Even with unlimited resources, it’s still possible for any organisation to build and scale the wrong things if the underlying contradictions are not first addressed. Even at Google.

Contradictions are like two vectors in your business that cancel each other out

Contradictions are a really big problem in most businesses. Because businesses are systems, not automatic machines, there’s usually a few contradictions at play between departments, or between the business and their customers.

Sometimes, these contradictions are hard to spot, and everyone ignores them. We just kind of build around them, and on top of them. This creates a lot of internal friction, and poor customer experience.

AI make contradictions in your business worse by providing the means to scale the wrong things so there’s more friction, not less.

Most businesses I’ve worked with have one big thing holding back their growth. Everything else is noise.

If you can find your own internal contradiction, it can unlock growth better than any other initiative. That has been my experience.

If you want to grow your business, it’s a good idea to start by first looking for one constraint that once unlocked can accelerate growth and reduce costs.

This is most often in the form of a contradiction at the heart of every business.

The Fundamental Constraint

Most businesses are held back by one thing that creates friction everywhere else.

• This constraint is usually based on an oversight.

• The oversight stems from an assessment that comes from an insider's perspective.

• It's a natural occurrence in many product-based organisations.

As an example, let’s talk about Google Maps.

You might think that one of the most intelligent organisations in the world might avoid the trap of internal contradiction.

Yet it’s often intelligence itself that blinds people pre-disposed to see the world a certain way.

I have spent countless lost hours driving on Irish roads when I could have taken a more direct route, thanks to a fundamental constraint built into Google Maps.

The problem is a psychological one, not a technical one, so it never gets fixed. This glitch in the system costs Google Maps millions each year building and engineering around it.

Fixing it would mean diverting all those engineers to a task that’s only relevant to weird European outlier cases - and at the end of the day, that’s not important enough.

Can you guess what the constraint in Google Maps is?

The one constraint: Google Maps thinks every city is like an American city, and every road has a backup road.

• In the USA, many cities were built from scratch on a grid system because there was little existing infrastructure a few hundred years ago.

• In Europe, including Ireland, streets are often built on top of ancient roads and paths, resulting in a non-grid layout.

If you’ve ever tried to follow Google Maps around Ireland, you’ll know all too well how this becomes a problem.

Google Maps really doesn’t like U-Turns.

I ran a deep research report in Claude.ai to validate my assumptions. Turns out, I’m not the only one Google Maps is sending all over the country:

The research:

Google Maps performs worse in European cities with medieval street layouts. The data shows measurable differences:

• Grid cities have 41-43% route efficiency vs higher inefficiency in organic cities

• European users consistently report needing workarounds and backup navigation

• Academic studies confirm grid cities offer 13x more routing predictability

According to my AI research report: “The real constraint isn't algorithmic bias - it's algorithmic-topology mismatch.”

“Google's priorities (traffic optimisation, road hierarchy, legal compliance) work brilliantly when applied to predictable grid networks with parallel routes and clear hierarchies. Apply those same priorities to organic European cities with medieval layouts, limited parallel options, and complex legal restrictions, and you get the exact frustrations you experience.”

Google Maps will divert you miles out of your way to find a road that doubles back onto itself.

It’ll also send you on a road across the top of a mountain on the way to County Clare because the road it favours is technically listed as a national road.

If you were a local giving directions, you’d know that the road Google Maps just recommended hasn’t been used in 50 years, except by bandits and lost Americans.

Welcome to Ireland, watch out for the sheep…

In the USA, if you miss your turn, there's usually another street that goes the same direction - like having multiple paths through a neighbourhood. So Google Map's ‘brain’ says "no problem, just take the next street over."

What happens if you’re on a road in Ireland where there is no grid, just a straight road for 10 miles until the next turn off? And another 5 mile road before you have an opportunity to point your car back in the direction you want to go…?

In this case, you stop, switch off Google Maps, swing your car around in the opposite direction, then switch Google Maps back on. That’s how I do it anyway.

The constraint isn't about grids - it's that Google assumes you always have choices when sometimes you don't.

The Real Constraint: Optimisation vs. Innovation

Google Maps doesn't assume grids exist - that would be stupid.

It’s a bit more complicated than that - it assumes grid-like benefits exist everywhere.

They’ve considered the trade-offs, and have decided that as long as roads are ‘mostly’ grid-like, then designed for that.

Google Maps algorithms optimise for multiple parallel routes, predictable road hierarchies, and standardised intersections. These assumptions work brilliantly in planned cities but break down in organic ones.

Like Irish roads covered in sheep poo 💩.

Many businesses do something similar - they apply their view of the world to their customer experience, and assume they’re the same thing.

Optimisation isn’t the same as Innovation.

Innovation is Disruptive.

Google Maps would need Innovation to fix the problem caused by their internal contradiction.

But they’re focused on Optimisation, so I keep getting sent down windy roads when I’m trying to find the train station at Ballybrophy.

Insider Blindness

I’m not a road designer, nor do I know what the technical term is for someone like that.

But I’m guessing the problem is the people, not the system.

My theory: Google's engineers solved navigation for the world they know - Mountain View, Seattle, Phoenix.

Cities with:

• Multiple route options for every journey

• Clear arterial → collector → local road hierarchies

• Intersections that follow patterns

• Legal frameworks designed around car infrastructure

They built a universal solution for a non-universal problem.

Why This Matters More Than Grid Bias

Google didn't just miss European street layouts - they’ve missed that navigation itself works differently when:

• There's often only one viable route between points

• "Main roads" might be 800-year-old cart paths

• Legal restrictions change by medieval city quarter

• Road hierarchies follow historical accident, not planning logic

• Desire paths on Irish roads are fun for some trips, but not great when you’re in a rush…

The Real Story of Google Maps

Google Maps works perfectly - if you live in a world designed like Silicon Valley. For the billions who don't, every journey becomes a negotiation between an algorithm optimised for choices you don't have and infrastructure that follows logic you can't code.

Google AdWords circa 2013.

This isn’t the first time Google let an insular perspective create friction in their business.

When I was engaged as a consultant to look at content delivery systems for Google AdWords globally over 10 years ago.

Email marketing campaigns involving over 60 million AdWords customers were getting all jammed up because the content producers couldn’t get their copywriting and design approved.

The backlog was reaching critical levels.

In this case, the answer to unlocking growth lay in a contradiction; one contained in brief conversation I had with the (tiny) Google AdWords marketing team in Mountainview.

It was so subtle I almost missed it.

I was talking to the smartest people in the room, and they just couldn’t understand why nobody else could understand how to write as well as they did…

‘Good question’, I thought.

I played the conversation back in my head on the plane on the way home. Over and over.

Suddenly it struck me.

The answer was in a contradiction.

Nobody could write like they did because they weren’t them.

Google was paying millions of dollars to people all over the world to write copy for marketing emails sent out to over 66 million AdWords customers.

All the copy kept bouncing back when Google leadership got it and rejected it.

Why? Nobody knew.

Even the people rejecting the marketing copy didn’t know why it was bad. They just knew it was ‘bad’. Not why.

The solution was sitting right in front of them. I saw it because I grew up in Tipperary, not California.

The one constraint holding back their growth and costing them millions in failed iterations and content cycles seemed obvious in hindsight.

Here’s what I figured out. If you want your marketing copy to get past the gatekeepers at Mountainview, pretend you went to Stanford.

That’s it.

When I returned and wrote copywriting and design briefs with this as the heading, everything flowed:

“Write it/Design it as though you went to Stanford.”

Google AdWords email copy sounded like a Stanford graduate: Clever, clipped, smart, funny and insightful.

This one insight unlocked a flood of marketing communications and saved a fortune.

It was a people problem more than a systems problem.

It was a core contradiction holding everything back.

Why does this matter for your business?

Chances are you’ve got a similar ‘built in’ constraint that is inhibiting your growth, and you’re probably too close to see it.

Below are some examples from my own work with clients over the past couple of years where it helped to identify a core constraint before building systems to grow their businesses.

Find The One Big Constraint: Opposing Vectors Creating Stasis

Offshoring consultancy firm

The ONE constraint: They were running a high-volume SaaS outbound model (think BDR/sales reps making lots of cold calls) AND a high-touch consultancy sales process simultaneously - each system undermines the other's success metrics, creating paralysis.

Key to growth: We unlocked growth by decoupling their opposing sales motions, initially pitting them independently against each other to see which won (to satisfy internal skepticism), and then doubling down on the one with highest potential for growth.

Result: 2x revenue growth.

ECommerce brand

The ONE constraint: They were using marketing automation and ads that generated leads at scale (“too many leads”!) - AND a sales process requiring personal phone calls for every transaction. The better marketing performed, the worse operations failed. Customers were waiting, left unattended, thousands of euro left on the table every month…

Key to growth: We built an AI powered system that handled personal responses required for ‘5 minute response times’, created another AI system that bubbled up the most valuable leads as they came in so the sales team knew who to call and when, and allowed the longer tail of delivery and fitting to be handled on the other side of sales.

Result: successful exit for founder, 2x increase in revenue over 3 months.

Accounting Tech Startup

The ONE constraint: An early stage founder designed a billing model that rewards hours spent AND the business goal of automation/efficiency - resulting in a business model contradiction that incentivised customers to reduce their spend and for every improvement in efficiency to directly reduce revenue.

Solution: When we identified the constraint, we were able to prototype parallel go to market strategies for each business model to see which had the most potential for growth.

Result: 2x increase in customer acquisition, funding secured.

Leadership & Executive Coaching

The ONE constraint: Thought they needed more leads to grow the business, and were spending too much on marketing and content.

Solution: opposing business models, with an app on one side of the divide and premium leadership coaching for C-Suite executives on the other, were cancelling each other out. We focused the go to market strategy on high quality leads from peer groups and considered outreach.

Results: doubling retention rates, saving 70% on costs, and building a complete business transformation to a better business overall.

My own Journey Mapper/Intercom Business

The One Constraint: We started out selling Intercom’s self-service AI tools to support managers AND trying to help those managers' need to protect their own teams/budgets - in effect we were asking them to automate away their own importance.

Solution: for me, this meant a frank admission to myself of a fundamental constraint affecting growth for any professional services on top of AI - this is about Digital Transformation, not selling product. The only way to do it is top down, with a consultancy business model.

Result: I realised this one constraint was too big to climb over - and pivoted back to my own successful HubSpot agency, Mount Arbor. Meanwhile, Julian, my co-founder, found a way to prepare businesses for their next step towards AI by partnering with a leading diagnostic platform, Hubbl, and continues to grow the business in that direction.

Mount Arbor (My Current Challenge)

The One Constraint: I was building intellectual property for scalable products AND selling time for custom implementations - every client project prevented product development, every product push starved cash flow.

The Solution: I realised I was trying to create systems as a moat between me and my clients, rather than leaning into helping them make progress. I decided to meet demand where it’s at, and re-establish my business as a leading HubSpot Agency and expert in AI.

Results: Watch this space.

Some more examples:

WeSwitchU (energy cost saver business):

The One Constraint: Premium service provider positioning AND commodity price comparison platform - customers expect concierge service but shop purely on price.

The Solution: we reformatted their internal HubSpot systems to eliminate the noise and streamline ‘cost to serve’ - a key metric in a commoditised market.

Result: significant cost reduction and profitability increase, better systems to scale.

IT Services Firm (€150M revenue, sold for est.€100M last year)

The One Constraint: This company was perceived in Ireland as a reliable/safe government contractor brand AND wanted to be seen as an innovative/transformative service for private companies - low risk AND high risk. The very attributes that win public contracts repel private sector buyers.

The solution: We were able to show the boardroom team and management where the internal contradiction was causing friction and failed marketing efforts.

Results: We helped to clarified the thinking and strategy around the core constraints affecting strategy and marketing overall. The CEO’s summary of our findings says it all: “F*ck ya! You’re right! How did we not see it?!”. He realised that spending more wasn’t going to solve the underlying problem - resulting in the subsequent acquisition of this company for a reported 100 million euro.

Uncover your own Business Constraint

If you need a little help digging into your business to find any internal contradictions you may not see but might be costing you growth, feel free to reach out. It might help to have an outside perspective!

https://www.mountarbor.io/

What does running a business mean if most of the WORK is done before we get to the office?

Automation by AI of everything ‘automatable’ is entirely possible.

Creation of new things by AI is debatable, but still possible.

In my work with creative business owners exploring genAI over the past couple of years, what stands out is just how disruptive AI actually is to existing business models and processes.

I've noticed from several deployments of AI in enterprise businesses that even while good AI use scales marketing, sales and operations beyond anything previously imaginable, there's a pesky human problem acting as a Gremlin in the wires.

The problem with most humans is that they don't really know what they want.

And they definitely struggle with expressing it.

If you ask AI what your customer said they wanted, AI will tell you exactly what they said. Advanced AI with NLP for voice analysis might even allow you to pick up on the nuances of what they might have meant.

Where AI will struggle, just like we all do, is knowing what people really want.

• What are they lying about?

• Where are they unaware of the causes of their struggle?

• Why now? Why is it important to solve their struggle today?

Businesses are there to help people with specific struggles, and they’re hired to remove that struggle so the customer can make progress. That’s pretty much it. Everything else is extra - it’s good extra, but it’s still extra.

The more vague those customer struggles are, the more costly it can be to scale the wrong systems and processes with AI.

Telling AI to “just go ramp up the system” results in a Frankenstein's monster of overlapping, siloed, supply side junk, while the essential elements remain hidden and sub-optimal for every customer.

Scaling the wrong things has always been a problem in business, it's just getting much bigger.

I’ve come across the problem of scaling the wrong insights over the years on a minor scale, before AI came along to magnify everything exponentially.

I helped businesses to scale systems and processes in different capacities and layers and roles - from Marketing down to Ops, it was always clear that essential elements that helped grow the business were usually simpler and cheaper than the costly noise of systems, people and status quo thinking that was surrounding them.

Over the last two years, I’ve used AI creatively to identify and scale the right insights to achieve the following outcomes:

1. one business owner deployed a genAI sales pipeline and lead handling system that resulted in a GBP2.5 million exit (6 month project)

2. one business owner applied a gap analysis I compiled followed by a systematic dismantling and restructuring of ‘supply side’ people, processes and communications to add 40% revenue YoY, adding 7.5 million in additional revenue over the next 3 years (3 month project)

3. another business owner repositioned around customer insights that showed that clients were lying about what they really hired for - resulting in an 80% cut in content and marketing costs, and accelerated growth (6 week project)

The businesses growing exponentially fast with AI are those who are most specific, deliberate and streamlined about what their customers want.

We're only beginning to see how SaaS, Service and other business models are being systematically dismantled by AI. It's still early days. Everything changes when the work changes.

One of the best funded, most flexible and focused 'startups' in AI is Intercom, or more specifically, FIN.ai.

As a certified partner and onboarding specialist for Intercom’s advanced AI technology, I got exposed very early to what is emerging as the fastest growing and best positioned AI agent platform in the market.

I’m also certified in Salesforce’s Agentforce technology, HubSpot’s Breeze AI (where I’m a Gold Solutions Partner) and other independent platforms like Make.com, bubble and integrated solutions in Airtable/Notion.

Last year I started a business with a co-founder to partner with Intercom as an implementation partner for its nascent AI platform, which back then was just a component of the existing Helpdesk platform they offered to customers.

Our bet was that just like every other SaaS company we’d ever encountered, they’d inevitably need partner support to capture the overflow for rising demand as they grew and struggled with deployment, onboarding, customisation and so on.

This was a bad bet. Not a terrible one. But like many businesses floundering in the face of AI and the associated business model disruption it brings, it challenged us to think very deeply about value.

Cookie cutter businesses do not survive in the face of AI.

In our case, we struggled to find a gap for professional services in deploying AI when FIN was sold as a simple plug in solution that got up to speed pretty quickly on its own.

As a partner, we were also competing with Intercom’s own professional services team, who were fully optimised for 10x productivity with AI.

Last week, Intercom announced a $1 million guarantee for anyone buying Fin.ai.

This is exciting and devastating in equal measure.

As an Intercom solutions partner, this is what that sounds like to me:

1. Fin.ai will work. If it doesn’t we’ll give you $1 million

2. If after you have either made FIN.ai work yourself, or worked with our internal team to make it work, OR refused a million dollars when it doesn’t… then you might want to work with a solutions partner, who can help.

The partner model just got chucked out the window with the old photocopier.

Bye bye.

So what do we do now?

Well, luckily we were flexible enough to adapt, and saw the writing on the wall around about December last year when I stepped out and my co-founder carried on.

Right now, my co-founder is dismantling the new new new business landscape, and continuing to run the business we began as a successful and valuable partner to anyone using Fin.ai.

I’m focused on consultancy projects, working on building out successful case studies with AI, and working with diverse partners and platforms to deliver value anywhere I can.

I’ve learned a lot on this journey over the past year and I thought it would be worth sharing.

It's telling that Intercom recently split out FIN.ai as a separate thing, and chose to focus on:

1. Knowledge

2. Behaviour

3. Insights

4. Action

What this means is that FIN.ai is finding product/market fit somewhere other than where Intercom originally positioned it.

AI is bigger than we might have thought, and it’s got very little to do with help desk technology.

Customer Support is just an entry point and the most valuable, logical sandbox for establishing new business models and astronomical growth for an ambitious startup.

See link: AI growth re-acceleration at Intercom

If Intercom are leading the race to enterprise deployment of AI across the world, have the world’s leading AI agent, and are prioritising not just AI but the surrounding management layer around the use of AI… shouldn’t that tell us something about the importance of creative nuance and understanding in business?

If you look at the layers of abstraction between AI just doing what it does, and the creative management of that genAI processing and layered threads of AI deployment, you begin to see the pesky human problem cropping up in each of the 4 layers being developed at Intercom.

This layering is also embedded in the evolving AI platforms in use at Glean.ai, Agentforce/Salesforce, HubSpot/Breeze AI and independent AI stacks.

It helps to break apart the layers to search for the human, creative core:

1. Knowledge: all knowledge that ever existed is (or will be) available to AI, as long as it’s been published at some point. However, as we discussed earlier, discovering what people want is almost imperceptible sometimes. It certainly doesn’t live in most CRMs or Google Docs folders. What people want isn’t in most datasets, and never was. We’re guessing what people want most of the time, so we use intuition, guesses and good strategic management to compensate. If you ask a customer what they want, they’ll tell you what they think they want, and most definitely won’t tell you what they actually need. That’s where good business leaders make choices. Strategic decisions have no right or wrong answers… that’s hard for AI to comprehend.

2. Behaviour: people behave great in a good system. Most systems are bad systems. People hate bad systems. AI doesn’t care. Yet bad systems scaled by AI, or even made more efficient by AI, are unimaginably costly. People’s behaviour is tricky. People can be weird. Weird costs a lot in a scaled system. If you’re anything other than an established, incumbent business with everything locked down, it’s even worse. Good business leaders compensate for behaviour of their customers by adapting to obvious human signals and behaviour that may not be so obvious to AI.

3. Action: the idea that a business is a machine makes a lot of sense. It’s especially appealing to engineers. If this, then do that. But engineers will often ‘build the product right, but not the right product’. Agentic systems designed to take independent action works as long as the actions taken fit the bill. Actions based on insights performed by agents are probably the safest area for scaling existing processes. Most businesses will have a general idea of their repeatable processes. By then the problems the business solves have probably been framed correctly. But not always. Passing Knowledge and Behaviour through the filters of common sense usually leads to the correct action. So AI agents are probably in safe territory here. But safe isn’t that creative… what else could we do?

4. Insights: Insights are not data. AI will guess at what’s most important for you to know in order for you, or an adjacent AI agent, to decide what to do with the newest available dataset. Great! In the future, we’ll learn a lot from this unprecedented knowledge and understanding of what exists in our business. At the core human level, we’ll learn very little new, or exciting. Without curiosity, ingenuity or desire, insights can come out as more noise and more productivity measures. Once the noise and productivity has been taken care of, what insights will lead to more creativity in business? What insights will inspire?

Most debate around AI occurs at the process or people level - can or will AI replace humans in systems, processes and experiences. Where will that happen and when?

The artists are always the ones to watch when it comes to revolution. Whenever I'm lost, I turn to art.

I'm a bit lost right now, so art is helping me make sense of it all.

In this piece by a working artist in the NYT, Christopher Niemann shares in the most beautiful, creative and visual way his own experience of AI and the implications for his own creativity as an artist.

If AI allows creative business leaders to create anything that has ever been created easily and quickly...

What do you want to create next?

When a purchase is being considered, the decision to buy is based on a weighing up of all the options against each other, including the unique aspects which serve as a counter-weight to the standard.

Being Unique in order to sell things and make money

In business, a USP (Unique Selling Proposition) is the way companies define their difference in a digestible way for buyers. USP helps people to decide to buy one product over another. In employment, a person’s ‘USP’ could be considered the deciding factor on whether to hire one person over another.

Your Unique Selling Proposition is an acceptable way to present your difference in the context of the standard. It’s a way to politely suggest that you’re safe and reliable and familiar, but with an edge.

Your USP is a sprinkle of difference that says you’re going to do what’s expected, and then go beyond in a meaningful way. The further beyond you go, the more valuable your USP.

There are certain prerequisites for establishing your USP. Among them are making sure to show comparable solutions, reasons you’re different, and when or where you’re generally useful.

Being unique on its own isn’t enough. You’ve got to be unique in comparison to something else.

A unicorn is unique, just like aspects of a person, product, or company are considered unique because among all other things, nothing like it can be found in the whole wide world.

If we weren’t sure that nothing else like a unicorn exists, then we wouldn’t know it was unique, would we?

Uniqueness is a relative state.

Let’s take socks as an example.

If I’m shopping for a pair of socks, they’re all pretty much the same… except this pair, oh this pair, their lined with water resistant plush felt and toe hugging patented heat technology, so, you know, these are the socks for me…

Meeting the standard, then exceeding it with a dash of the unfamiliar, has always been important in establishing value. Commodities can be bought from suppliers in bulk, and suppliers can be swapped in and out of a supply chain, because a USP has less value in a commodities market.

Price is often the biggest factor in buyers’ decision making process when it comes to buying the same thing from one supplier over another.

You don’t really need a USP in a commodities market, other than price and a few other variables. I’m not going to go into commodities markets, because they’re so boring and I know very little about them. For now, let’s assume you don’t want to be a commodity.

Why you need a USP, even if your thing is pretty standard

If you’re selling services, premium products, technology or anything else that requires you to establish a value considered by your buyer as beyond the standard, then you need a USP.

In the technology or software market, for example, companies sell more based on how they compare to their competitors.

If a software product differs enough from the next best option, and exceeds the standard expectation as a result, they can charge more for the additional value that particular difference brings.

Your USP describes this difference in ways that make sense to someone ready to buy.

Being different is important in a market where buyers consider more than price when they choose a supplier.

AI MAKES YOUR USP DYNAMIC AND IT CHANGES ACCORDING TO THE CONTEXT

When A.I. is responsible for your Customer Experience, what the customer experiences at any moment will depend on the person affected, the brand delivering value, and the USP which are all dependent on the context.

When A.I. can dynamically represent your brand and offer customers exactly what they need in their ‘struggling moment’, the USP, the brand are actually variables (not fixed) which can change dynamically depending on the person interacting with the A.I., how much you know about them, and the context.

A.I. makes everything fluid, even the things that were fixed before like your brand and your USP.

What is context?

The context is the space, time and circumstances of any one exchange of value involving any one person at any one time.

Being unique is invaluable when the context is known and understood.

We usually pay more for products, services or employees who meet the standard but exceed what we’d expect from the average.

If we can clearly show how we are unique, not just different, then we can usually charge more if we want because we’ve established that our thing is rare and thus price it accordingly.

A.I. makes it possible for systems and people to be presented with a unique assembly of a dynamically assigned ‘Unique Selling Proposition’ based on the timing of any interaction with a known customer. Your USP can remain the same for every person, but it can also be different for each. Up to you.

This complicates matters when your USP is set in stone.

It opens up the possibility that you can set the criteria for your USP to be dynamically created according to your own unique ideas of what you’d present to a customer or a person if they were in a particular situation, dealing with a particular struggle, at any particular time.

The whole customer experience can rotate around the customer, dynamically changing in real time to provide a customised, unique experience dependent on the moment, the person, and what progress they’re trying to make.

Can you imagine?

Pricing will be dynamic too.

Price is a variable under the control of the supplier, and it can be used in strategic ways to position a USP more competitively in the market.

For the last 20 years or so cloud software companies (SaaS) have often charged less than their competitors for their USPs to gain an unfair advantage in their market.

SaaS companies can distribute their costs of delivery across their customer base over time, in order to offer something revolutionary. They can be disruptive very quickly in a large market of buyers by applying an ‘economy of scale’ to their products and services.

Salesforce can offer billions of dollars of software to any individual for 49 bucks a month. That’s been the theory up to now anyway!

This approach to pricing and scaling cloud software has allowed thousands of SaaS (Software as a Service) companies to scale and win more customers very quickly; not only because they were superior to their competitors but also due to the lack of any comparable alternatives offered at such a competitive price.

Balancing USP with price, the costs of acquisition and retention of customers, access to your market, the speed of delivery and the costs to serve are what’s generally called ‘Marketing’ in most businesses.

There’s more to Marketing obviously, and this is where Sales and Finance and Customer Success and everyone else can get upset about ownership of such things…but can we agree that USP is in there somewhere?

Hiring to get the Job Done

When you ‘hire’ a superior product or a person to get a job done, hopefully they’ll do the job better than anyone or anything else you could have chosen to do the same job.

Buying things is usually a matter of weighing up whether the thing you buy will get the job done, balanced against how anxious you feel about change, sticking with what you know, and the ‘pull’ of anticipation should everything work out as you’d hoped when and if you commit to the purchase.

Buying is complex - way more goes on than you’d think.

(Note: for more on this, please refer to Bob Moesta and Clayton Christensen on ‘Jobs to Be Done’ theory).

The value of your USP in sales

As a buyer, when you decide to pay for something unique, something with a clearly expressed USP that you value, your level of expectation increases depending on how much you’ve paid for the difference.

You’ll happily pay more for the USP, as long as it’s what gives you additional value at the end.

The USP provides that extra bit that you weren’t expecting or that you can’t get from anywhere else.

When buying or hiring anything or anyone to get a job done, a Unique Selling Proposition (USP) seems important, even if it’s just a slight difference to the next best thing.

Being unique, especially with the rise of AI and Generative AI. is becoming more challenging to articulate these days, because the capabilities of A.I. are so vast and different from anything before.

Some ways you can think differently about what you create with A.I.:

1. The future requires a shift towards embracing and expressing uniqueness.

2. Our role as creators is to be more chaotic and expressive, allowing AI to assemble our unique aspects for specific contexts.

3. The equation for the future is "Standard fed to Unique to output Unique," emphasising the importance of uniqueness in a world dominated by AI-driven standards.

A.I. adds to the heap of ‘same’ on the Internet by cloning and copying anything that can be described or referenced as a template online.

With minimal overhead, your winning ads, your marketing, even your products can be multiplied and replicated until your stuff is just more stuff in a wash of stuff that your customer is subjected to whenever they pick up their phone.

This makes whatever makes you unique (your USP) more valuable as a result.

I wanted to share some ways I’ve learned to look at the things that make us different, particularly in the context of business and marketing.

What’s wrong with copying something that works?

There’s a lot of value in being the same or just slightly different to your competition. Originality isn’t the only goal. Today I read about a 5 person business that reached $5M ARR by copying competitors’ ads and harvesting their marketing ideas.

This approach leverages A.I. agents for an acceptable threshold of plagiarism by getting A.I. to circumvent direct rip-offs so it’s possible to deploy working models and grab customers in any market. Pretty slick.

Copying pays, at least in the short term.

It kinda stinks.

There’s something parasitic about using the ingenuity of others to enter and win markets they worked hard to define and lock in.

Marketers can spend years looking for product market fit. One of the outputs from that hard work are the ads that attract customers successfully.

Cloning successful campaigns to hack into successful marketing funnels and divert those same audiences to your own product is a formula for success. It’s a short cut to riches, and plenty will follow this track to make money in the next couple of years.

I’m not saying there’s anything wrong with it. The best art is copied, as Bowie said (I’m paraphrasing - he’d say it with style).

The bad news for anyone copying someone else’s marketing or products is that someone else will copy you too. It’s getting very easy to do. And someone will copy that person, and so on, until your entire market is saturated with a wash of repeated archetypes that numb your customer until they turn to something new.

Not to mention - it’s boring.

The commercial benefits of leveraging the cloning and agentic abilities of A.I. to recreate something that looks like it is working for another business has a shelf life of months.

As A.I. gets better and better, the half life of anything created using an existing template gets shorter - its value decays the more there is of something to take it’s place.

Once A.I. gets a hold of a pattern that it recognises that is worth recreating, the bots will furiously spawn replicants so numerous, so quickly, you’ll be lost in the mix before you know it.

Copying can’t really form the basis of a business unless you’re constantly iterating.

You’ll spend your life looking around for the next thing to feed your copy monster in order to keep making money. Who wants to live like that?

There is another way!

Keep an eye on the target, not the prize 🎯

Bob Moesta's insight that "contrast creates meaning and context creates value" highlights the importance of both context and contrast in establishing uniqueness.

The good news is that if you can manage the things within your control (context and contrast) then the things outside your control (cloning and copying your USP) won’t affect your uniqueness.

You’ll remain different in a sea of same!

Uniqueness in Isolation doesn’t work

In order to compete, sometimes there’s pressure to come up with something entirely new and different.

I’m pretty sure every single CEO I’ve ever worked with has said the following two things:

1. Our product is completely different to everything else

2. Our product is in a completely new category that we now need to invent

There were a couple of exceptions, but generally in my career I gravitated towards the more innovative founders and startups who were at the cutting edge of technology.

A lot of the CEOs I worked with were scientists, engineers or mathematicians who decided to apply their amazing brains to creating products and a business around their groundbreaking ideas.

Naturally they were inclined to focus on the novel, and generally they were bored by the mundane.

Honestly, so was I. I love new things. Old things that everyone understands can be intensely uninteresting.

But repeatable, knowable systems and processes are what customers gravitate towards. And that’s what makes money.

Faced with competition that can leverage A.I. to do the same things in a fraction of the time can send founders in wild and risky directions.

Be boring where possible

A business should always try to work towards becoming boring. At least internally. It’s more profitable that way.

A.I. can help you to be more boring.

About 3 years ago, I had a gut punch realisation that my own business was going nowhere if I kept reinventing the wheel on every single project. Sure, it was entertaining, but completely unsustainable.

After 6 years of cycling through client projects of every variety and hue, I realised I was close to burn out.

I needed to take a mature look at my business to see if there was anything I could do to stand up the things that I did on a regular basis on their own, or with help, so I could sell and resell a process that didn’t cost me an arm and a leg to deliver every time.

Being boring is less fun, but healthier.

You might as well have A.I. do the bulk of the boring stuff, so you can focus on having fun.

Reinventing the wheel can feel like the only way to stand out.

The idea of being different is somewhat redundant when it’s just you and nobody else.

Take job hunting as an example.

If I was to look for a job by creating a resume that reflects the complete and exhaustive picture of what makes me unlike anyone else in the world, warts and all, I doubt I’d get an interview. It would be too risky to hire me.

Better to find a safer candidate who looks like the others, with a manageable amount of risk associated with an understandable amount of quirks.

Hiring people has always been about finding someone who meets or exceeds the standard while being aware of the risk. Ideally there’s a mutual understanding between employer and employee about areas that need development. We can work with known risks.

Employers don't expect perfection but want no surprises. They support employees in reaching standards through tools, training, or additional resources.

Uniqueness in Products

The same principles apply to products. A product reduces risk by clearly stating where it meets or doesn't meet standards. This clarity is only meaningful when contrasted with other products.

The 3 things that make you special

1. Comparison: Establishing a standard requires comparison with other products.

2. Inherent Uniqueness: Every product, even a cloned one, is unique due to variables like hosting, delivery, customer service and the different ways in which your customers will interact with your product.

3. Embracing Difference for Value: Being unique is about knowing about the other ways your customers could achieve the same results you offer, and showing them in contrast to your solution in order to demonstrate the value. This is crucial for job hires or product offerings.

Lessons from the elders

I recently attended the Business of Software conference in Cambridge UK.

I was surrounded by some of the world’s best coders and product developers. I listened carefully as they talked about their 20+ years in the world of software development.

The sages in the room related how they’d built and sold their companies, how they manage their teams, how they grow their business.

A lot of wisdom was concentrated into that small theatre in Churchill college, built to house innovation and new ideas.

When A.I. came up, I noticed a mixture of unease and excitement.

Vibe coding was met with a combination of awe and indignation.

“What’s going to happen when all these people who don’t know how to code simply start coding??” said most coders when they saw how easy Vibe Coding is…

Fair question.

But skipping past that existential crisis, if you’re running a business or looking for a job or figuring out how to add value in any way in order to make money, you may need to pause for a moment.

Where should you focus for the next 20 years?

The three pillars of value led by A.I.:

1. Standardisation: In order to survive, it seems important to quickly figure out how to repeat your best recipes over and over in the fastest, cheapest way possible so you can scale, reduce costs and remain profitable.

2. Own Context: In order to compete, figure out how you can leverage A.I. in a unique way to meet customers in unique situations to solve unique struggles only you can solve so the customer makes the specific progress you’re being hired to provide. Note: This is classic Jobs to Be Done theory, and it is essential.

3. Invent: In order to win, what new opportunities lie in wait in the untapped potential offered by limitless coding ability and an uncapped power to create new things?

How do we make money if it’s getting so easy to reproduce?

The biggest challenge to workers and businesses today is working out the commercial aspects of value in the context of A.I.

Jobs, Products, Businesses, Companies all fall into the same value bucket. They’re just systems, processes, people or things hired to help someone make progress in their lives.

Whether it’s A.I. or you fixing a problem, it doesn’t really matter. At least, if it’s a problem worth solving. A customer will choose a solution that gets the job done and I doubt many will be that fussy about the mechanism used to do it.

Commercial models depend on the notion that people will pay if you help them out. Economics are built on this idea.

A.I. can replace a lot of what jobs, products, businesses and companies did before.

The value of intelligence is plummeting.

Automation scrapes out manual work and repeated processes from any business and gives it to A.I. which can do 10x the work in a tenth of the time.

If you think this is all science fiction, here are a few stalwarts of the past 20 years that are already broken because of A.I.:

1. Predictable Sales: outreach and sales processes that have worked for 20 years no longer work. Ask any BDR working in any SaaS company how they’re getting meetings today.

2. Digital Marketing Funnels: campaigns are being stolen, replicated, used and squeezed beyond recognition until they no longer work. How’s your ad campaign doing?

3. Hiring: Pick a job and send your resume to a robot who reads your A.I. application to screen you for an A.I. generated list of requirements pulled from an A.I. based analysis of a pile of job descriptions until the job has nothing to do with what you actually need and the candidate is bounced at the first hurdle.

I don’t know how it’ll work in the future, but it does seem to me as though the wild variety and randomness of being human and the particulars of each individual’s taste lead to a world where standardisation, conformity and systems become less valuable than an ability to adapt to the nuances and ambiguities of helping individual people in whatever weird situations they find themselves in.

Bridging unique human conditions with empathy and understanding that only fellow humans can provide in order to use A.I. to build and manage commercial applications seems like a good place to start.

TL/DR;

1. Value through Contrast and Context: People seek to hire options that meet standards but are different enough to add value. This value is defined by contrast with other options and the context of the offering.

2. Contextual Importance: The context in which skills or tools are applied, like CRM management for example, can vary greatly depending on the situation and current state. Progress towards the future state defines the value of any contribution to a buyer’s life.

3. The Changing Value of Knowledge: In the knowledge work field, the value of intelligence is diminishing due to A.I. My unmatched value in areas like HubSpot implementation or revenue operations is challenged by AI's capabilities, so I now need to focus on how I can lean into empathy, creativity, experience and common sense in order to remain relevant.

4. AI's Impact on Expertise: A.I. can meet or exceed standards in knowledge work, reducing the value of expertise. Anyone can now be considered an expert, but what sets them apart will be how they successfully apply that expertise to solve problems and invent new things.

5. A Shifting Value Equation: To offer value, I must amplify my uniqueness and understand the context in which I operate.

The Equation for Value:

Application of the Value Equation:

To establish sustained commercial value, focus on what you can control (S, A, U and C) so you can deliver competitive offers, products and outputs in whatever mode you prefer (as an employee, product, business, company, artefact, ad, art piece, whatever).

This equation applies to getting work as an employee, consultant, or selling products. It emphasises value for potential purchasers and aligns with A.I. and AI Agentic work principles.

Key Idea:

Dynamic USP: My USP must adapt to the context, emphasising both uniqueness and contextual understanding.

Moving Forward

I plan to:

1. Communicate Uniqueness: Clearly establish and communicate what makes me unique.

2. Understand Context: Reflect on past projects to describe their context and where I met or didn't meet standards.

3. Apply the Equation: Use this equation to emphasise value in various work scenarios, whether as an employee, consultant, or in product sales.

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Going to bed last night, my daughter explained to me that she is unique. She told me that no-one else is like her in the whole world, and that this makes her special. She’s 5.

Growing up, I remember how I would bump up against the grown up requirement to fit in. It was ok to be different, unless it was for something serious like an exam or a job interview. Then I’d shave a little off the unique shape of my character in order to fit.

I took a chisel to my idiosyncrasies so I could get good grades, or to get my first job. I’ve been filing away at the things that make me weird ever since - but it hasn’t really worked. I’m still different.

I am beginning to think it might be time to let the cat out of the bag. Maybe it’s time you did too?

A.I. makes being different your most valuable asset.

A.I. has aggregated the world’s knowledge and allows that depth of data to be deployed in an instant.

Knowledge, and knowledge workers, are not as valuable in the context of A.I. as they used to be.

Anything that has ever been created before can be created again by A.I. with increasing speed over the next few years. It’s easy and fast to recreate a famous painting today. It won’t be perfect, but it’ll look the same, more or less.

Tomorrow, your business could be cloned in a few minutes.

Originality is now a premium resource.

Perhaps whatever makes us special should be something protected, organised and deployed carefully if we are to maintain our value in markets disrupted by A.I.

The closest thing to defining what makes you special in business terms is your USP (Unique Selling Proposition).

Who owns your USP? When was the last time you looked at it? Has it changed?

USP is often something we think about at the beginning of a business, and then it’s kind of an afterthought. We move on quickly to competitive marketing, where our established difference is pitted against others in our market to win customers.

For a lot of companies I’ve worked with, the USP is assumed. It’s such a vital and original piece of the company’s origin story that it’s kind of forgotten about, or written in stone. Everyone quotes the USP and puts in the pitch deck at sales meetings, but few question it.

The Annoying USP Question: Are you sure?

I always had the annoying habit of questioning a company’s USP. I wondered aloud whether it was correct. As you can imagine, this annoyed many’s a CEO who hired me to ‘do marketing’ for their company. I didn’t mean to be annoying. Sometimes it was worth a conversation. Often we were able to uncover new markets by questioning the fundamentals of what made us different, sometimes shifting to an entirely new market altogether as a result.

When you boot up any A.I. platform today, what’s the first question you’re asked?
After you’ve experimented with generic prompts, what’s the first thing you’re asked in order to tailor the experience and outputs to you, specifically?

Whether you’re using ChatGPT or an Enterprise platform like Intercom to run A.I. for your business, you’re inevitably asked some variation of the following during your onboarding:

“Tell us about your business. Tell us what makes it special. Tell us what makes your customers special”.

Your USP is the first step to using A.I., yet few of us think about it very much once the marketing team has done a workshop, or whatever.

Since it’s the very first step to everything else you do with A.I., your USP is the one thing guaranteed to help you establish a meaningful difference to what everyone else does with the same A.I…

Look at the latest product releases from Intercom, for example. Building on top of their AI capabilities, Intercom are now racing to build new backend tools concerned with cornering, articulating and deploying the unique aspects of your business as you see them, so that FIN (Intercom’s A.I., built on Anthropic) understands how it might tailor responses and customer interactions to fit exactly to your unique business.

Standard vs. Unique: balancing the known against the unusual

Traditionally, businesses and individuals have relied on a mix of standard offerings with a touch of ‘uniqueness’ to sell product. Being distinct from the competition usually means having and promoting a minimal set of features, activities or attributes that are recognisably different, but not so different that you’re unrecognisable.

Same, same, but different.

What causes someone to buy is another story (see Jobs to Be Done theory on this subject).

Seth Godin defines a brand as a distinct promise that you’ve made to the buyer, that you’ve got to keep.

The value of a brand is:

“How much extra am I paying above the substitute? If I’m not paying extra, you don’t have a brand”.

Brand promise doesn’t just apply to companies or products. It can be equally important in how you sell yourself.

In job hunting, for example, candidates aim to meet standard expectations while showcasing unique skills. In his recent book “Job Moves”, Bob Moesta shows how "Jobs to be Done" theory helps candidates ‘hire their next job’ based on specific, unique criteria they deliberately assume before their search. With clarity, they are then able to identify standard requirements, match their unique attributes to suitable positions, and confidently identify and articulate where trade-offs can be made.

The Impact of AI on creative output

AI is amplifying the ability to meet standards at scale. This is also creating a phenomenon called "AI slop." Anything that can be done, will be done, at scale, with no end in sight. Especially memes.

Reid Hoffman's experiment with AI cloning LinkedIn in 17 minutes highlights AI's capability to replicate standard configurations quickly.

As AI evolves, reproducing standard stuff will become easier, affecting job applications and product development. It’ll become more difficult to wade through the ‘amplified insanity’ as Bob Moesta calls it. This is already happening in the job market, where every job description and every resume is sliding slowly to the middle as they’re processed through A.I., so nobody knows anything about the candidates, and job seekers know nothing about the actual job they’re applying for.

As well as exponentially increasing the amount of stuff out there, A.I. will contribute to exponentially increasing the amount inaccuracies and waste already contained in most things that are standard today. There’s no limit to the sh1te that A.I. can drum up.

Anything that can be framed and pointed to as a standard worth copying for any reason can and will be cloned at some point.

The Importance of Uniqueness

In a world where AI can replicate standards, uniqueness becomes crucial for differentiation.

In light of the amplification of the standard by A.I. here are a couple of ideas for consideration:

1. Individuals and companies must articulate what makes them unique to stand out in the marketplace.

2. Uniqueness involves embracing personal history, creativity, and idiosyncrasies.

Let me know what you think…

Next up: Some further thoughts on being unique

Last year in November 2024, I saw Des Traynor of Intercom on stage at SaaStock make a prediction. By the end of April 2025 (around about now), people will begin to climb out of the "trough of disillusionment," following the initial AI frenzy kicked off by ChatGPT over the past couple of years.

Des was right.

Looking back on my own experience, the hype was real in 2024, including a tonne of real-world business AI experiments and POCs across all industries.

In 2025, AI vendors and consultants are pushing hard on an AI-first agenda. Your LinkedIn feed is now flooded with AI FOMO (*fear of missing out), but right now it looks like it will still take a while for AI to really become mainstream at an industrial, enterprise level.

Why?

Businesses in general have yet to 'cross the chasm' of skepticism and fear associated with any new technology in the 'hype cycle'.

The Trailblazers

First movers, many of them AI and tech startups themselves, are certainly accelerating into AI and showing what can be done. They’re absorbing the risk and getting extraordinary results by plugging in AI early to see what happens. The larger incumbents who might have more to lose are still wary.

A lot of people I’ve spoken to over the last year, in Europe in particular, are taking a more ‘wait and see’ attitude to AI, despite sometimes having direct mandates from their C-Suite to use AI anywhere they can.

The Hesitants

Company buyers explore AI platforms with enthusiasm before they often get stuck in the "implementation gap" – the space between the promise of AI and the reality of making it work in their actual business.

The Hesitants want to know more about what’ll happen before they let AI have real conversations with actual customers. They are anxious about giving AI the ability to act and leverage on their internal systems, where AI might actually break something important in customer relationships. They want to know what it’ll cost, really, once they let AI loose.

According to a survey by Slalom Consulting, while a significant 79% of businesses are running AI pilot initiatives and 68% have already seen productivity gains, a smaller 31% are beginning to leverage AI for differentiation and disruption through more advanced applications.

This reveals a critical disconnect between financial commitment and strategic execution.

So what's going on? Why is AI implementation getting stuck?

Understanding AI Memory & Context

"Think of AI as having two kinds of memory: long-term memory and short-term memory.

AI's long-term memory is basically what's called training data. These days all the major AI models have the same training data, more or less. The entire internet is AI's long term memory.

Most of the advantage of working with AI now comes from short-term memory; the specifics of the problem you're trying to solve, which we call your context. The context is what you supply to the AI for it to know something about you."

• Tiago Forte

Short-term AI memory is the data that your AI is working with in any particular moment. AI will ask 'What is the conversation I'm having right now?' and use short-term memory to orientate itself before reaching into long-term memory for answers.

Context is very important to consider when you start using AI.

YOU set the context for YOUR AI.

But since context is quite subjective and can be hard to pin down, both technically and conceptually, it’s also a sticking point for those wishing to embark on their first steps with AI.

The 3 Dimensions of Context

1. Customer Context: Your deep understanding of customer struggles in specific situations

2. Operational Context: Your unique processes, workflows, and organisational knowledge

3. Market Context: Your distinctive position relative to competitors and industry trends

Setting the context for your unique use of AI forms the gap between buying AI and implementing AI. As the use of AI grows more common, the real edge for business use cases is in applying your unique knowledge and USP to any context.

But while most organisations understand AI's potential, they're failing to recognise that their unique context is the primary differentiator in success.

The first step in launching any AI initiative is defining the context for your own unique use of AI.

To understand why this is so critical, we need to look more closely at the implementation gap that's emerging.

The Gap

I launched a new AI services business with a co-founder in 2024 called Journey Mapper, partnering early with Intercom to focus on AI for Customer Service.

What we’ve seen is that sophisticated AI stacks often fail to deliver because of internal complexity and inertia.

We actually saw less action coming from those companies that claimed more sophistication in CX and customer support.

I've been involved in selling GenAI applications since 2016, when I was first exposed to AI models and PhD level AI scientists as a Marketing Director at Boxever, a travel focused 'CDP' that was later acquired by Sitecore. I've also worked with a couple more well-funded AI startups since. When it comes to the practical use of AI, I'd say I'm beyond a novice, but nowhere near as good as a Doctor of Artificial Intelligence from MIT. Somewhere in the middle.

In the early days, selling AI in real life was more like R&D. Most businesses weren't ready.

But some things remain familiar.

The gap between what an organisation wants from a new technology like AI and the very real struggles people in charge of that investment have when they think about how it’ll impact them is pretty big.

As it should be - AI will change all of our daily routines, it will impact in fundamental ways our ways of managing people and systems, and ultimately it will alter the economic realities of work and value.

People need to take a minute.

Investing in your AI platform

When you buy into an agnostic (meaning it’ll work anywhere) AI Agent like Intercom's FIN, or a platform native solution like Agentforce, or HubSpot's Customer Agent powered by Breeze AI, each will promise 50-90% resolution rates right out of the box.

But what does that mean exactly?

The sales pitch makes AI sound seamless: Connect your knowledge base, set up a few workflows, and watch the magic happen…

But here's what actually happens: Your AI is immediately confronted with your messy business reality – fragmented knowledge bases, undefined processes, and brand guidelines that were never designed for conversational AI.

For your AI to reach beyond 50% resolution rates in Customer Support, for example, significant and continuous honing and crafting is necessary.

For now at least, humans still have to conduct the AI orchestra for it to sound good.

AI Vendors are telling the truth about their AI

Let’s be clear -

The best AI Vendors aren't lying about their capabilities.

AI really can do more than you think. When you first turn on AI in your organisation it's the worst it'll ever be, and AI is awesome already. It is only going to get better, and AI itself is going to make AI it easier and easier.

[note: If you still don’t believe in AI’s capabilities and have yet to move beyond trying ChatGPT to do basic tasks, try putting some skin in the game and pay $200 for ChatGPT Advanced or Claude MAX for just one month. Get it to do just one bit of research, or just one part of your typical daily process. Then let me know how you got on.]

The question we’ve been arguing about for 12 months is: how does a buyer even know the difference between a GPT hack and something like FIN, which is built with years of R&D? The skepticism is real.

In practice, getting the most out of AI today still requires significant expertise, commitment, time, and organisational change. As you'd expect of any technology transformation.

McKinsey’s 2025 State of AI Survey reports that 83% of organisations cite “data fragmentation” as the primary barrier to AI scaling, with only 15% achieving enterprise-wide integration.

Technology should be easy, but it ain’t, mostly

For comparison, let’s briefly talk about CRMs, based on my own exposure to implementing them for clients as someone running a HubSpot Platinum Tier Solutions Partner agency, Mount Arbor for almost a decade.

I've spent 8 years selling CRMs to people who hate CRMs.

When someone invests in a CRM platform they are often under the impression often that their CRM will just work on its own. This is an impossible dream designed by vendors to close deals and attract customers. Nobody really believes it, but still - people are often annoyed they need to talk to me about their CRM in the first place. I try not to take it personally.

See, with a CRM, to get the whole thing to work it’s usually not the technology that’s lacking - it’s the extraordinary complexity of your customers that throws all the standard processes out of whack so you have to customise almost everything.

I call it the ‘Pesky Human Problem’.

“It’s not that AI technology isn’t up to the challenge. We just haven’t supported it with the right enablers—both technological and human.” - Deloitte Insights article.

Most engineers would love the pesky human problem to go away, but business owners understand that their customers need more than the fastest path to an outcome in order to be satisfied.

What if it’s the wrong outcome?

What if your customer doesn’t even know what they want in the first place?

The challenge with CRMs (and Customer Experience in general) is that most customers don’t know what they want. Not really.

Expecting customers to know what they want causes everyone to design and build the wrong systems.

Humans are more complicated than that, so the way you use your technology in the customer experience also becomes a bit tricky.

It’s a similar pattern with AI investment.

“It’s not the customer’s job to know what they want” - Steve Jobs

Real-World AI Struggles

Let me share some real-world examples of companies struggling with AI:

1. Customer Support AI Reality: One company connected their knowledge base to a leading AI platform expecting exceptional resolution rates above 50%. Months later they were still at 30%. It wasn't the AI – it was their fragmented, outdated knowledge base that lacked conversational context. But they blamed AI and decided to pause their AI program for a while.

2. Internal Policy Assistant Goes By the Book: An Enterprise AI pilot designed to answer employee questions went unused. Despite having access to all documentation, it couldn't handle the unwritten exceptions and departmental variations. As one employee put it: "It just gives me the handbook answer I already know isn't right."

3. Marketing Chatbot Failure: A SaaS company's technically perfect website chatbot caused conversion rates to drop. All the marketing team's carefully crafted messaging never made it into the AI's knowledge base, so the bot sounded like a bot and was largely ignored, despite the advanced technology running it.

In a year or two, people will look at this list and laugh at how stupid it is.

All of these bottlenecks can be fixed, but when people hear stories like this early in the hype cycle, it can fuel skepticism and inertia.

What do you think is the number one concern I've heard from most people when we talk about conversational AI?

It's not whether the AI will technically work, or even if it's secure and safe.

It's the control of brand and tone of voice.

While these issues seem technical, they actually reveal a fundamental misunderstanding about how AI adoption works.

Resistance Among Those Who Benefit Most

At Journey Mapper over the past year or so, we’ve been helping clients to implement AI solutions for better Customer Experience (CX).

Since early 2024, we’ve been talking with Customer Support and Customer Success (CS) leaders at leading mid-market SaaS companies across Europe. We’ve been spending a lot of time with CX leaders who want to learn more about the promise of transformation and efficiency in AI.

We ran a series of successful webinars during the year that covered a lot of ground.

We spoke to hundreds of the people who stood to gain or lose the most with pioneering deployments of AI in Customer Support.

What we discovered shouldn’t come as a surprise.

A lot of them aren’t that keen on AI. Not yet.

Customer Support leaders are historically among the most change-resistant in the enterprise.

CS leaders are evaluated on consistency, reliability, and satisfaction scores. Their primary incentive is stability, not transformation.

Asking CX Leaders in particular to undertake radical platform changes, where most haven't changed platform for 20 or 30 years, represents a substantial risk with uncertain rewards.

Take Intercom’s FIN AI Agent, for example.

Technically, it's hard to understand why everyone wouldn't just add FIN to their existing platform (e.g. Zendesk or Salesforce), just to see if it works. A lot of people do - FIN is an industry leading, fast growing product that will blow your mind and very exciting for those who try it out.

But this isn't a technical struggle. It's an emotional and social one.

A lot of CX Leaders are taking baby steps, and taking a 'wait and see' approach.

Wouldn't you, if it was your job?

When I sold HubSpot services, I was usually dealing with customers who had used their CRM for a year or two. They already know how complex it is and that they need help.

We’re still in the very early days of AI.

We're asking leaders to take a leap of faith on AI technology that's still evolving.

Despite compelling evidence showing AI's positive impact on efficiency, quality and customer experience, there are a lot of potential pitfalls to be considered too.

The AI Adoption Journey

“A quarter of CIOs have launched something - but half don’t plan anything for at least a year” - Benedict Evans, AI Eats The World presentation, 2025, slide 59.

Working with dozens of organisations implementing AI, I've observed the adoption journey typically follows three distinct phases:

1. Initial Implementation: Setting up the foundational knowledge base, configuring the first workflows and conversation flows, and setting the initial parameters for brand and tone of voice.

2. Optimisation Phase: Refining AI behaviour, content, and actions based on performance data and feedback loops, adding more external data to enrich the experience.

3. Strategic Integration: Elevating AI from a support tool to a strategic business advantage, by applying unique and inventive uses of AI to enhance the customer experience.

AI vendors sell the vision of phase three (Strategic Integration), but so far most customers can barely get through phase one (Initial Implementation) without help.

The Skills Gap in an AI-Powered Economy

Successful AI implementation requires a unique blend of skills that doesn't neatly fit into existing organisational roles. This is what the AI vendors mean when they say that AI won't put people out of work, it'll simply require people to get skilled up in other areas.

Based on my experience so far, the key capabilities now in demand include:

• Conversational AI Implementation & Optimisation

• Customer Journey Design & Enhancement

• Cross-Functional Collaboration (CX, Product, Marketing)

• Performance Analytics & Data-Driven Improvements

• Content Strategy & Workflow Optimisation

• AI-Powered Automation Strategy

• Brand Voice & Tone Alignment

These skills aren't typically found in a single individual or even within a single department.

Traditional customer support teams excel at understanding customer issues but may lack the technical skills to optimise AI systems. Marketing teams understand brand voice but may not grasp the nuances of conversation design for support contexts. Engineering teams can integrate systems but may not understand the human elements of customer experience.

This skills gap represents both a challenge and an opportunity, but requires co-operation, training and experimentation in finding new ways to work alongside AI.

These challenges point to a fundamental gap that needs addressing.

I propose a framework for solving what I call the "AI Context Activation Gap."

The AI Context Activation Framework

“Take a stakeholder from every department, build an AI board, and start pointing at horizontal solutions. Then you’ll actually get value.” - Jonny Marriott, Glean.ai, speaking on a Journey Mapper webinar in 2024

The 'AI Context Activation Gap' is a failure to adequately leverage unique organisational context. Activating Context is about the specific nuances of your business, your customers, your internal processes, and even your unwritten rules and cultural understanding.

For example, even within business communication, the challenge lies in humans constantly moving context between silos like email, Slack, and even tools like HubSpot and Figma. Without explicitly feeding this crucial business context into your AI initiatives, you're essentially asking a powerful tool to operate blindfolded.

The growing demand for roles like AI managers and knowledge managers is external validation of the importance of managing and leveraging organisational context. Companies who use their unique knowledge to activate their USP across their own context window for AI in a deliberate, planned way are going to do better than companies just buying AI tools and hoping for the best.

AI's general knowledge is becoming less and less special. The three dimensions of a company's unique context is becoming the key differentiator for companies committed to an 'AI-first' advantage.

Onboard your newly hired AI like a human

"We think about it just like that new hire. They’ve just landed in seat for the first time. First, we train them up on all the knowledge… then it’s about tone, behavior, and how they interact with customers." — Paul Maher, GTM at Intercom speaking on a webinar hosted by Journey Mapper

One simple rule of thumb to guide the onboarding of AI in your organisation is to treat it as you would the onboarding of a new employee, rather than a technology transformation project.

Once you start to think of 'hiring AI' this way, you begin to see how your existing onboarding programs, management systems and knowledge bases can be used to onboard your AI more effectively than a technical migration program would.

Consider AI Agents to be a new hire, with all the same challenges any new hire would face when starting a new job with a new team, talking to new customers about a new product. Except AI's onboarding ramp time is usually weeks, not months, and it doesn't forget anything.

You'll also see how culturally, an AI Agent almost needs to be treated similar to how you'd onboard a human when it comes to setting the boundaries of value-based decisions and behaviour.

Remember, AI Platforms are incentivised to present their solutions as easy to implement to drive adoption, but this can create unrealistic expectations that ultimately harm long-term success.

A Way Forward: Bridging the AI Context Activation Gap

AI is fully onboarded once your unique AI Context has been Activated and deployed to customers. I see several potential paths forward for addressing the AI Context Activation gap.

For AI Platform Vendors:

• Reconsider your market category: Rather than positioning as replacements for established platforms, consider whether adjacent categories might offer better alignment with current capabilities.

• Invest more heavily in Activation services: Whether through internal teams or partner programs, recognise that successful AI Activation and Context Management require specialised expertise.

• Be more realistic about implementation requirements: Setting appropriate expectations about the expertise and effort required will lead to more successful deployments.

For Solutions Partners:

• Develop specialised, vertical-specific offerings: Rather than generic implementation services, create packages tailored to specific industries or use cases.

• Build proprietary methodologies: Differentiate through unique approaches to implementation rather than just platform expertise.

• Consider alternative business models: Explore outcome-based pricing or recurring service models that better align with customer value realisation.

For Enterprises Adopting AI:

• Recognise the expertise gap: Be realistic about internal capabilities and the expertise required for successful implementation.

• Start with focused use cases: Rather than attempting wholesale replacement of existing systems, identify specific, high-value use cases for initial implementation.

• Invest in internal capability building: Alongside external expertise, develop internal teams that can own and evolve AI systems over time.

Your AI Path Requires Contextual Honesty

AI technology is powerful and transformative, but we're still figuring out how to implement it effectively.

How each business embraces AI in its own organisation requires honesty about the challenges and a willingness to adapt their approach to the unique and sometimes counter-intuitive signals of their customer.

The irony is that the more powerful AI becomes, the more hesitant and anxious people get about just 'plugging it in'.

The slower a business is to start, the less likely a business is to be able to ‘corner it’s context’.

What’s uniquely yours today may not be tomorrow, unless you grasp it now.

Start using AI now in order with a goal to own, manage and deploy your unique contextual knowledge.

Close your AI Context Activation gap as soon as possible so the 'tacit knowledge' inherent in your organisation quickly gets passed into the 'short term' memory of your company's own Context Window.

Protect your UNIQUENESS above all else in order to remain competitive in an increasingly ambiguous economy.

For my part, I remain excited about the potential of AI in customer experience despite the implementation challenges and the disruption it’ll cause.

As AI platforms and tools continue to evolve rapidly, and each implementation gets a bit easier than the last.

Realising the full potential of AI in your organisation will require more than just better technology - it will require new skills, new business models, and new ways of thinking about implementation and partnership.

The companies that recognise this reality and adapt accordingly will be the ones that successfully bridge the AI Context Activation gap.

Andrew McAvinchey is a certified Intercom FIN AI implementation specialist and co-founder of Journey Mapper, a consultancy focused on customer experience transformation through AI and technology. With over 15 years of experience in technology and customer experience, he has worked with organisations across industries to implement and optimise customer-facing technologies.