You’re thinking about AI costs the wrong way.

A lot of commentary is written about AI costs and how much firms are spending on tokens.

I’ve even indulged in some of this myself.

In today’s post I’m going to share why large spending on AI can make complete commercial sense.

Can you spend too much on AI?

Cost per task matters when you are building an AI-first operating model.

If a solo operator spends $100,000 a year on AI, is that too low, about right, or too much?

If a 1,000-person firm spends $100 million a year on AI, is that too low, about right, or too much?

Whether AI spend makes sense depends on its wider context.

• How big is the business?

• Who is running this initiative?

• Are the right guardrails and governance in place?

• What are they really using AI for?

• How big is the opportunity that they’re targeting?

• How long will this spend level last - is it build or run?

• Is this removing things from the operating model permanently?

• Is this a major cost avoidance or reduction in regulatory risk?

The right unit of analysis isn’t a token

I think the “token maxing” trend was downstream of a wider anti-AI pushback trend in the workplace where many workers, even when given access to the best technology of the era, don’t want to put any effort into using it and learning something new.

Instead of thinking about tokens consumed, think about the business benefits. Time savings, error rates and rework are the easiest to plan, measure and realise.

For each use case, articulate the benefits and how they will be realised. An AI project is just another project.

One common failure mode I’ve seen is finance-minded folks thinking they can control cost by restricting models. This can be catastrophic!

For a detailed adversarial review of a plan, Fable 5 can justify a higher per-task cost than Gemini 3.6 Flash.

If you’re not operating at that level of detail, you shouldn’t be imposing cost controls and usage guardrails at that level.

This is where many firms go wrong: severe guardrails and cost controls imposed up front tie teams’ hands before they can experiment towards better cost efficiency.

Big returns are plausible for some firms

You don’t need major percentage improvements if an initiative is wide enough.

Not backfilling roles through natural attrition, or redeploying people between functions, represents a real benefit to a business.

Deferring hiring for six months in roles previously filled at scale can generate savings beyond generous AI budgets.

Removing a vendor category, SaaS subscription or enterprise product feature at renewal can produce the same result.

At larger firms, a 3% reduction on a $100 million spend can justify significant investment, depending on hurdle rate and expected payback.

Enormous numbers can be plausible when you are improving a $10 billion payroll or $3 billion vendor spend.

There is a jagged frontier of adoption

The jagged frontier means some AI use cases are proven enough that piloting them is a waste of time: agentic engineering, long document review, or infographics.

Everyone competent knows that you can use these use cases and execute them at a decent level of quality. This is limited only by your own skill level and the models and harnesses you’re using.

For more complex use cases, a human in the loop with the skill and taste to see what needs rework is still required.

In many industries, human-in-the-loop quality review is non-negotiable, especially when work interfaces with regulatory requirements. In legal practice, rules vary by jurisdiction, but lawyers remain responsible for verifying AI-assisted submissions and following any court-specific directions on disclosure or use.

AI transformation is trial and error, like every new technology. Clear scope matters, but AI is surfacing blockers elsewhere in the business faster.

If systems cannot talk via API, or data rules are unresolved, agentic coding tools quickly expose the legacy technology and poor decisions underneath.

The call many leaders make is that this is now “too hard” and “AI didn’t work for us” - when all it did was force a look in the mirror.

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When I quit my corporate job, I didn’t have much idea what I wanted to do.

I knew that AI was the most consequential technology shift since the Industrial Revolution, and that it could become more important than the internet or smartphones.

I needed to find a way to share that thinking with like-minded people who understood that it was now a people problem, not a technology problem.

The technology can do the task. The people are still pushing back.

• They hate AI

• They don’t want it in the workplace

• They don’t want AI-generated content

• They don’t want AI-generated work product

The exact people who would get the most leverage out of AI are people with experience, people with taste, and people with domain expertise.

So what is going on here?

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You need to rethink your entire operating model, and because no one is actually doing this properly yet in the AI era, there’s a window where first movers can build big advantages over their competitors.

At this point, everyone has an AI strategy. I hope so, anyway. You might have even put together very strong AI governance forums and ensured that all of the policy updates and framework calibrations are aligned.

You’re working in an organisation of process, and the processes have been “adjusted” for the AI era, so you’re ready to win. You have rolled out ChatGPT licences, let the engineering team incorporate agents into their processes and even used some AI-generated LinkedIn posts yourself.

So where are the economic returns and why are they not showing up as expected?

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The truth is sometimes disappointing for leaders who thought that “empowering” teams to solve their own use cases and get the benefits would be enough for an AI transformation.

Just like all other transformations of old, an AI transformation is not really a technology project.

It’s a people and change management problem - and if you don’t put as much thought and care into how your people are brought along on the journey towards your AI-enabled operating model, not much is going to happen for you.

The release of Qwen3.8-Max is yet another reminder that AI models are getting better, faster, and cheaper every few weeks.

The excuses that AI “can’t do that” are melting away, and what’s remaining is evidence of cultural challenges that mean teams don’t use AI or lean in because there isn’t a clear future vision they are tracking towards - and they don’t trust that there’ll be a place for them at the table when you get there.

If you want to reset your AI change initiative, the place to start is understanding what your team is actually thinking and feeling about AI itself. All of those attitudes - good, bad, informed and uninformed - are shaping the wider culture around AI.

If you want to improve your operating model radically and realise the benefits, you are going to need to partner with your people rather than make them feel like AI is being imposed on them.

As a technology optimist, I find it hard to believe that there are still people who dismiss AI outright.

But as someone with change management experience, I know all too well that even the most dire change initiative can be turned around by understanding team members’ daily frustrations with processes and platforms that don’t work as expected, removing those blockers, and making sure the training and support are there.

Source: Qwen

I’m in the process of doing additional research into how people respond to AI-driven change, and one thing that keeps coming up is the idea that the first wave of AI projects was more about being seen to be “doing AI” than challenging or reimagining how a business works.

Why do you think that is? I’d love to hear from you.

Leave a comment

Regards,

Brennan

In today’s newsletter:

• Benchmarks are getting saturated

• AI-era operating model redesign

• Shipping use cases to production

• Implementing this playbook (mega-prompt for paid subscribers)

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Benchmarks are getting saturated

Every few months, we get much better AI models. A benchmark is a set of tasks of varying complexity that gives us a basis for comparing Claude Fable 5 with Kimi K3.

Benchmarks are getting saturated - in June, Artificial Analysis removed IFBench from its Intelligence Index because it no longer distinguished frontier models well enough.

Artificial Analysis recently launched Doomscroll, a randomly shuffled feed of charts showing how AI models compare across many measures.

Many well-specified tasks can already be done faster and more cheaply by AI agents.

You don’t need to spend your money running a pilot project to prove that an AI model can extract text from an invoice and turn it into something useful.

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I’m writing this article because increasingly, in conversations I have and company filings I review, I’m building a picture where I feel like too many folks are walking past the $100 bills l…

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Implementation can be delegated, but we still need leaders to care about the judgement they exercise. The point of all this is achieving real business outcomes.

One thing common to technology projects the world over is the tendency for leaders to ask technical people questions that should already have an answer from the business itself.

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This sometimes manifests in technology projects being designed, approved and implemented as ends in themselves. There is no clear link to the overarching business strategy or what the future operating model needs to be. Sometimes there isn’t even a clear people strategy. No thought is given to change management.

There is a difference between a trusted advisor making a recommendation to you, making a decision for you, and executing the decision for you. When we engage external help, it is because we need to go from a state that we are not happy with to one where we have achieved our goal.

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There are three lanes you might want to think about - the leadership lane, the specialist lane, and the team lane.

PS: I’m also going live on Substack with at 1800 EDT 23 Jul / 0800 24 Jul AEST to talk about human in the loop in the AI era.

If you have your own AI change problem you’re stuck with, I have limited availability over the next 90 days to work with a small number of clients to help them unblock their AI initiative. DM me on Substack or reply to this email if that’s something you’re interested in.

In the next section, paid subscribers can access a decision map to help stop decisions bouncing between governance forums and overburdening business owners and leaders with decisions relating to AI initiatives.

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This project can still have no real business owner. In fact, there might even be an executive name in the “Business Owner” box - yet this person was “voluntold” and will be polite about the project but won’t put their best effort into making it a success.

The gap between a sponsor going through the governance motions and a business owner who cares deeply about achieving the end goal because their reputation is on the line couldn’t be starker.

Transformation projects already carried high stakes before AI began dominating boardroom agendas around the world, so it’s no surprise that CEO-level ownership matters so much.

When I worked in change roles in the corporate world, I often came across well-documented projects with the finest hand-curated governance arrangements. Yet, when I looked under the hood and did the testing myself, I often found a documentation-reality gap.

Well-funded change programmes with “best practice” governance can produce amazing governance documentation while accountability and ownership dilute with every decision and meeting.

There is nothing worse for culture and buy-in to a change agenda than a delivery team waiting for business decisions while no one senior understands why AI projects are surfacing complex issues that block progress. Many AI projects are also surfacing hard-to-reverse, one-way-door decisions that leaders cannot afford to defer.

Silo structures and corporate politics are always fun to navigate, but on AI projects, they can be even more disruptive because of the additional hysteria floating around in relation to job loss and other topics like outsourcing and offshoring.

This requires careful planning and a lot of intensive stakeholder management through regular check-ins and over-communication. All of the different “project roles” can’t afford to be passive when so much is riding on AI projects at many firms.

What are the key warning signs?

• Delayed decisions that keep being rolled over from meeting to meeting

• Steering committee meetings that deviate from required decisions or actions

• No clear business owner with the authority to make decisions

• The gap between formal communication and informal vibes widening quickly

• Constant escalation of problems due to lack of clarity and delegated authority

A theme I’ve written about a lot on Getting AI To Work is that AI projects surface the cans that have been kicked down the road. If you took a shortcut on a data project three years ago and those gaps now undermine your AI project outputs, well, you need to fix that blocker to achieve the desired standard.

A technical team can’t decide a firm’s risk appetite, figure out the entire operating model in isolation, or manage the people consequences of AI change without the right level of executive support.

In the next section, paid subscribers will get further analysis and a short ownership test they can apply to a live AI initiative in their business. If you subscribe now, you’ll get the full ownership test and access to the archive of practical AI transformation and change playbooks.

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As the second half of 2026 proceeds, the distribution of outcomes across firms with active AI initiatives is wider than optimists might have predicted.

The governance is in place, the budgets have been approved, and the licences rolled out - yet the operating model is the same as it was last year.

I’ve spent many an hour over the course of my corporate career in a workshop or meeting that danced around the real choice a business faces.

Sometimes you can build a stakeholder coalition and get the right outcome. Other times, politics and commercial realities push the tougher calls down the agenda.

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One example is a use case that crosses departmental boundaries. Stakeholders need to agree that the way finance and sales run the process must change to realise the benefits. This is a 30-second decision from a leader, but a 10-week drama at many firms.

Deciding who is accountable for AI output, and what risk profile is acceptable for work that reaches customers or regulators, is a big part of the complex decision-making that can accelerate benefits realisation or cause a project to stall.

The core idea is this: decision debt is the accumulation of tough calls you haven’t made. It increases cost, confusion and resistance in much the same way as tech debt - or keeping an underperforming supplier in place.

The consequence of this is that when leaders do not make tough decisions, people reporting into them and the people reporting into those people start to become more cautious and risk-averse.

People worry more about their job security and the political consequences of making a judgement call. They start to wait for permission and preserve the old processes. That is the opposite of what an AI transformation, or any change initiative, needs.

Paid subscribers will be able to read on to see the five questions I would use to expose the decision debt inside an AI change initiative and how I think about this challenge for AI projects of all sizes.

PS: If you haven’t already subscribed to my YouTube channel, do that now.

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• The rise of AI cost consciousness

• What many leaders are missing

• How to implement this playbook

PS: Check out my latest video over on YouTube:

The rise of AI cost consciousness

The money you spend on AI subscriptions and API bills is visible. The money you spend on time wasted, duplication, and inefficiency often hides in your management reports.

The rise of AI cost consciousness is what I’m calling the intense focus in recent months on how much AI is costing some businesses. If you’re using APIs or coding agents at enterprise scale, every token can add to the bill.

And the pricing for the best models isn’t cheap. When you read about senior Uber executives complaining about blowing their Claude Code budget for 2026 in just a few months, you can see how that happens.

This is because of both the capability and the marketing. GPT-5.6 Sol can just do more for you at a higher level of quality. If you’re on a subscription plan, using it up to the limits or even stacking multiple $200/month plans is something I increasingly see among power users.

When you have worked in the corporate world or run a business, you know that it is quite normal for departments to have a budget and be managed to that budget. For some commentators on social media, that sounds outrageous, and increasingly I think that they’re not wrong.

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• Muse Spark 1.1 and Grok 4.5 (not GPT 5.6)

• Why more efficient models matter

• How to take advantage of this trend

While much of the world is still talking about Anthropic’s released-blocked-released Fable model, or the excellent GPT 5.6, two other AI models were released this week that offer a lesson for our AI change agenda.

Grok 4.5, developed by SpaceXAI in collaboration with Cursor, and Meta’s Muse Spark 1.1 were released this week, showing that the AI race is far from over.

I’ve written extensively in this newsletter and spoken on my YouTube channel about how AI is now a people problem, not a technology problem.

This doesn’t mean I’m not heavily focused on the tech. In fact, one of the key skills of this new era is that we all have to become more technical.

This week’s AI news has arrived in such volume that I’m glad I never focused solely on repeating AI news, but instead on change strategies for AI transformation.

I’m starting to feel the benefits of having spent the time and effort pushing AI tools and models hard over the 18 months since I quit my corporate job.

The rate of progress, as I have directly experienced it, fuels my conviction that embracing AI and rethinking your operating model is one of the most important challenges many businesses face.

This is the real lesson of this week: we want to improve our businesses, and we are regularly getting new AI models that help us achieve that.

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Muse Spark 1.1 and Grok 4.5 (not GPT 5.6)

Source: Meta

Over recent months, there have been a lot of complaints about single-vendor risk and the cost of tokens. Much of this is because enterprise spending is currently focused on Anthropic’s and OpenAI’s top models. We need situational awareness to protect our businesses and make sure our change programs are flexible enough to meet the moment.

Muse Spark 1.1 represents a substantial lift in performance and is a credit to the Meta team. They had the compute, cut what wasn’t working, invested billions in new talent and have now shipped near-SOTA coding and agentic capabilities in Muse Spark 1.1, launched Muse Image and previewed Muse Video.

Meta has also launched its new Model API in public preview, introducing more price and quality competition for the leading labs and putting further pressure on Google DeepMind to get on with it and release Gemini 3.5 Pro.

SpaceXAI was also able to pivot and put its compute to good use, with its massive resources and collaboration with Cursor lending a hand. The release of Grok 4.5 will take some time for enterprise buyers to appreciate, but it represents a substantial improvement in efficiency and productivity at a much more competitive price point.

This is good for businesses around the world because it means that, as the capabilities of AI models rise, more models at better price points are available to meet their needs and help diversify away from single-vendor risks.

In Australia, APRA has warned that boards need stronger AI literacy and better oversight of third-party dependencies. If greater model choice helps regulated entities reduce concentration risk while meeting their security, resilience and governance obligations, that’s a good thing.

Muse Spark 1.1 and Grok 4.5 are not Fable-level or Mythos-level models, but they are not that far behind GPT 5.6 and will improve over time. The key point I want my audience to consider is that getting an AI transformation right involves more than picking one vendor or selecting a specific model version.

It’s about building a culture of trial and error that helps you learn from mistakes and discover what works in your business when taking AI use cases from idea to production.

Source: SpaceXAI

Why more efficient models matter

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AI change often fails because of people, not technology.

The real question is whether your team buys in or pushes back.

The people side of AI change matters because too many companies are treating it like a technology project, not a massive cultural change problem.

It requires big changes in behaviour and how things are done around here.

Most companies have started some level of AI experimentation. Many have rolled out AI licences or let their engineers use coding tools like Claude Code, Cursor, Devin, and Codex.

Some have launched more expensive and ambitious endeavours, including full AI transformation.

But increasingly, the media share stories of companies concerned about rising costs and an inability to isolate what the return on that spend actually is.

New here? Read these articles:

Translation is the hard part

People understand the seriousness of AI and the need to act. Translating that into tangible business outcomes is where it breaks. You need to get things done better, faster, and cheaper within your operating model.

To achieve these benefits, you need to change how work gets done. This means changing decision points, workflows, and the incentives team members have.

It requires shifting the quality guardrails that must be obeyed, as well as the different handoff points between your teams and the customers or regulators who consume your work product.

There are many hidden failures. A company might spend money on licences, or even a fortune on AI API usage, without doing the mapping to link that spend to tangible, real outcomes.

The change fails because people still don’t trust AI. They haven’t actually changed how they work, or they use it in a purely cosmetic way. Often this is just to hit the top of a token-maxing dashboard because that was the best idea management could come up with to track AI usage.

What people opportunities are you thinking about with AI in your business?

DM me on Substack with your answer.

Winners learn from mistakes

PS: I’m running a limited offer until midnight 1 July 2026. Upgrade your Getting AI To Work subscription to an annual paid one and you’ll lock in a 30% discount today and keep that 30% for the life of your subscription. Lock in your exclusive discount now to get access to all paid articles.

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The leaders in this space are rethinking their entire people, process, and platform strategies. When I read about how the frontier labs use the tools they ship, it’s clear they are dogfooding, they use the product and they push what they do to the limit.

Anthropic uses Claude heavily. OpenAI uses ChatGPT heavily. The leverage gained from using AI effectively is significant.

When I read about companies struggling to show a tangible return on investment, most of these companies haven't actually progressed past basic implementation.

Cultural barriers are holding back their teams’ ability to learn through trial and error. Even in risk-averse corporate environments, this requires making mistakes and learning from them.

Technical change can be simple. You get the licences, execute the enterprise deal, and pay the invoices. You have a dashboard showing that 100% of your employees have access to the latest AI tools.

People change is a lot messier. People will tell you to your face they love the initiative, work hard, and even ship projects that deliver. But deep down they think it's yet another harebrained management idea.

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We learn from mistakes, and we accelerate progress through making more smart, risk-adjusted moves. My claim today: AI works fine, you’re just not pushing it far enough.

Most AI advice is too shallow. It focuses on tools, prompts, and isolated use cases. This piece is longer because the real issue is deeper: AI only works when leaders change how work happens.

We already know that AI can help you rewrite an email, build a spreadsheet, or automate the production of social media posts. But leaders need to stop using it like a productivity toy and start using it as an operating model redesign lever.

The gap between companies that are getting returns on AI and those that are writing off failed AI projects isn’t about the model capability. It’s about ambition, integration into the business’s daily operations, and leadership.

There’s a lot of talk at the moment about companies struggling to find the return on spend for their AI subscriptions and AI API usage. I think that’s because the focus has not been enough on the people and the change management side. Too much attention has been given to treating the rollout of AI like a tool provision exercise, not an operating model redesign exercise.

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Take smarter risks with AI use cases

Time is the dominant factor in gambling. Risk and time are opposite sides of the same coin, for if there were no tomorrow there would be no risk. Time transforms risk, and the nature of risk is shaped by the time horizon: the future is the playing field. - Peter Bernstein, Against the Gods

AI adoption shouldn’t be reckless. It can’t be too timid either. A 2024 Gartner study predicted that by the end of 2025 at least 30% of AI projects would be abandoned after the proof of concept because of poor data quality, weak risk controls, increasing costs, or unclear business value. They weren’t wrong.

At the same time, the HBS and BCG Jagged Frontier study found that consultants completed 12.2% more tasks and worked 25.1% faster on tasks inside the AI capability frontier. Consultants who over-relied on AI for tasks beyond what it was capable of struggled.

I know from my own AI testing and work over the last 18 months, the lesson is very clear: AI works, and it works well. However, this is only when you’re working on the right use case with the right model, the right harness, and the right context.

The way you take smart risks with AI use cases is to make sure that every use case has a clear owner, a measurable outcome you are targeting, guardrails around what cannot be done, and a due date.

You want to be asking, “Where are we wasting time?” or “Where are we doing things that are repetitive, context-dense, too slow, or inconsistent where we’re using manual processes when we could be automating with AI?”

When it comes to getting AI to work, I hope that you’ve learned that the real risk isn’t trying out AI and giving your people licences to use ChatGPT or Claude. The risk is running vague pilots and proofs of concept that don’t teach the business anything new, and don’t give you lessons to be learned and folded into a wider portfolio of AI bets.

Some of these bets will fail. Some of these will deliver core workflow enhancements that save a lot of time and money. More ambitious ones will start to shift the economics of your business. We’re getting AI agents to do tasks at scale and in volume. Where they are better, faster, and cheaper at doing them, it shifts the economics of your operating model and starts to push those return on spend numbers in the right direction.

Waste less time on manual processes

Manual work hides in plain sight: customer support that could be self-serviced, meeting notes that never become actions, spreadsheets moved between systems, documentation reviewed manually, and emails that exist only to coordinate work that a workflow could have handled.

AI gives leaders permission to start attacking a lot of the repetitive and low-value work that exists in every team in their business. The first generation of AI value delivery is shrinking or compressing waste, cutting the time between getting a lead and sending out a response.

Holding a meeting and taking action, getting a question and issuing the answer (all of this stuff isn’t glamorous). But inside every operating model, there are operating processes that are currently far too manual. System one doesn’t talk to system two. Even though an API key exists, no one in the business knew what it was. So, people will be downloading information into a spreadsheet, reformatting it, and then uploading it into another system.

When I think about wasting less time on manual processes, using AI in your business is partly about discovering all of this waste and inefficiency. Instead of having to wait months to do something, you can start removing waste in an afternoon.

We can redesign and reimagine how the whole end-to-end process can work. We do not need to make broken processes faster. We can start drilling into why the processes exist at all. In the example I mentioned above, just by automating data transfer between systems, some businesses could free up entire teams that are currently just doing back-and-forth data reconciliation.

Take advantage of AI leverage to create value

I think a lot of the AI conversation is focused too much on reduction: saving time, saving money, and letting you reduce headcount. A bigger opportunity is creating new value, improving product quality, improving response times, closing customer requests faster, and adding product features. All of these new value creation ideas are easier than they’ve ever been.

Saving time isn’t the finish line for an AI project, it’s an opportunity for teams that have that freedom to start running more experiments. An example in my own business has been doing A/B testing and quantitative analysis of what’s working and what’s not.

Leaders using AI well shouldn’t just have much nicer-looking dashboards, spreadsheets, and well-formatted emails with perfect grammar.

The real value comes from shortening that decision cycle, where problems get surfaced faster with richer information to help support better decisions.

For example, self-service data and analytics was a challenge for many years. Yet the lack of technical skill on the part of a lot of people meant that they were still hamstrung by what a data and analytics team could produce for them.

You can now use natural language to query and get answers to questions that previously might have taken someone in the data and analytics team a couple of days to make sure everything was right.

You obviously need a lot of testing, quality control, and data governance around it, but you can just start and learn. You can find out what works and what doesn’t, and where the data quality is good enough and where it isn’t.

When it comes to the value you can get from using AI tools properly, the last place you want to be is in more meetings and emails going back and forth. You want more outputs and outcomes. You want to be moving the dial, not creating a whole new architecture of governance and bureaucracy around AI transformation.

Become more ambitious with operating models

Most businesses are still trying to roll out AI inside their operating model and their org chart exactly as it stands today. There’s a leadership-employee perception gap where leaders think that managers are creating space for AI experimentation, trial, and error, but fewer employees think that’s actually what’s happening.

The barriers around change management and organisational culture dictate how people try things, and what the organisational response is whether things work or don’t. If there’s a blame culture that does not let people make mistakes, you just are not going to get the results, compared to an organisation with a learn-from-mistakes culture that lets people learn and move on.

The real operating model redesign opportunity isn’t about giving everyone ChatGPT access. It’s thinking about how work flows across the business.

In this newsletter and in my videos, I’ve spoken a lot about questions which aren’t really being asked yet. Why do functions exist for some value streams? Why do teams exist? If you think about the task-by-task flow for some value streams, if you have processes that move back and forth across teams with no clear gateways and quality control checkpoints, you can probably redesign these workflows with the help of AI agents. Instead of having three or four teams touching a work product, you can have one flow handled by one team. You fold the contributions from each team into the workflow itself, complete with the evals, controls, and guardrails that you desire.

Then, the specialist experts act as the quality assurance and performance improvement people, owning and remaining accountable for the delivery of that new workflow. We’ve got an opportunity to redesign everything.

If you think about realising the benefits from all of this spend, it’s obvious to see why some companies will never realise $1 of net benefit from AI spend. They have not changed how they work at all. The entire point is a complete rethink of how we do everything.

The future operating model isn’t humans versus AI. It’s a collaboration of people, AI, processes, unique IP, and knowledge, sitting inside whatever guardrails the board or company owners want to put around it.

PS: I’m running a limited offer until midnight 1 July 2026. Upgrade your Getting AI To Work subscription to an annual paid one and you’ll lock in a 30% discount today and keep that 30% for the life of your subscription. Lock in your exclusive discount now to get access to all paid articles.

5 Tips To Implement This Today

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If your AI rollout has failed or is failing, this is actually a good thing, not something to shy away from.

In this article, I’ll share a playbook for how to turn your change program around.

PS - if you missed this video, you should check it out:

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In today’s newsletter:

1. Why mistakes build valuable knowledge

2. How teams deal with complex change

3. The playbook for successful AI change

1 - Why mistakes build valuable knowledge

When you make mistakes with AI, you are building process knowledge. You learn what does and does not work with the tools and the environment you are working in.

Mistakes build valuable knowledge for business owners and leaders because they start to understand what needs to change. Everyone can drive a successful change program that leverages AI to do things better, faster, and cheaper.

The difference between tacit knowledge, living in our heads, and explicit knowledge, written down, matters. Here is where the intellectual property supporting a business’s ability to do things for its customers in a predictable, reliable, and high-quality way lives.

If you try to do something with AI and it works, you’ve learned something. If you try to do something with AI and it doesn’t work, you’ve learned something.

Because of the cultural impulse towards more risk aversion over the last few decades, an obsession with risk management has often killed off these important entrepreneurial trial-and-error processes.

This means that many companies have spent more time and effort worrying about AI governance and AI risk management than actually figuring out how to use the tools themselves.

They need an accurate fact base on which to reason about the risks involved and how to govern them. You could not dream up a more ironic example of putting the cart before the horse.

Some companies have spent months worrying about all of these concerns before they have even let their users have access to one license to conduct experiments.

This is why if you get to the point where you have had a failed AI initiative, you have accumulated valuable information. Some companies haven’t started the trial-and-error phase of AI adoption yet.

Every little step that you have taken in the direction of making your company more AI-first is a step where you have learned and improved. You have updated information.

For example, you will know who the natural AI leaders are in your business. You will know who is advancing change and who is blocking change.

When it comes time to think about organisational design, you will have a better sense of who leaned into this opportunity and who threw up roadblocks.

Action Point: If you still haven’t let your team do some trial-and-error and experiments, with the goal of making improvements to your operating model - get that sorted this week. There are ways to reduce the risk from light AI experimentation. You should be able to find one use case and get it into production every week.

2- How teams deal with complex change

What we know about change management from the literature, and what I know from my own direct experience in the space, is that a lot of teams struggle to deal with change. The more things that are changing at the same time, the harder it is for a team to process the change. There is such a thing as change fatigue or change exhaustion.

This is what happens when there are so many little things changing across how someone does their job every day that cumulatively, after many years of this, having something as threatening and emotional as AI potentially automating you out of a job is almost too much to process.

The rhetoric in the corporate world is about AI job layoffs. Reducing headcount is one way to realise benefits from AI investment. It is disingenuous for people to claim it is all about augmentation; some spend is intended to reduce payroll expenditure.

We need to be open and honest about all this AI change and what it means for teams in reality because they have lives to live and you owe it to them to be upfront. That openness, transparency, and communication is so much more important than I think a lot of corporate leaders realise.

When you work at big companies, you know that a lot of things are scripted and rehearsed. Communication is planned. Everything feels like it has this flavour of a Hollywood production.

People see through that. They know when you are talking absolute nonsense as a corporate leader or a business owner.

What you are doing when you choose that communication style is effectively saying that your team is incapable of handling the truth. It is a paternalistic and arrogant style which does not really work in the 21st century when constant streaming information is the default life experience.

When someone in your workplace picks up their smartphone, they are extremely exposed to the algorithms and everything they see and process. If they are coming into a work environment and your style is stuck in the 1990s, that means that everything you say is being filtered as if it is curated nonsense.

You don’t have trust. You have an environment where free and frank conversation can’t happen. There are other highlights of this which make adopting AI harder than it has to be. When there are things like restricted information flows, or instructions to “please cascade this information to your team”.

Everyone gets a different story about what the agenda is, why we’re doing it, how we’re going to do it, and when we need to be finished by. This is a common theme in failed projects- what you want isn’t what the team hears, and confusion makes it harder to get the change understood let alone adopted.

This means all these programs in the AI space face an uphill battle. You are going into an information ecosystem that is already anti-AI. How do you get cut through? That’s what I’ll talk about in the next section.

PS: I’m running a limited offer until midnight 1 July 2026. Upgrade your Getting AI To Work subscription to an annual paid one and you’ll save 30% today and 30% for the life of your subscription. Lock in your exclusive discount today.

3- The playbook for successful AI change

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Today I had an article planned about what AI models like Anthropic’s just-classified-as-export-controlled-products Fable and Mythos mean for AI transformation and change work in our business…

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I think it would be an understatement to say that the focus in the corporate world on doing an AI transformation or adopting AI has reached a fever pitch.

The idea that you can do things better, faster, and cheaper in your operating model and achieve your corporate goals through just adopting a new technology is quite an enticing prospect.

I fear that a lot of leaders have forgotten many of the lessons we’ve learned in the realm of change management. These lessons apply to an AI transformation just the same as they do to any other project or program of work.

Tailoring beats the default playbooks

AI transformation requires a tailored change approach. It needs to take into consideration how your business works. It also needs to consider how your people feel about change and all of the different characteristics of your culture. This means how you approach an AI rollout is going to be very different.

A cookie-cutter approach where we do a pilot, a proof of concept, then roll things out with the help of a big consulting firm or a business process outsourcing firm - it just isn’t going to work for many businesses. The default isn’t going to move the dial here.

A lot of smaller businesses and startups will find that because they don’t have the baggage of bureaucracy and process, they can move faster. This means there are a lot of risks they’re introducing. These require careful management and deliberate decision-making about where you strike the balance of human and AI in a new workflow.

Rebuild fast, protect trial-and-error

The pace of change is faster than it’s ever been before. Every few weeks, there’s something new that is worth exploring. We sanity check whether it could add value, or dismiss the latest tool as mere hype. The pace of this change is far beyond what most businesses are capable of dealing with properly, let alone implementing, doing all of the testing and controls, and getting it into production use.

This new normal presents enormous challenges. Leaders want to be able to demonstrate that they’re operating close to the frontier. Unless they’ve been operating their business in this way for the last decade, they need to entirely rebuild their operating model from the ground up. It’s not that they were too slow in the past, it’s that the speed of today dissolves the quarterly cadence of yesteryear into mist.

If they want to evolve in this direction, that presents enormous risk to any business. Being able to run business operations from a position of stability is critical. You can’t be constantly chopping and changing what tools you’re using and what workflows you’re doing. Clients won’t tolerate it, and your people will get very frustrated if they have to change how they do their work every five minutes.

Imposing top-down change is hard in this environment. A lot of the value is coming from bottom-up experimentation. Your best people are figuring out, through using the tools themselves, how to apply AI in new ways to do things better, faster, and cheaper. You need to listen to them, incorporate their new solutions, and reward them.

This entrepreneurial trial-and-error process is how we get all the cool things and innovations in our world today. Keeping that experimentation and openness to novelty is one of the most important cultural things that a company going into this AI era with eyes open is going to want to retain.

People, not technology, constrain adoption

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If you’re new here, I’m Brennan McDonald and I write about the people side of AI transformation. This newsletter grows through word of mouth and your recommendations. If you enjoy this, please share it with a friend today. It’s always appreciated. If you have any feedback, you can reply to this email. You can also find me over on YouTube. I’ve also started a new newsletter called AI Risk & Response where I’ll be sharing my thinking and research on AI risks and policy responses. This newsletter will continue normal service!

- Brennan

In today’s newsletter:

• What does this mean for change?

• So what about my AI transformation?

• So how do you respond?

• What do I do this week?

I’ve been thinking a lot about how attitudes in wider society about AI spill over into the workplace. What change playbooks can we run to drive better adoption and return on spend?

When you’re thinking about how you want to implement AI to do things better, faster, and cheaper in your business, part of the landscape you’re operating in is one where many of your employees spent the morning commute scrolling through a social media feed with reels where people are ranting about AI data centre water use or other sorts of politically charged anti-AI commentary.

This means when you make any AI-related announcement at work, you’re already operating inside a polluted thought space.

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What does this mean for change?

I think it means that if you don’t have a clear idea of what level of AI use you want in your future state operating model, and you haven’t clearly articulated how you will bring your people on that journey, you’re starting on the back foot.

A lot of the layoffs that have been labelled as AI layoffs haven’t helped this year because it’s flooded the zone where a lot of different issues are being conflated into one.

Wanting to cut jobs because of “overhiring”. Wanting to cut jobs onshore so that you can do things “cheaper” offshore. Wanting to cut jobs because you actually did deploy some AI workflows or previous automation projects that have some similar impact but no AI attached.

All three options could be sitting beneath something labelled as an “AI job layoff”. When you read or listen to what corporate executives say on earnings calls, press releases, or interviews about how they are thinking about AI, it is clear that a lot of the rhetoric in the public domain is split between two things at the opposite end of the spectrum.

On one side, you have people claiming that they are hiring more. They will need more skilled people. AI is going to create more new jobs. It is just like any other normal technology.

At the opposite end of the spectrum, AI is helping cut costs. AI is helping reduce headcount. AI is going to enable the business to scale up headcount reductions over the next couple of years.

When companies update earnings guidance, those statements carry regulatory consequences if they are materially misleading.

This all feels like “flooding the zone” where there are so many new announcements and new perspectives every day that it’s hard to know what’s going on.

So what about my AI transformation?

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If you’re new here, I’m Brennan McDonald and I write about the people side of AI transformation. This newsletter grows through word of mouth and your recommendations. If you enjoy this, please share it with a friend today, it’s always appreciated. If you have any feedback, you can reply to this email. You can also find me over on YouTube.

- Brennan

In today’s newsletter:

• Frontier time vs friction time

• Why more thinking time matters

• The Mistake Mining Playbook

Frontier time vs friction time

The firms that win with AI will not be the ones that move recklessly in frontier time or defensively in friction time. They will be the ones that use structured thinking time to close the gap safely.

There’s frontier time, the fast pace, something new every day, something better, something shinier, something loudly heralded or criticised on X in volume.

Then there’s friction time. Months to years behind where the frontier is operating, subject to a slower pace of adoption, the constraints of people, process and platforms, and considerations around governance and risk management that only those who have lived through the pain appreciate.

I’ve spent most of my career working in firms where friction time rules the day. There are always reasons not to do something. Not the time, not the people, not the budget, not the right compatibility with existing operating model capabilities.

At regulated entities in particular, we have things we care about where we want humans to be accountable for decisions. A lot of this friction time in isolation makes total sense. Do we want a vibe-coded core banking platform that doesn’t get proper testing? No way!

This tension between being an early adopter and not accidentally blowing up businesses that customers depend on is a very real part of what’s going through the minds of boards and executives when it comes to AI adoption.

At the moment, I pretty much live in frontier time. I literally am using AI every day for as much as I possibly can.

I spend a decent amount each month on making sure that I test out whatever I can, burning AI API usage so you don’t have to. When I work with people, I have as one of my goals to expand their mindset for what is possible with AI.

This is why Getting AI To Work has focused on the people side of AI change. Because of the pain I’ve experienced at the frontier, I know that for people living in friction time, there is so much pain relief that is possible if you clearly think about an AI-first operating model in your business.

There’s a reason why I say you need to start shipping AI use cases in production that your people can use to do things better, faster, and cheaper every week. You need to do whatever you can to reduce that gap between frontier time and friction time.

Does this mean you need to deploy a model to your people the day it comes out? No, but it does mean you should have someone in your business scanning that horizon. Scanning what is happening in real time, checking, testing, evaluating and recommending what the next pivot in your business’s AI adoption journey is going to be.

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Why more thinking time matters

I’m not referring to thinking time when an AI model is generating its response to your query. I’m thinking about thinking time. You step away from your computer. You step away from the emails. You step away from the meetings. You actually reflect on what you want your AI-first operating model to look like.

The idea of path dependence where choices you make today have long-running impacts has never been more real to my mind. How you choose to respond to AI today as a challenge and opportunity could mean life or death for your business in a few years’ time.

These sorts of decisions can be expensive to reverse if you make the wrong one. If you picked the wrong mix of people, process and platforms and don’t have the right talent helping you achieve your transformation, you could be permanently locking in structural disadvantages.

There are clearly risks in AI that you need to manage. A lot of complexity in the AI space is about deciding where you want to automate and where you want to keep humans in the loop to avoid outages or mistakes leading to reputational damage or fines.

Thinking through all of these things and figuring out what you really want that operating model to look like, especially as an intelligent firm where you’re pushing more decisions and day-to-day activity down to AI agents, takes time and effort.

So some leaders can be forgiven if they’ve been moving slowly. The best ones have already figured out how critical this moment actually is. I’ve often referred to AI acting as a ray of sunlight. It reveals the cans you kicked down the road.

You’re now at the point in many boardrooms and executive meetings where AI pilots start revealing the consequences of some of these cans that have been kicked as far as you can get away with in the AI era.

You need to put in the thinking time to design your operating model in an AI-first way. Thinking time is about going from frontier time to friction time and not ending up in the wake of AI-first startups in your niche.

In the next section, I’ll share how you can run this playbook in your business today.

The Mistake Mining Playbook

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Hi there,

If you’re new here, I’m Brennan McDonald and I write about the people side of AI transformation. This newsletter grows through word of mouth and your recommendations. If you enjoy this, plea…

Read more

If you’re new here, I’m Brennan McDonald and I write about the people side of AI transformation. This newsletter grows through word of mouth and your recommendations. If you enjoy this, please share it with a friend today, it’s always appreciated. If you have any feedback, you can reply to this email.

- Brennan

In today’s newsletter:

• The buy-in problem is misdiagnosed

• Evidence creates buy-in, clever words don’t

• How to apply this to your business

The buy-in problem is misdiagnosed

There’s a standard change management playbook. It’s tempting to roll it out when you’re doing an AI transformation.

However, if there’s one thing I’ve learned over the last year and throughout my corporate career, it’s that you need to tailor your approach to the situation you’re actually facing.

A lot of the standard change management collateral can be easily generated with the help of AI. You can make a stakeholder map. Prepare a comms plan, prepare the training course content, draft emails and communications with stakeholders and sponsors.

It doesn’t mean you should. In fact, one trend I’m starting to see is a lot of AI-generated content working its way into corporate change program collateral and being exposed as soon as the problems begin.

As a discipline, change management has learned an awful lot and incorporates a lot of the lessons of program management, project management, systems engineering, and general business practice.

It was built to solve the challenge of getting people to do new things in new ways when they would much prefer to keep doing things the old way. When it comes to AI transformation, I think the buy-in problem is getting misdiagnosed. That is one reason why I worked on the 5C framework and shared it in this newsletter.

All of this change around AI in the workplace is happening when we have unresolved policy problems to solve in the personal domain. If your worker doesn’t want an AI data centre built in their neighbourhood and buys into a lot of anti-AI rhetoric, they’re not going to lean into working on an AI transformation of how they do their daily tasks, are they?

A lot of the pushback around AI in the workplace is in regard to things like compliance with regulations, the quality of outputs, or what things mean for job security. A lot of these are reasonable issues for employees to raise.

It doesn’t help when you have a global group of corporate leaders who have turned AI job layoffs into a mantra. Whether the reason is because an AI automation workflow was deployed or because they hired too many people during the pandemic doesn’t really matter.

There’s an enormous amount of fear. When you add it to the fact that a lot of this is going to impact knowledge work jobs that have already suffered from enormous amounts of both disclosed and undisclosed offshoring and de-skilling, the scale of the challenge from the change management side is clearly apparent.

There is a difference between irrational resistance to change and rational threat detection to your own well-being and safety. In this article, I’m going to argue that you have to show how AI works and improves your team’s experience in the workplace instead of rerunning played-out persuasion and communications playbooks from the pre-AI era.

Evidence creates buy-in, clever words don’t

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