In today’s article:

• The first prompt advantage

• How things actually work

• Data quality matters

• Ownership and accountability

• Determined machine problem solvers

• Avoid failure modes

• Why it’s different this time

• Low-cost automated audits

• What the AI sunlight finds

A lot of people think that an AI transformation is just going to help them do things better, faster and cheaper, and that that’s that. My view is a little bit different.

I think AI is first and foremost a sunlight disinfectant that enables us to understand what’s really going on under the hood at a low technical level of detail in a much more efficient way than we might have had to do the investigation just one year ago.

This change is in what we see across the operating model. We’re still working in much the same way, but with the help of AI agents, we can rapidly find the bottlenecks and use agentic workflows to solve them much more quickly than we could in the past.

If you change how a business is using AI to an adversarial and audit-type style, the first thing you start to see is all of the little bits of extra fix and repair and maintenance work you need to do. You could almost think of it as an engine tuning or performance improvement exercise.

Every business has $100 bills sitting on the footpath that they haven’t picked up yet. I don’t think there’s much benefit in trying to put cosmetic KPIs around AI usage that don’t link up to what you actually want to achieve strategically.

When you start applying AI well, you start to get detailed, low-level insights into what is going on in your operating model that may not have previously been reported to you unless you had an incident and had to do a deep dive on a particular function or process.

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The first prompt advantage

You start with a blank prompt window in the tool of your choice. An AI agent won’t start automatically fixing your business. You still need to steer it in the right direction and set things up so that it can actually operate with the level of agency frontier models have today.

This is why I think that leaders getting on the tools and working extensively with AI is important. If they haven’t experienced both the success modes and failure modes themselves, the way they guide and govern AI transformation is going to be undercooked.

All of the standard things you need to think about when doing a transformation apply to an AI transformation. To make the right decisions and nudge the AI models in the right direction, having knowledge about how things actually work under the hood in a business or the curiosity and tacit knowledge to figure out where the answers are or who has them is more important than ever as a skill.

This comes naturally to a lot of business leaders when they are in direct control and directly accountable for a piece of work. They can operate. They have the context. They know the right questions to ask.

I think many people are missing that if you put in a goal that is modest, you will get a modest outcome. The AI models can do much more for us if we think expansively about what is possible.

I think one of the reasons why a lot of people are very sceptical of AI is that it’s very easy to get stuck in a doom loop of prompting. You can set up a very fancy set of workflows and dashboards and look like you’ve done an awful lot of work, but the business metrics that you care about don’t move.

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How things actually work

Processes, and how they’re designed, how long they’ve been operating, and the problems that people already know there are, are another key area here for exploration and interrogation.

The processes that everyone knows how they work often don’t actually map to exactly how they’re configured in systems and exactly how things flow from customer to firm to customer again, or customer to supplier to you.

You need to understand and appreciate that one of the downsides of being in a continuous-improvement, AI-first era is that every little process improvement win you achieve can probably be made better with the next generation of models.

In my corporate experience, I did a lot of business analysis work, sitting with people, watching how they work, asking them questions and tracing entire processes end to end and documenting them. When you do that process many times over many years, you understand the gap between how we think our business works and how it actually works at the front line. It can be two very different things.

It actually links back to the decision-making challenge around how we execute AI transformation, because what I see as one of the biggest value-adds of AI agents is that sunlight exercise: the requirements gathering, the configuration validation, mapping the plans and documentation (or lack of documentation) to what actually exists in our operating model today. This exercise is really important.

There’s this gap between tacit knowledge, which lives in people’s heads, and explicit knowledge, which is written down in documentation. A lot of that gap is the IP or competitive differentiation that enables firms to deliver value to their customers.

One thing which AI helps us do is close that gap at a much faster pace than having a team of business analysts come in and do interviews with everyone. They can still do that, and in fact, if I was tasked with setting up a requirements-gathering exercise today, I would be leveraging AI tools by doing things like recording all the transcripts and feeding them straight to my preferred AI agent to synthesise and tease out all of the things to make sure nothing’s been missed.

Why? Because context is everything in the AI era, and anything that isn’t accessible to an AI agent is something that you can’t use to optimise your operating model further.

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Data quality matters

Having worked in a data analytics team and having a lot of personal experience with data cleaning, data scrubbing, data processing and figuring out how to actually get golden-source data into a beautiful dashboard for stakeholders, I care about data quality.

I know that throughout the corporate world, and the business world in particular, data governance has been a buzzword as opposed to how people are actually operating their businesses.

This is one thing which I’ve written about a lot on Getting AI To Work, but data quality matters so much because if an AI agent is looking at the wrong piece of context, or has an inaccurate idea about what different data points mean or the value they have in optimising different processes or delivering specific bits of value to customers or complying with specific regulatory or legislative requirements, you can have really unfortunate consequences from not taking it seriously.

What are AI agents doing in this aspect of thinking about AI transformation? Well, AI agents can help you do those data quality deep dives and build out data remediation plans at pace.

You need to have a lot of testing, quality control and business expertise sitting across these processes, but to my mind this is yet another example of how, when you’re doing these transformations, a lot of the tasks which may have needed to have been done sequentially can now be done in parallel.

At the same time as doing all your deep diving on your operating model, you can kick off data quality interrogation agents to go and look at things and understand: how are things flowing? Where do you not have those business definitions? Where are the blockers in your data ecosystem that could lead to inadvertent AI agent mistakes?

Ownership and accountability

A lot of organisation charts have evolved over time and constantly get restructured or reorganised based on how leaders want to manage things and achieve other objectives through the stock-standard corporate approach of disestablishing roles, moving things offshore, or changing who reports to who in order to achieve the outcome they personally want inside the organisation’s goals.

Now, you can see from the attempts of the frontier AI labs to not really talk about the liability for actions of AI agents that, in corporate environments, making sure that someone is accountable for every single thing that an AI agent does in your business is quite an important thing.

There’s also a problem where that gap between what AI agents can do well, can’t do well, and need human help or help outside of the firm needs all of the right governance controls sitting around it, so that you know and can understand to the best that you can inside the volume of work that is now going to be done in these AI-first operating models, where things are happening at a high and detailed level alike.

Because when things go wrong, and they will go wrong, rapid remediation is now an expectation from external stakeholders. Sitting around for weeks and months doing a response to a regulatory incident isn’t going to cut it. People now expect the speed of AI disaster to match the speed of AI-enabled response and recovery.

So this ownership and accountability problem is a serious issue because, in my opinion, you need both the right business knowledge and technical knowledge. It’s no longer good enough to be delegating AI-related things to technical people.

One way to think about it might be: the most successful business leaders over the coming years are going to be the most AI-native ones, who have leaned into this technology to apply their domain knowledge and expertise to get the leverage and have the ownership over the overarching quality of every component of that operating model they are responsible for.

Determined machine problem solvers

Why do AI agents serve as auditors so easily? Well, if you give a human worker a task, they are going to route around the gaps in knowledge through doing the work. They’re going to go and ask people they know. They’re going to ask people they know who else they should talk to. They’re going to look at a system and know from their own experience the documentation and some basic research: what’s working, what’s not working.

The difference is with AI agents, they will not route around anything. They will either get stuck or complete what they’re asked to do. If they are aggressive and goal-maximising, they will find things which people are shocked and surprised about.

One thing I’ve found useful in my work so far in this AI journey is seeing the low-level things that get surfaced when AI agents operating at speed to achieve a goal run headfirst into the blockers. I think about times in my corporate career when low-level issues were sometimes only surfaced after enormous amounts of effort.

The fact that we can now just ask an agent to audit an entire database and prepare a detailed report is useful. We still need the experience and skills to judge and assess the output.

The flip side of having all of this is that the logs and audit trail are now evidence. So if someone in your business asks a question and finds something that requires regulatory disclosure or a chat with the auditors, more companies are going to find themselves having to do that.

From a wider societal perspective, this is probably better for increasing quality and compliance across all manner of industries because AI agents will start to surface things that even the best human experts haven’t considered over many years and regulators would love to hear about.

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Avoid failure modes

So what I’ve seen myself is that when I’m working through these sorts of tasks, the first run doesn’t really run into model limitations. It’s almost always context and access. So the key part of getting this investigative and exploratory side of an AI transformation right is around getting things set up properly and making sure you give things access.

I believe that a lot of pilot projects and proof-of-concept projects are completely set up to fail because they’re given such a narrow-scoped set of things to focus on that the wider value of AI isn’t being leveraged.

There are so many things which are really interesting to me, especially things like system misconfigurations, process steps that people have forgotten about or aren’t clearly documented, and handoffs that only work because there’s tacit knowledge about what needs to be done when. People haven’t stepped back and thought about end-to-end processes and how they should actually be working.

So there are a lot of issues in existing operating models that we might have dealt with previously through process improvement, continuous improvement, digital transformation, or however we might want to label these sorts of exercises. But all of these things are still present when we are thinking about doing an AI transformation. It doesn’t just stop.

So is this all just a rerun of “garbage in, garbage out”? I don’t think so. It is true that bad inputs have always produced bad outputs, but there are some things about AI and its capabilities that make it a little bit different this time.

Why it’s different this time

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Today I’m going to talk about an incident where an influencer partnership Xero had in the UK led to a backlash from accountants. I think this isn’t actually a clumsy marketing mistake, but it’s exposing why software vendors have a lot of opportunity in the AI era, and intermediaries like accountants and other professionals face enormous risks.

This influencer claimed she could save £800 a month for management reports that previously an accountant performed. This was via a read-only Claude MCP connection to her Xero account. This use case is just the tip of the iceberg in terms of what sorts of things business owners who pay accountants lots of money will choose to do themselves.

I think that a lot of the initial reaction shows a lot of accountants haven’t thought through the strategic risks they actually face from AI disruption. Just because the current tool is read-only doesn’t mean that other AI tools that exist today can’t use browser use and computer use and access to APIs to do many more things that they thought they might be safe from disruption on.

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This week’s newsletter is a little bit shorter than usual. You see, I’m getting increasingly disappointed at slow AI adoption.

There are clear reasons why this is the case. Business owners a…

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Everyone wants to talk about how they’re using AI, yet much of the evidence we see from companies applying AI in its public form is AI-generated LinkedIn posts.

In today’s post, I want to share how I think about AI process improvement and what I think a lot of firms are missing when they select AI use cases without first taking a step back to reimagine how those use cases fit within an AI-first operating model.

An operating model is just a concept of how a business is put together. It’s about how people, processes and platforms roll up into capabilities that enable a company to ship products and services to its customers.

This AI-first operating model perspective focuses every single part of operating model change on reimagining how everything is done as if you were starting from a blank whiteboard with the power of not just the best AI tools as they exist today, but what they will be like in six months’ time and beyond.

An example of the old way of approaching operating model change is how companies traditionally approached outsourcing. Let’s say they have a call centre with 500 employees. They know they have to pay for all of the systems and processes and controls and audits around operating this call centre.

The traditional way of doing this is to get a consulting firm in and get them to map out how the call centre currently works. Then get them to do a market scan to see if there are outsourced call centre providers who can replicate that capability, meet all of the different quality and cost targets that we want to achieve from this operating model change, and then give us back a nice pretty report and execution plan.

For us to go to the board and say, we want to outsource our call centre, here’s the plan, and “brand name consulting firm” helped us put this together. Can we get this spend of $10 million approved because we will save $15 million a year ongoing if we outsource this?

A more cynical take might be that the old way of thinking about operating model change involves a lot more predetermination and constraint in terms of reference and assignments of work to arrive at the preordained outcome of, oh wow, we have to outsource or offshore this thing that we’ve wanted to do for ages, and here is all the evidence to back that up, and here is the concrete plan for us to execute it, and look here, the consulting firm that helped us do this just happens to have a preferred outsourced call centre “strategic partner”!

The AI-first operating model change is a little bit different. When I talk about starting at a blank whiteboard, I’m serious. You want to be imagining how you would rebuild the capability you’re trying to rebuild. In this example, a call centre, and whether that is even the right boundary of capability you want to be rebuilding.

What are the jobs to be done? What is the right blend of human and AI agent? What are our customer expectations around the sorts of things they can just do self-service and the sorts of things where they do need to speak to either a human customer services person or an AI agent? What are all of the regulatory challenges we face around voice interaction with customers?

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Over the last couple of days, I’ve been researching something you probably think about when you hear corporate leaders talk about what they’re doing with AI, but may not have examined in detail: what’s going on under the hood.

When a CEO claims on an earnings call that 10,000 people are being laid off because of investments in AI, that’s just the surface-level proclamation. When you look under the hood, what is often happening is that the 10,000 number is made up of people who were going to leave anyway because of retirement or churn, the movement of headcount from a big corporate to an outsourced vendor, or even an FTE-equivalent number made up of hours saved from processes that started getting automated years before the ChatGPT moment.

Every case is going to turn on its own facts, but over the last few years, before AI model capability could even justify some of the claims being made, it was clear that trying to label every cost-cutting measure as AI-linked would be rewarded by capital markets.

My take that I’m going to unpack in this article is that there are three things going on here:

1. AI capability is very real and already delivering results

2. The evidence is weaker for AI already causing large-scale job losses

3. There is an AI capability-to-deployment gap, not fully explained by culture

That is to say that deploying AI yourself and deploying AI in an enterprise environment are two very different things. There are all manner of considerations that a business needs to worry about: provisioning and permissions, how AI tools integrate and connect to other systems, and what sort of evaluation and testing processes are in place.

When you read an article that claims AI does work or doesn’t work, this is so simplistic that it’s essentially worthless if you’re a business owner or leader thinking about how you actually get AI working in your business.

Every operating model has so many moving parts. That is why, when I talk about an AI-first operating model, I talk about the need to rethink everything in it. This is all happening in an environment where frontier AI capability has moved much faster than the ability of a business to take that upgraded capability and turn it into reliable workflows and use cases that are actually delivering value.

Only the companies that care the most and have put in the most effort are getting these rewards. A lot of companies were already behind on the technology journey. They are suddenly finding themselves blocked or restricted in the number and quality of use cases they can put into production.

AI essentially acts as sunlight and brings all of the poor decisions and underinvestment of the past to the surface. It is this environment in which things like AI washing or attributing cost savings to an AI project when it was actually a plain old automation project can start to be the response to incentives you see from some corporate leaders.

What are the variants of AI washing?

There are several different subtypes of AI washing to consider. These are substitution washing, reallocation washing, bundle washing, denominator washing, forecast washing and vendor washing.

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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…

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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.

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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

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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.

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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.

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3- The playbook for successful AI change

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