governance before scale is cheaper than governance after an incident.

Does a readiness audit tell us anything our taskforce has not?

Sometimes very little, and that is worth knowing before you spend anything. If your taskforce already has a costed sequence, a sponsor and a mandate to change roles, buy the delivery rather than the analysis. What the AI Readiness Audit adds, where it adds anything, is the breadth a taskforce rarely reaches: the workflows in functions it never got into, a straight read on your data estate and risk appetite, and working prototypes rather than slides, inside a costed plan your board can approve. Four weeks, £44,000 fixed, standalone, and a recommendation to stop is a valid outcome.

Is an AI centre of excellence better than a taskforce?

Often it is the same structure with a better name, and it meets the same wall for the same reason. The test is not what the group is called or where it reports. It is whether the group can change how a job is done inside a function nobody on it reports into, and whether governance is designed by it rather than negotiated with it after the fact. A centre of excellence with real cross-functional decision rights and full-time people is a different animal and will get a long way. One staffed part-time with enthusiasts and a mandate to advise will prove feasibility and stop.

What should we measure to tell whether the taskforce is working?

Two numbers, and neither of them is licences issued. First, the share of the target population using the thing in their real work, which tends to stall somewhere around 30% across the engagements we have run, a pattern we observe rather than a published statistic, and the number that tells you when the constraint has stopped being capability. Second, whether the process itself changed: a step removed, a queue gone, a handover that no longer exists. If the second is flat while the first climbs, you have a popular tool sitting on top of an unchanged process, which produces enthusiasm and no measurable value.

Should the taskforce be full-time or a side-of-desk job?

Side of desk is fine for exploration, and it is part of why a taskforce is such a cheap first move. It stops being fine the moment you want a pilot to spread, because the work then is negotiating role changes with functions that never volunteered, and that is nobody's second priority. Part-time staffing is also why a taskforce reads as advisory to everyone outside it. If you cannot free at least one person properly, say plainly that what you are buying is feasibility evidence rather than a rollout, and say it to the sponsor before the pilot lands.

Are we too big for a taskforce to carry this on its own?

Size matters less than how many org charts a single decision has to cross. In an organisation small enough that one team's decisions affect everyone anyway, the people in the room already set roles and measurement, so the mandate problem does not exist and a taskforce can carry the whole thing. Once a workflow spans functions with their own targets, their own systems and their own view of the risk, the taskforce is negotiating rather than deciding, and negotiation is where it stalls. Count the functions the workflow touches rather than the headcount.