5 Priorities for Brand Tech Leaders in 2025
How to stay ahead of the curves in the year of the snake.
It’s 2025.
Yes — really…
Let’s get straight to it. Here’s what you get out of reading this article.
It delivers on the title; so you get what that promises.
It’s not played safe with a ‘not in order of priority’. The priorities are in order. Strong opinions welcome on those.
And, lots of (gratis) recommendations. Like a Consultant would do, except without the 10k-a-day invoice.
Spoiler-not-a-spoiler — yes, this piece contains reference to AI. Avert your eyes should you already be incurably sick of reading about it.
Here we go…
1. Up your projects game
Why the cognitive dissonance?
Every ambitious organisation is continuously changing, and that means projects.
Most brands I’ve worked with have a veritable banquet of active tech change projects.
Projects underpin brand operational changes, drive growth initiatives, and are crucial to staying ahead of (or at least on par with) the competition.
Yet, projects are mostly hauled, huffed, grunted and painstakingly edged over the line by well-meaning, under-resourced brand teams; sometimes acting without best-practice knowledge and governance.
We want the change. We want the change to be done properly. But we don’t always give projects what they need to succeed. Why the cognitive dissonance?
If we were commissioning a delicious birthday cake — a Black Forest Gateau, let’s say — and so provided our star baker with the baking powder, the vegetable oil, and a few cherries, then told them to have it ready in a couple of hours, they’d rightly look at us like we’d just blown our nose on their sleeves.
Brands nailing change projects in 2025 will be a big difference-maker in turning OK years into good years, good years into great years, and great years into game-changing years.
And, here’s the thing. It’s not that difficult.
An article on excelling in technical project management for retail systems is coming soon — the below image is a quick preview. For now, check out my recommendations and stay tuned.
Recommendations
For tips to get into projects on the right terms, read this and this.
Quantify, quantify, quantify. We can make better cases for projects being given enough resources. Boil everything down to numbers, even if just rational estimates. If you're dedicating 0.5 days a week to a project instead of the recommended 1.5, and suspect it might slip without more input, quantify it: By how much? Why? How will it delay other work, and at what cost?
Bring in outside help. Pros and cons of permanent hires, right? We can all advocate strongly for hiring permanents. But, for stretched teams with a daunting project backlog, bringing in trusted specialist help that can hit the ground running (if you’re lucky enough to know someone like that…) can be a game-changer.
Recognise the value of a dedicated PM. It’s a terribly under-appreciated specialism, but I’ll get into that in the upcoming piece. For now, just take my word for it. Hire a great PM (there are plenty of average ones), focus them solely on PM work, and watch your projects deliver faster, with better results, with more control, lower stress, and suppliers taking notes on your approach.
2. Put data-driven decision-making (even more) front and centre
Data totalitarianism, this is not.
There is plenty of room for ‘gut feel’, instinctual decision-making.
Sometimes, there just isn’t data on which to make a decision, and good instincts naturally fill in the gaps where data can’t yet be.
Sometimes, the data tells us one thing, but we just know it’s pointing us in the wrong direction. We should trust that instinct. At worst, we’re wrong and we learn something. Ignore our instinct and have it proved right, and we might harden around instinct-oriented decision-making in future cases where data might be the better option.
(There’s a ‘but’ coming…)
But: when reliable data can be flexibly manipulated to answer targeted, important questions, and that can happen on demand, quickly, without manual work or lead times — that’s amore…
I worked with a streetwear brand in 2024 to set up a better stock automation into their Shopify sites.
As part of the launch of the automation we needed to establish, based on historical order data, how high order volumes were likely to be in:
A specific 1hr window
On a certain day of the week
For customers in a specific timezone (the Shopify site served multiple timezones)
The brand was using PowerBI and got the answer in a couple of minutes. It confirmed the volumes would be very low in our chosen launch slot.
Getting that decision quickly meant that we saved hours planning a more complicated go-live, and possibly even avoided putting a holding page up on the site temporarily to allow the stock to be added to the store safely.
Imagine if that level of clarity could be given to all of your departments to assist their most important decisions.
Recommendations
Tool up on BI. (If you already use a tool and are happy with it, ignore this bullet). It’s time to put more time and money into the pro BI tools. Specialist tools will pay for themselves many, many, many (many, many, many…) times over if utilised well.
Create a BI knowledge hub (in Notion or your equivalent) to make your BI tool more accessible for everyone. Here you can answer FAQs, provide how-to videos, document successes, publicise a feature roadmap, take requests — whatever helps build a culture of seeing value in data insights. (PS — Notion has built-in AI functionality to help generate 1st drafts of documents, and Loom is easy to use. It won’t take as long as you think. See priority 4!).
Start a Slack/messenger channel for BI comms which the hub doesn’t cover. Consider diverting relevant BI hub updates here via webhook.
Use cross-departmental work to identify where BI can support other teams. The more cross-departmental exposure tech leaders have, the better they’ll perform — our systems serve all departments, after all. If such opportunities are scarce, create them. Sitting in on a few extra meetings each month with merchandising, eComm, CS, Product, et al, shouldn’t be too demanding.
Again — quantify, quantify, quantify. Nothing proves value better than clear numbers: How much time is saved? How much profit is added?
3. Deliver value with AI
AI is here.
It’s very hyped, often misdefined, and it has actually been here for a while, but for the sake of a snappy opening gambit, let’s just say ‘it’s here’.
After months of exploring AI to understand and simplify its capabilities in our field, here’s my take:
AGI is still a way away, if it will ever be possible. (So don’t build your bomb shelters just yet).
Generative AI is helpful but output must be verified before production use.
Most AI projects don’t deliver profit. (Some stats suggest 85% of AI projects don’t even get finished).
Most AI ‘Agents’ are automation or AI workflows in disguise. (Learn about the difference here).
True AI Agents can deliver value but not without hard project work, lots of learning and some compromise.
The potential of AI is very real, but the current position is closer to potential than easy profit.
But the arrow of time points in one direction — greater and greater outsourcing of human work to machines via automation, workflows and AI tools.
I primarily work with and write for brands with annual turnovers of £20m–£200m. These brands have been outsourcing to automation for years, and that trend will continue.
But, the next evolution is to outsource more and more work to AI.
Right now that might just be customer service chatbots, smarter search tools for website shoppers, ideas generation from ChatGPT, etc, but soon the capabilities of AI tools are likely to be much more advanced.
When that moment comes, the scale-up/challenger brands moving quickly and smartly on AI will have a clear advantage over their competitors.
Recommendations
Make ‘AI Research’ a part of someone’s role. Hiring a dedicated AI specialist internally might be overkill now, but may not be for long. For the moment, assign someone to regularly check where AI functionality is and think about profitable applications for you.
If you don’t have the team for the above bullet, consider engaging with a recommended AI Consultant. But, only if you have genuine use cases where AI can better what automation or other, readier tools can do.
Start taking in trusted AI content. I like this blog. This podcast. Some voices on LinkedIn seem pretty sensible, example. But, find your own too. (And if you find good ones please share them back with me. There’s too much for any one person to sift through…).
Set a goal to deliver X profit with AI tools by the end of 2025. This is a win-win, really. You can set the target high and achieve it, in which case, great! You can set it low and achieve it; also great. Or, you can set it anywhere and fail (which, given the stats, might be a pretty likely outcome). In this case, the experience will still mean you’re much better placed to capitalise on the proliferation of different tools heading our way soon.
4. Make this the year you finally document everything
I know, I know.
‘There’s never enough time to document things’.
‘We tried to document things but ended up just kind of…stopping’.
‘Some things change so quickly documenting them is a waste of time’.
You agree with me on the principle, though.
We know that documenting ‘as we go’ is worth the effort.
We know it pays us back in droves later on.
And I know from painful experience that it’s not easy to do.
We’re busy.
There’s always a higher priority.
People don’t always use the things you do document.
I get it.
But thanks to various improved tools, providing good documentation has never been easier than it is now. Therefore, it might be time to psych yourself up for another crack at it.
Now, I’m not on commission by Notion (…), but I have been using it a lot recently.
Once I got over the clunky early stages of the learning curve, it’s been great.
With a bit of persistence and some upskilling, you can build something pretty good, fast.
So, inspired by my recent Notion experiences (but substitute in your preferred tool — hopefully it’s as good or better than Notion!), here’s how I’d make creating documentation easier.
Templates for all different page types, page sections and buttons.
Buttons that generate those templated pages with one click. (Means pages are uniform in format and content).
AI to create first drafts of knowledge hub documents.
With all of the above set up, a user could have a 1st draft of a new how-to article set up in under a minute.
Carve out 20–30 minutes a week for a select group of people in the business to work on documentation, and in a month you should be seeing significant results.
5. Find the hidden savings
It seems we’ll continue through challenging economic times for growth in 2025.
Most of us are (at best) uncertain about the impact on the global economy of the impending 2nd Trump term.
Growth in the UK (where most of my readers are) is flatlining.
Take out the US, and growth in the G7 as a whole is pretty insipid.
These are the macro conditions we find ourselves in and they affect us whether we acknowledge them or not.
Now is a good time to get the tech stack as lean as possible.
Recommendations
Create a tools inventory with associated ongoing costs (if not already available).
Go through a ‘right-sizing’ process. Remove under-utilised tools. Identify tools with overlapping functionality and scale back as necessary. Check user licenses adoption. Unused test environments. Etc.
That’s all folks
Thanks everyone for giving this a spin — all my winter well-wishes to you.
A quick summary:
AI = warm, not yet hot. But, start getting acclimatised now lest it cook us all in the near future.
Projects still a priority and often under-valued. We can make them better.
Data-driven decision-making still under-utilised in Retail and adds demonstrable value.
It’s never been easier to master documentation, and we all know what that unlocks. (PS, try Notion).
2025 might be a great time for thriftiness given the macro picture.
Until next time.