I didn’t bring a laptop and tried to leave my phone in my pocket, but here are some moments or ideas that really stood out for me…

Rachel Coldicutt on hope

• Reality has been distorted and problems hidden by technology giants operating at a scale that makes accountability almost impossible. The damage is real but it is also frequently invisible — until it isn’t.
• Many of the loudest voices in tech — those doing the most complaining about how broken everything is — are white, male, and privileged. Yes…
• Hope can be dismissed as being naïve, but Rachel introduced the idea that hope is actually contagious. It only takes concerted action by a relatively small number of people for it to start spreading to those around them. You don’t need a majority. You need a committed, visible few (see her new organisation, the societyforhopefultechnologists.org).

Himal Mandalia on the intersection of commitment and burnout

• When people who care step in to fix broken systems, they inadvertently teach those systems that the problem is being handled. The incentive to actually fix the underlying issue evaporates. The people absorb the dysfunction so the organisation doesn’t have to.
• This means the negative impact on the humans doing the fixing quietly compounds. The system is fine. The people are not.
• The key concept Himal introduced — and one that felt particularly relevant to strategic design practitioners — is the idea of taking responsibility without having authority. The people who care most about the work are precisely those most likely to step in and carry a burden that isn’t formally theirs to carry. They do it because they are dedicated professionals, not because anyone asked them to. Over time, that gap between responsibility and authority becomes a significant and under-acknowledged cause of burnout. I’m going to be reflecting on this over the coming months.

Tessa Quinn on doing the right thing against formidable odds

Tessa told us the story of Scotland’s “Super Sponsor” scheme — the intention of which was to allow Ukrainians fleeing the war to apply for a visa naming the Scottish Government as their sponsor, meaning they didn’t need a pre-arranged host.

This policy decision set Scotland apart from other host nations and simplified the process for refugees while meaningfully improving safeguarding at a moment of genuine crisis.

It’s an extraordinary story. Worth reading more on scotgov for the full picture. Several things from Tessa’s talk really hit home — to the point where I found myself unexpectedly emotional at one point (long story; involves Covid and Nicola Sturgeon).

Three things really stood out for me:

Emotional safety under pressure. The team was operating in genuinely traumatic conditions — the subject matter was war, displacement, families separated. Tessa talked about how deliberately cultivating psychological safety within the team was operationally essential. People needed to be able to say when something wasn’t working, raise doubts, flag distress — and know they’d be heard, that she had their back.

Radical adaptability. This was not a programme with a comfortable runway. It was weeks, sometimes days. The team used whatever tools, platforms, and people were to hand — patching things together in ways that would make a procurement officer weep, but that actually worked. It’s a vivid example of what’s possible when you stop chasing perfect.

Leadership as moral clarity. The Scottish government’s framing of this issue made the work almost self-motivating. People weren’t just executing a programme; they were manifesting a set of shared values.

Three very different talks, visualised though different lenses, but that orbit around a central theme of what it means to have purpose. I’m reminded how lucky we are to be doing this work:-)

Good day out in Manchester.

See also excellent write-ups (thanks Hugh for the blogroll energy!) from Adam, Hugh, Heledd, Matt, and Nicola.

I think if you could put the two thinkers on either side of a full tea-pot supported with quality biscuits (could they be doing that up there right now?), you would hear some interesting reflections on how leaders have a responsibility to help others:-)

Both thinkers have delved deeply into the realms of organisational leadership, culture, and change, emphasising the importance of values, ethicals, and interpersonal dynamics.

Human-centred leadership

Both Schein and Greenleaf prioritised the human aspect of organisations over rigid structures or impersonal strategies.

• Greenleaf is known for pioneering the concept of “servant leadership”. At its core, servant leadership emphasises that leaders should prioritise the well-being and growth of their followers. A servant leader is someone who leads not to gain power or prestige, but to serve and ensure that others’ highest priority needs are being met.

• Schein, on the other hand, is renowned for his work on organisational culture. He emphasised the importance of understanding the underlying beliefs, values, and assumptions that drive behaviour in organisations. Schein believed that effective leadership requires an understanding and appreciation of these cultural nuances.

The importance of ethics

Both Greenleaf and Schein emphasised the moral dimensions of leadership.

• Greenleaf’s servant leadership is deeply rooted in ethical considerations, highlighting the importance of empathy, active listening, and moral authority.

• Schein’s work implies that leaders play a crucial role in shaping organisational culture, and thus have a responsibility to ensure that the values and beliefs they instil are ethically sound.

The importance of listening

Both scholars highlighted the importance of deep listening and understanding in leadership.

• Greenleaf’s concept of servant leadership stresses the importance of listening intently to others, recognising their needs, and fostering their growth.

• Schein, in his work on culture, elaborated on the importance of humble inquiry – asking questions in a way that promotes trust and genuine curiosity to truly understand another person’s perspective.

Change Management

Both thinkers provided insights into how organisations should navigate change successfully.

• Schein discussed how the deep-seated beliefs and values of an organisation can either facilitate or obstruct change, and how leaders can influence this dynamic.

• Greenleaf’s philosophy implies that servant leaders, by their very nature, are well-positioned to guide organisations through change because of their emphasis on empathy, understanding, and putting others’ needs first.

I love this thread connecting Schein and Greenleaf:-)

It’s the understanding that effective leadership is rooted in humanistic and ethical principles. Leaders should serve as stewards of organisational culture and values, fostering environments where individuals feel valued, understood, and empowered.

One more thing…

Because we all know the best way to get an idea across is to tell a story, here’s a quote I’ve stolen from the above Wikipedia page that does just that:-)

…In this story, we see a band of men on a mythical journey… The central figure of the story is Leo, who accompanies the party as the servant who does their menial chores, but who also sustains them with his spirit and his song. He is a person of extraordinary presence. All goes well until Leo disappears. Then the group falls into disarray and the journey is abandoned. They cannot make it without the servant Leo. The narrator, one of the party, after some years of wandering, finds Leo and is taken into the Order that had sponsored the journey. There he discovers that Leo, whom he had known first as servant, was in fact the titular head of the Order, its guiding spirit, a great and noble leader…

Finally, since I’m using Wikipedia’s service and copying their materials I’d like to suggest you join me in donating to their service.

Wikipedia is not for sale: an important update from Jimmy Wales

Thanks:-)

What is a random-but-impactful piece of advice that you would give a new facilitator?

One hundred and fifty-seven people replied. Coaches, OD consultants, service designers, L&D professionals, workshop facilitators from Kenya, New Zealand, Germany, the US. Meg jumped into the replies, asked follow-up questions, pushed people to say more. The thread ran for days.

I contributed one piece of advice: Bring enough pens?

Meg said it was one of her favourites in the thread. Which tells you something about what this community thinks good facilitation is actually made of.

I saved the whole thread. Here’s what I found when I read it properly.

The physical environment is a design decision

A surprising amount of the wisdom in this thread is about the room itself — and it’s worth taking seriously, because new facilitators often treat the room as a given.

Room setup tells participants what kind of conversation they’re in. Margaret Stacy-Duffy put it plainly: don’t set up a room for a lecture and expect vulnerable discussion to happen. The arrangement of chairs communicates what’s expected before anyone opens their mouth. Lisa Taylor adds: if you’re stuck with theatre-style seating, at least angle the two sides toward each other — a shallow V keeps people from staring at the back of each other’s heads.

The physical logistics are not beneath you. Yes, bring enough pens. Check that your flipchart markers actually work. Know where the bathrooms are relative to the room. Know how long the coffee queue takes. Know whether lunch arrives in the room (Meg: “you have five minutes before you totally lose the group’s attention”) or somewhere else. Know whether it’s hybrid. Catherine Simpson adds: check participants’ sensory and mobility requirements, not just dietary ones — and remember that sound, temperature, and smell all affect what’s possible in the room.

Arrive early. Rachna Verma: arrive early if onsite, set up the space and familiarise with amenities. This is boring advice and it is correct:-)

Time is a design material

The most reliably bad thing a facilitator can do is pack too much in and then run out of time.

Sulu LeoNimm: Don’t fill the time available and stuff everything in with rigid time constraints. Plan that things will take longer — then have things in your back pocket in case you get extra time. Robin Gissing adds a useful heuristic: for every item on your agenda, ask ‘if this were dropped, what would we lose?’ Things that can be dropped without losing substance become your bonus material.

Ben Brearley: if I’m talking for more than five minutes straight, it’s a sign that I need to get participants doing something. This is a reasonable forcing function.

Take the break. Dan Manning: You’ll feel the temptation to press through. If you ask, they’ll tell you to keep going. But, more times than not, you’ll regret not taking the break. Meg replied: The amount of times I used to be like ‘we can do just one more thing’ and then half way through that think been internally kicking myself because it wasn’t going well… Marcus Crow goes further: bladders last 90–100 minutes, and the further down Maslow’s triangle you get, the more you lose the group. Plan around biology.

Don’t end on logistics. Sarah Willcox: find a time to cover housekeeping before the closing, then close with something that lets participants reflect on what they’ve achieved. Leave them on a more energetic note than if you were talking about parking spaces.

The first five minutes

Lauren Sowers: create an opening that lets everyone find their voice within the first five minutes. The earlier each voice is heard, the more invited folks feel to speak from that point on.

This is also why Jacob Chromy’s advice — don’t skip the check-in — landed so well that Meg pushed him to say more. The check-in is not warm-up fluff. It’s how you signal that this is a participatory room, not a presentation. It’s how you establish the norm of speaking before the stakes are high.

Katerina Kupenga: no one knows what’s on the runsheet. Don’t freak. Move with the moment and the group.

Your ego is not the tool

The advice that recurred most often, in different forms, was about the facilitator’s relationship to their own need to perform.

Emanuele Mazzanti: Check your ego at the door. It is about letting their wisdom emerge, not your need to impress. Fran Cormack: Let go of the need to be good. When Meg asked what she’d replace it with: a desire to be useful.

Mike Cardus offered two connected observations:

1. You don’t need to see learning for it to happen.
2. When you are working harder than the team to solve their problem, you are now a part of the problem.

Josh Cox: Fight the temptation to be anything else but yourself. Katerina Kupenga: Don’t think you have to impersonate a great facilitator — find your own natural talent and stand in it.

And Lana Kristine Jelenjev, perhaps most usefully: Settled bodies settle bodies. When you’re regulated, you help others regulate. Your nervous system is part of the room.

Silence and questions

Célynne Shipley: Don’t fear the silent gaps. Someone will fill it and ideally it isn’t you.

Teresa Bassma: Ask one question at a time. I hear so many people ask questions like ‘should we have this event and if so when and what theme.’ It makes for pretty confusing conversations.

Robin Funsten: Talk less and ask more questions. If the questions aren’t being answered in the big group — make small groups. Meg highlighted this one too: so simple, so helpful.

David Warren: Be comfortable being a mirror back to the group, even when the mirror sees things that might not be pretty.

Plan, but run loose

The most-repeated structural tension in the thread was between preparation and adaptability. Several people landed on variants of the same idea.

Whitney Thoren: Design right, run loose. Be prepared for it all, but be willing to ditch it along the way if that’s what the group needs.

Jodie Rogers: Have a rough plan for sessions — but don’t be a slave to the plan.

Martina Gobec: Be understructured and overprepared. Be willing to change the agenda on the fly, if the process requires it. I called this the best advice in the thread, and I stand by it.

Shani W: They haven’t attended this before, and don’t know what your facilitation plan was. If things go wrong, breathe, improvise and remember that nobody will know this isn’t what was supposed to happen.

How you get better

Chad Littlefield: Want to run or swim better? Run and swim more. Same logic applies. Meg agreed: she spent her early years doing free sessions, which lowered the stakes for herself and got her reps in.

Kerri Price: Go watch other facilitators in action. Participate in lots of workshops, even if you’re not particularly interested in the topic. Find a facilitator you admire and ask to shadow them for a day. Jes Smith adds the harder version: watch yourself. Force yourself to watch those Zoom recordings. Ask someone to record your face-to-face sessions.

Kate Horton: If you suck at first (which you will) identify areas that you WANT to enhance and learn more about. Stay curious and committed to being a life-long learner.

What the thread itself is

I asked Meg at the time if she’d collate and share the thread. She said she hoped to but wasn’t sure she’d find the time. She hasn’t yet — but the thread itself is still there, and it’s worth reading in full if you do this work.

What struck me, looking at it properly, is that almost none of the advice is about techniques or methods. Nobody said try the 1-2-4-all or use dot voting. The advice is almost entirely about the facilitator’s relationship to the room, to the group, and to their own ego.

The things that experienced practitioners converge on, apparently, are: the physical basics matter more than you think; ego is your main obstacle; questions beat talking; silence is productive; plans are for abandoning gracefully; and — yes — bring enough pens.

Meg Bolger is Head of Facilitator Cards, which is exactly what it sounds like and worth a look.

Capital moving from building stuff to charging rent might sound abstract, but it maps directly onto startup culture. When you chase a Series A round (and then B, C, etc.), you’re dealing with people who don’t necessarily care about community, society, innovation, or creativity. These players are converting capital into ARR as quickly as possible — in other words, they’re talking to you and your mates because they expect you to start charging rent. ASAP.

Obviously, business isn’t charity. But when I started my first business, it was because I had a dream — a vision of what my future could look like and how I might put a dent in the universe.

I suspect many people starting up today feel the same way, but the environment is very different now. We live in a time of extreme inequality and a heavily financialised economy. For most younger entrepreneurs, that probably means the default pattern is a startup backed by VC funding.

If I were backing business ideas today, I’d want to talk to people with plans that can survive the current AI bubble. I’m not saying everyone needs to bootstrap — but the VC-to-ARR conveyor belt is not (at least in my view) a sustainable plan.

(A ‘vintage’ ad for BASF. The claim is the chemistry of the mag tape keeps the “emotions intact”)

This really touched me so I left a comment which I thought I’d keep here for later:-)

Hey Sanni:-)

I so desperately want to agree with you.

Dad was a director and as a teenager I learned how to load ArriBL and Éclair Cameflex magazines, built a darkroom, including using abandoned hamster cage bottoms as trays and a 35mm slide projector from the 1960s (it was in the loft) as an enlarger (involved quite a bit of carpentry). I kept going for years on pure passion…

“Sweat doubt and joy” are so real.

I remember countless nights parked outside Metro E6 Labs in Chelsea waiting for clip tests, the extremes of joy and desperation when the results came out…

But, where we are now feels more like DoPs not wanting to shoot on video. The dynamic range eventually caught up and that’s no longer an issue.

The essence is some kid grabbing whatever cheap film-making solution is available and carving out a story, painting that emotion. If I look at the film you shared, I don’t see a depth that’s absent in films by AI video users. At least not as much as I was expecting when I read your post.

I guess I need to see where this goes a bit further before taking a view. I suspect this isn’t going to be an either/or, but instead that some people are driven by creative demons while others are not?

(There is no shareable link to the film that Sanni mentions in her post)

• Here’s a RunwayML thing someone posted yesterday (Alienmart)
• and here’s Aardman’s 1989 “Creature Comforts” film

What would this be like?

How would this Language-Based Analysis (LBA) manifest; how would it work? Instead of mapping: Systems, functions, and capabilities, we might map:

• meanings
• terms
• framings
• narratives
• spoken/written interactions
• data vocabularies and taxonomies

And then ask:

What language is used by each actor to describe their world, and how does that language shape (or distort) the exchange of value?

How is this different from traditional enterprise architecture (EA)?

I guess there would be similar but different tasks:

Where would LBA fit in the enterprise transformation?

We would engage our customers with an LBA approach:

• Instead of as-is/to-be capability maps?
• Before any tech re-platforming?
• As the foundational diagnosis of misalignment between business and data, policy and digital, or product and customer?
• To make visible the invisible contracts that define how value is exchanged or perceived?

Example: Supply chain transformation via Language-Based Analysis

We were looking back at large scale transformations over the years and considering the example of a supply-chain transformation at a major FMCG company (this is where this whole idea begins to seem bonkers).

A company like this will have a complex, global supply chain – with dozens of factories, thousands of suppliers, and millions of customer touchpoints.

In a traditional enterprise architecture approach, the supply chain might be broken down into functional components like:

“Procurement → Manufacturing → Logistics → Warehousing → Distribution”

But in a Language-Based Analysis (LBA) approach, we don’t start with structure – we start with conversation.

We’d start by analysing what do the teams actually say?

Instead of mapping process flows, we’d begin by analysing the language used by each part of the supply chain, for example:

Demand planning team: Talks in terms like “confidence intervals,” “forecast horizon,” “demand signal noise.”

Manufacturing operations: Talks about “batch sizes,” “changeover efficiency,” “plant utilisation.”

Logistics: Refers to “load consolidation,” “delivery windows,” “on-time in-full (OTIF).”

Sustainability team: Focuses on “carbon intensity per SKU,” “embedded emissions,” “scope 3 visibility.”

Each group is operating with a valid model – but their language frames their reality differently, creating misalignment in expectations and incentives.

Conflicting metaphors = conflicting decisions (and reduced congruence)

Language can describe and list things, but it’s also metaphorical. LBA reveals that even common metaphors are loaded with different assumptions:

“Just-in-time” suggests precision, minimal waste, tight tolerances. “Resilience” implies buffers, optionality, and slack.

These ideas often clash in planning conversations.

For example, a planner might suggest holding 3 weeks of buffer stock “for resilience,” while the finance lead counters that this “kills working capital”

LBA surfaces these underlying narrative conflicts early – before they become programme blockers.

Value framing: Everyone’s speaking in a different currency

Different teams may describe “value” in ways that don’t align:

Procurement: “lowest total landed cost”

Sustainability: “climate-positive procurement strategy”

Customer service: “maximising fill rate without backorders”

Finance: “protecting gross margin”

These are not just different metrics – they’re different worldviews. LBA helps make this visible.

Semantic mismatches create hidden friction and prevent reaching congruence

Misunderstandings are often subtle and systemic:

“Product availability”

• To the warehouse team: “stock is in the system.”
• To the retailer: “I can get it on the shelf tomorrow.”

“SKU rationalisation”

• To marketing: “reduce range clutter.”
• To production: “minimise changeovers.”
• To supply chain finance: “optimise carrying cost.”

“Forecast accuracy”

• Planning means statistical precision.
• Sales means “we hit the number we told the board.”
• Operations means “can we trust it enough to schedule labour?”

Each of these semantic mismatches leads to poor handovers, rework, and misaligned decisions.

We could then build a semantic map of:

• Shared concepts
• Conflicts in terminology
• Gaps in understanding
• Lost meaning in handovers
• Incomplete or ambiguous data labels

And this might reveal that there are gaps in understanding:

• Planning uses probabilistic models, but operations wants commitments. For example, a planner might say: “There’s an 80% chance we’ll need that SKU in week 42,” but Ops asks: “Look, do I order it or not?”
• Sustainability uses a future-forward, impact-oriented language, while procurement is often contract-anchored in today’s prices.

Legacy data labels hide meaning

A field in SAP labelled “Product Class” is used:

• By sales to mean product category (e.g. “personal care”)
• By R&D to mean technical platform (e.g. “sulfate-free formulations”)
• By logistics to determine storage requirements (e.g. hazardous, refrigerated)

These mismatches cause data integrity issues, failed automation, and flawed reporting.

For this FMCG company, the supply chain isn’t just a flow of goods – it’s a network of conversations about those goods. Language-Based Analysis can show where those conversations align, where they break down, and where transformation requires semantic, not just systemic, change.

How could this method link language to value exchange?

A key innovation here: language becomes the infrastructure of value exchange.

We’re analysing:

• Internal dialects (Finance vs Ops vs Design)
• Value contracts embedded in documents, workflows, and platforms
• Narratives that determine investment, urgency, or inertia

We’re looking at how:

• Language creates poor levels of congruence across functions
• Language shapes expectations, permissions, and obligations
• Misunderstandings cost money, trust, or time

Practical LBA Tools (I’m just making this up to get the thinking out…)

This different approach also suggests creating different tools and artefacts:

(The language drift report has nothing to do with Gran Turismo, it’s a way of understanding the degree of divergence, I can imaging this involving spider diagrams?)

## Broader implications of the approach

When you start looking at organisations through a language lens, the potential applications go way beyond just digital transformation.

Inclusion

LBA helps surface accessibility and comprehension issues. In many large organisations, people are excluded not because of capability, but because the language used is overly technical, abstract, or filled with jargon.

For example, someone in a warehouse might struggle to understand a new planning dashboard because it’s designed with finance terms.

Or neurodiverse employees might interpret certain metaphors differently — leading to misunderstanding or stress.

LBA can help you design communication that is genuinely inclusive — by spotting which terms confuse, exclude, or alienate people.

AI Alignment (because it’s not going away, and if we simply focus on improving current processes things will not end well)

LBA makes sure AI systems are trained on the right kind of language.

As organisations increasingly use LLMs (like CoPilot or internal GPT-style tools), bots, and automated data pipelines, it becomes essential to make sure those systems understand the contextual meaning of terms used internally.

For example:

A bot trained on general English might misunderstand “product class” if that term means something specific in the customer’s organisation organisation.

LBA can ensure your AI tools are:

• Trained on the real, domain-specific language the people use.
• Aligned with how our customer’s teams define value, make decisions, and interpret data.

Change Management

With LBA, you’re not just training people on new systems — you’re aligning shared language. Traditionally, change programmes train people how to use new tools or follow new processes.

But often what people struggle with is:

• Understanding why the change is happening.
• Adapting to new terms, metrics, or labels.

For example:

A new CRM system might use the term “opportunity” — but that might mean different things to marketing, sales, and delivery. LBA ensures you’re aligning the meaning, not just the mechanics — helping people understand, adopt, and own the change more effectively.

So where does all this leave us?

“Transformation fails not because systems don’t connect, but because meanings don’t align.”

Language-Based Analysis re-centres the conversation around:

• Meaning
• Value
• Alignment

It treats communication as the architecture, not just a layer on top of it. It allows transformation to start with dialogue.

Fun quote: Barthes argued that meaning is created not by the author but by the reader. Translate that into enterprise transformation: the organisation isn’t defined by top-down architecture alone, but by the language acts (and interpretations) of its people. The enterprise can be drawn in an org chart, but is actually defined by what is said and understood.

The idea is simple: instead of facing a blank application with difficult questions, less privileged users can type a short, free-text description of what they want to achieve (e.g. “I need help with childcare while I study for a new qualification” or “I have a small business idea I want to pitch”). The LLM then pre-populates the form fields with suggested text, creating a first draft that users can edit, delete, or refine.

(Check out the prototype)

Two things are common to the teams I’ve worked with:

Firstly, they all put LLM experimentation in a separate ‘ai’ team, sometimes with an emphasis on governance, sometimes technology, sometimes both, but never in the hands of front-line people actually shaping digital services.

And secondly, most of them are primarily driven by the mitigation of risk (or at least perceived risk). This means that design speculations or futures experimentation don’t really happen. This is a shame because I believe this makes it harder to pinpoint areas where the technology can provide a helpful boost to users.

We've spent so much time worrying about precision and accuracy that we may have missed out on the potential to be helpful...

So I’m sharing some of these examples in case others are interested in how we might use AI to lower barriers to access, promote inclusion, and encourage people to claim the support they deserve (I’m using the cheapest model as a proof of concept—there are many faster and better models available for those with budgets).

It would be great to hear thoughts—especially from public sector colleagues in service delivery, social impact, or those who have witnessed these challenges firsthand.

Possible future explorations

I haven’t looked at these yet, but I’m thinking it could be helpful to create similar experiments for these sorts of services.

Start up loans (via the British Business Bank)

• The UK government offers loans and mentoring for entrepreneurs, yet many people think they won’t qualify, or that it’s only for “real businesses.”
• An LLM-powered tool could demystify the process and pre-fill the loan application form, reducing anxiety over formal requirements.

Adult skills courses & training (local councils)

• City councils and adult education centres offer free or low-cost courses (e.g., digital skills, business basics), but many assume they need prior qualifications or a certain background.
• A user-friendly interface driven by AI could guide someone through eligibility checks and highlight key course benefits.

The King’s (ex Prince’s) Trust enterprise programme

• This programme helps young people (18–30) start businesses with training, mentorship, and funding. Some individuals assume it’s only for “the really talented” or “unreachable” types.
• An AI-driven questionnaire could pre-populate a simple pitch outline, making the initial approach less daunting.

Tax credits or universal credit support for childcare

• Parents juggling work or studies can receive help with childcare costs, but the forms can be complex, and the eligibility criteria confusing.
• There is a lot more to tax credits than how good or bad the forms may be. It’s an incredibly complex space. That said, some form of GPT / agent might be mitigate some difficult language or automatically complete parts of potential applications.

Local chambers of commerce

• Growth hubs (often run in partnership with local enterprise partnerships) provide free business advice and networking. Many think it’s “only for established companies.”
• With AI, a user who perhaps has never started a business before might describe their idea in everyday language and get a draft membership application or introduction email tailored to their region’s hub.

Have I missed any? What would you like to solve first?

Testing the approach with a Car marketplace example

If I wanted to understand the market and one operator’s place it in (let’s say Carwow for this example) I would build out propositions for each operator.

So here are a couple of examples. For both of these value propositions we assume the customer is: Individuals in the UK looking to sell their car quickly. Who they are: Private car owners, often with vehicles that are difficult to sell privately or via trade-ins. They may value speed, simplicity, and convenience over securing the highest price.

Example value proposition: Carwow

Carwow addresses the frustrations of traditional car buying by simplifying the process, providing transparency, and ensuring a user-centric experience. It’s particularly appealing to tech-savvy buyers who value convenience and cost savings.

Example value proposition: Webuyanycar

WeBuyAnyCar appears to align well with its customer segment by addressing the specific pains (hassle, risk, and time) and delivering gains (speed, simplicity, security). The company has positioned itself as the go-to solution for quick, stress-free car sales in the UK.

What we learned (comparison of customer pains and gains by competitor)

What it might mean (who is Carwow’s biggest competitor?)

Based on these unvalidated and guessed ratings, Motorway could be Carwow’s biggest competitor.

Strengths shared by Carwow and Motorway?

• Transparency in pricing: Both platforms allow dealers to bid on cars, creating competition and higher transparency for sellers.
• Competitive offers:
  The auction-style model enables sellers to secure higher prices compared to fixed-price services like WeBuyAnyCar (maybe… is this true?).
• Simple process:
  Both have streamlined online systems that minimise hassle for sellers.
• Focus on dealer networks:
  Both connect sellers directly with dealers, bypassing the need for branches or private buyers.

Motorway’s edge over Carwow?

• Uncertainty about car valuation: Motorway’s valuation process is highly transparent, and sellers often get bids that reflect the market value, making it slightly stronger in this area.
• Hidden fees: Motorway consistently emphasises a fee-free experience for sellers, which can be a deciding factor.

Carwow’s differentiators?

Carwow is a broader platform that also supports buyers (especially for new cars), meaning it benefits from brand recognition among a dual audience.

Head-to-head comparison

• Motorway is better suited for sellers prioritizing price maximization.
• Carwow has an edge with its cross-functional platform for both buying and selling.

However, for pure car-selling services, Motorway is the closest and strongest competitor to Carwow.

But this is entirely make-believe and just the result of a half-hour look online. To manage this proposition will require ongoing research, planning, and creative.

What next?

So had some feedback, I guess I need to sort out a couple further posts:

LLM-enhanced business design tools

I’ve now built some LLM demos for creating most of the more frequently used tools - writing a business case, testing the big idea, defining a value proposition, crafting business model canvas, setting up lean testing etc. I should probably explain how those work and why you might want to use them. The main idea was that smaller businesses either might not know about these things, not can they afford fancy consultants to guide them, so the tools are a little ‘self-serve’ experiment.

Method deeper dive

There are lots of question around choices versus assumptions, how you map specific research to the proposition, why certain pains or gains make for more useful comparisons etc. I’ll have to unpick this and share when i get a minute…

The intepretation of this policy let to the universal adoption of ‘cookie banners’. While these were intended to empower users with choice and transparency, they have, in many ways, detracted from the quality of the user experience online. Basically, GDPR broke the internet.

The issue at hand is not the GDPR policy itself, but rather the disjointed approach taken in its implementation. The policy was developed with noble intentions by policymakers, but the execution was left to digital delivery teams, who often had to interpret the rules and implement solutions in isolation. This siloed approach resulted in a wide array of cookie consent banners that vary in design, clarity, and user-friendliness, leading to what many users perceive as an annoyance rather than a meaningful choice. This disconnect highlights a critical flaw in the traditional ‘waterfall’ approach to policy and digital delivery, where policy is formulated and then “thrown over the fence” to those responsible for its digital execution.

The unintended consequence of this approach has been that far from empowering users, the proliferation of intrusive and confusing cookie banners has led to ‘consent fatigue,’ where users blindly accept cookies without understanding their implications, thus undermining the GDPR’s original intent.

This is how a valid policy intent can lead to negative outcomes when the delivery mechanism is not adequately considered or integrated into the policy design process.

But what could have been if the GDPR policymakers and digital delivery teams had collaborated more closely from the outset?

Imagine a policy lab where the GDPR was co-designed using design thinking and service design methods. In such an environment, a more user-centric approach could have been taken, acknowledging the importance of both privacy protection and user experience.

In this alternate scenario, the teams might have conceived an asynchronous solution to address the policy’s intent while preserving the quality of the internet experience. For example, tracking details could be stored on a secure, government-operated website. A tool, accessible to all internet users, would then alert individuals to their tracking status and provide options to delete or block specific cookies. Such a solution would not only align with the GDPR’s privacy protection goals but also mitigate the negative impact on user experience by removing the need for intrusive consent banners.

This highlights the importance of considering the user experience of the policy from the outset, and involving a diverse range of stakeholders, including technologists, designers, policymakers, and end-users, in the policy development process.

Have you been briefed on policy intent and outcomes? Are you working on a similar challenge? Would you like to ask about how a policy co-design lab could work in your organisation?

I’d be happy to hear more about it.

I watched a colleague use her phone to perform a regular task and be surprised by the arrival of a new feature that had been deployed at the last update (she doesn’t read release notes and never watches tutorial videos).

What’s (very?) unusual about this is:

• She was not interrupted by the new feature
• She was able to understand the new feature without explanation
• As she completed her task, the value brought about by the new feature became immediately apparent
• The new feature did not change the value of her financial arrangement with the vendor in any way
• The new feature did not cause the UI to break with the zoom setting she uses (New York Times she’s looking grumpily at you on that front…)
• The feature did not add complexity or slow down her experience
• There was no muscle memory driven usage that she could no longer do after the feature was released

Overall, this update improved her experience, helped her cocreate value, improved her security, made her feel safer, and let her do her task more quickly.

I may be exaggerating but it’s been literally years since I’ve experienced something like this.

What does it mean? I’m not sure, but I thought I’d share…