Why change resistance is really memory in disguise
Written By Maciej
If you’ve already grabbed the “Memory, Emotion, Loss” tip sheet from this episode, this article goes deeper into where the MEL framework came from and how Patrick actually applies it.
How two datasets became a book
The origin of Patrick and Victoria’s collaboration – and eventually their book Stuck – started with two separate piles of data that had nothing to do with each other until they did. In 2014, Patrick was sitting on a massive dataset: an annual engagement survey of the entire US federal government workforce, roughly two million people a year, which he’d scraped together without a clear plan for what to do with it. Around the same time, Victoria was presenting her change diagnostic index – a tool she’d tested on six or seven thousand people, measuring the factors that create organisational attachment. Patrick’s idea was simple in hindsight: apply her index to his two million data points. They presented the resulting research at ACMP in 2016, which grew into a book chapter, and eventually into Stuck itself.
Data is both the change and the way you drive it
By the time Patrick left to start Symmetry, he’d been working with chief data officers – a role that was still relatively new at the time – on a thesis that made a lot of people uncomfortable: organisations could use natural language processing to run genuine sentiment analysis on internal communications, capturing what he calls revealed preferences instead of the stated preferences you get from a survey. He was blunt about it with clients: they own all your emails, they could analyse how people actually feel instead of asking them. Working largely with US government organisations at the time, the reaction was often disbelief – people were certain their organisation would never actually look at employee email that way.
That same instinct – data as the lever for changes people didn’t yet believe were possible – showed up in concrete client work. Symmetry helped an insurance industry client use data to drive underwriting decisions in a way they’d never done before. It helped a large US retailer use data to negotiate with suppliers for the first time. In both cases, the technical work was really a mindset shift dressed up as a data project.
What robust change data actually looks like
Patrick draws a hard line here that’s worth sitting with: don’t tie your data to whether the change “worked.” Tie it to the strategic objective the change was meant to serve. If the objective was to make money or save money, measure that directly – in the supplier example, the real measure of success wasn’t whether people adopted the new negotiation process, it was whether the company started selling a higher percentage of product from suppliers it had used the new process on. If that number moved, the change had worked. If it didn’t, no amount of positive feedback on the rollout mattered.
One of his favourite examples of this in action involved a quasi-governmental organisation that needed accurate location data on its field staff. The existing process involved everyone submitting an Excel file at the start of each quarter, predicting where they’d be – a static process that, unsurprisingly, people stopped bothering to correct once it went out of date. Symmetry helped build a dynamic app with geolocation built in, so correcting your location took seconds instead of a form resubmission. The actual success metric wasn’t downloads or logins – it was whether the organisation’s key team hit real, accurate usage. By 2021, they’d reached 100% usage for the group that mattered. That’s the adoption signal. Email open rates on the training that supported the rollout never came up, because they were never the point.
You have more data than you think
Not every organisation Patrick works with has a robust dataset lying around, and his response to that is direct: it’s almost never actually true that there’s no data to work with. Every organisation has a record of its stakeholder communications – the number, the type, the diversity, the touchpoints – and usually a decent sense of whether those touches are landing. Government systems, he points out only half-jokingly, are essentially form-fillers: wherever people stop filling out the form is exactly where your adoption problem lives, whether or not anyone’s labelled it that way.
Beyond internal data, Patrick recommends pairing whatever you have with things like social trends relevant to your sector, or impact-area data if you’re in the non-profit space – housing, incarceration, environmental metrics, whatever your organisation exists to move. These act as a “true north” even when a formal change-metrics dataset doesn’t exist.
The mindset shift underneath all of this, in his words, is to stop thinking too procedurally. Process is useful when you don’t know where to start, but getting bound to it – thinking “I’m still in phase one” – means losing sight of the actual destination. His advice: look at the end of the journey first, say out loud what it will actually take to get there, and let the plan follow from that, even if it ends up looking nothing like the process map you started with.
The MEL framework: why resistance is really memory
This is the conceptual core of the episode, and it comes directly from the work behind Stuck. Patrick’s argument is that three things happen simultaneously in the brain’s limbic system when a change lands on someone: a memory gets triggered, an emotion tied to that memory floods in, and a sense of loss registers alongside it. The same part of the brain is also where learning happens – which means the same three-part structure has a positive flip side: memory, emotion, and learning instead of loss.
We think of that as resistance, and it’s not – it’s memory and emotion coming together.
His practical example: someone is told new technology is arriving immediately, and the reaction that floods in isn’t really about the new technology at all – it’s the emotion attached to the memory of the last time something similar happened. The old framing of “unfreezing” resistance, from classic change theory, is really about unfreezing that memory and emotion specifically – and the only way to do it is to give someone a new experience they genuinely like, which creates a new memory and a new emotion to replace the old pairing. That’s why training matters so much to adoption, and why starting it too late in a rollout undercuts its own value.
It’s also, Patrick is careful to note, entirely individual. Two people in the same cohort can carry completely different memories of the same past change, or have gone through two entirely different changes that shaped how they’ll respond to this one. Layered on top of Victoria’s attachment theory work, which explains differences rooted in early development, MEL adds a second, more immediate layer: each person’s specific history with change at work, which is just as important to account for and constantly shifting.
When the memory isn't even yours
One of Patrick’s favourite – and most telling – observations is how often the memory driving someone’s reaction isn’t actually their own. Organisational mythology accumulates the same way family stories do: someone recounts “the way things used to be” with total conviction, despite not having been there for it. Patrick describes catching this directly with a younger employee at a previous firm, who reminisced about “the good old days” of an era that predated their employment entirely – stories about things like M&Ms in the break room, retold so often they’d become folklore. He points to Angus Fletcher’s book Primal Intelligence on why this happens: storytelling is arguably the defining trait of human cognition, and it cuts both ways – it’s exactly as good at spreading useful culture as it is at spreading myths that actively work against a change.
Nowhere is this more visible, or more costly, than in mergers and acquisitions. Ask most people what a merger means and they’ll describe something close to what Patrick calls the Pretty Woman effect – the 1980s-and-90s-era image of a buyer acquiring a business purely to break it up and sell it for parts. It’s a memory that isn’t even the listener’s own; it’s inherited from pop culture. In Patrick’s telling, that’s simply not how most M&A works today – most private equity activity is aggregation aimed at growth, not liquidation, and even the aggregators still face a real change management challenge, just a different one than the myth predicts. He speaks from direct experience here too: after selling his own business into a larger company, people routinely asked him whether it would get broken up or whether he’d be fired.
The scale of this problem is bigger than most change practitioners treat it. Patrick cites an estimate of roughly five million people affected by M&A activity every year in the US alone, and 60 to 70 million globally – a genuinely large-scale change that gets far less attention at industry events than technology rollouts, despite the numbers. His prescription is the MEL framework applied directly: name the outdated myth before anyone can hear the real reason a deal is happening, then help people understand the authentic reason behind it – whether that’s a founder nearing retirement, or a business choosing growth capital over debt. None of this requires spin. Most M&A has a legitimate, positive rationale behind it; the work is making sure people hear that reason before their inherited mythology fills the gap for them.
Stop binge-watching change
Patrick’s framing for why this matters at a structural level, not just an individual one, is one of the sharper lines in the conversation: business today runs as a series of episodic changes, not one continuous stream. Changes do end – they’re episodes, not an unbroken cycle – but most organisations are, in his words, terrible at telling people when one has actually finished before starting the next. The result feels like binge-watching change: exhausting, directionless, with no sense of completion. He connects this directly to a familiar pattern of leadership behaviour – a leader returns from a conference having read a new book, and immediately layers a new initiative on top of the last one before it’s had a chance to land. Even when the exhaustion is felt mainly at the leadership level, it radiates through the rest of the organisation. His fix is simple to state and consistently skipped in practice: mark the end. Tell people this one is finished, here’s the next one, and here’s why – and let people actually take the breath in between.
Reading the room at scale
For organisations that do have engagement data, Patrick and Victoria’s federal dataset – now spanning sixteen to seventeen years – shows five clear markers that move up and down at an organisational level based on how people are experiencing change, including productivity, motivation, and morale. Two contrasting examples make the pattern vivid. When the US fundamentally changed its approach to space travel, NASA’s productivity and morale stayed remarkably steady, because its mission – doing the best scientific research in space – never changed, even as parts of the mechanics around it were outsourced. The Secret Service tells the opposite story: a scandal in the mid-2010s over inappropriate use of funds while agents were travelling directly threatened the organisation’s sense of mission, and motivation, morale, and productivity all measurably collapsed as a result. Mission stability, in other words, is one of the strongest buffers an organisation has against the disruptive effects of change – and one of the clearest casualties when it’s damaged.
Symmetry uses the same logic in M&A work directly, running annual engagement surveys before and after acquisitions to measure whether applying the MEL process actually improves engagement scores post-deal compared to pre-deal. For organisations without survey-scale data, Patrick points to simpler proxies – time-tracking data in professional services firms, for instance, is a genuinely useful window into productivity trends without needing a formal engagement instrument at all.
Find your own potential space
Patrick’s closing reflection was inspired by another speaker at the same conference, on the subject of curiosity – and it ties the whole conversation back to something personal rather than organisational. Curiosity, in his framing, comes from the combination of two executive emotional functions: learning and playfulness. Both require what he calls potential space – room between where you are and where you’re going – to activate properly. Without that space, fear tends to override everything else, the same way it does in moments of genuine transition or tension, and people default to the worst version of a relevant memory rather than a more useful one.
His practical advice is to deliberately build a habit that creates that space – walking while listening to something, music, cooking, or simply silence – because it’s often the fastest route back to a positive memory that can serve as an analogue for whatever you’re currently going through, rather than the automatic worst-case one. For leaders, the organisational version of this same idea is worth taking seriously: accepting a short-term dip in productivity to actually give people room to think is very often a better trade than pushing straight through it.
The takeaway
Underneath every part of this conversation – the data work, the MEL framework, the M&A mythology, the episodic-change problem – is the same throughline: what looks like resistance to a new change is usually an old memory and emotion resurfacing, and what looks like a lack of data is usually data that’s never been looked at through the right lens. Patrick’s approach doesn’t ask organisations to collect more information. It asks them to take seriously the information, and the memories, they already have.
The Outlier Group
A specialist Change Management agency who design and deploys change campaigns that are memorable and move the needle.
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