A LinkedIn post about my old company, insightsoftware, popped up the other day. It was lamenting the challenge of turning 29 acquisitions and close to 80 products into one comprehensive platform. I spent years in the middle of that work, and what I know now is how much harder that consolidation is than you might think, and how much of it you only learn by getting it wrong first.
So, when I arrived at Veson in November and found a similar landscape, I felt a bit of déjà vu. A bit of “I’ve done this before, so I know what I’m doing” and a bit of “I’ve done this before and I know it’s no cakewalk.” Veson had spent the past several years acquiring a range of products like Shipfix and VesselsValue and now needed to consolidate them onto a platform alongside its flagship IMOS solution. A new variable is also now at play that changes the whole equation: the advancement of AI.
While AI may sound like a cheat code for this work, it’s very much a double-edged sword… with a grenade taped to it.
Yes, we can do some things faster and differently than we ever could a few years ago. But we’re also learning as we go and upskilling our team as we go, on a technology that’s changing under our feet.
The newness of these tools multiplies the complexity in a way I didn’t fully appreciate at insightsoftware. Back then, the tools simply didn’t exist or were just emerging. Now your platform must transform so that it thrives in the age of AI. It has to be built in the age of AI, which — depending on whose study you believe — means anywhere from 2X to 10X faster.
And then your team needs to adapt to the age of AI, which again means faster, at higher quality and producing more output. That’s a lot of spinning plates, on top of each other, none of which you’re allowed to drop.
Oh, and one other plate: this is a centuries-old industry that moves billions of dollars in freight a year and does not stop for anyone. You don’t get to tie the ship up at the dock while you rebuild the engine. Business continuity, zero migration, no breaking the thing people are already running their business on. And do it as fast as you possibly can.
Building a connected, modern platform
At Veson we had a fleet, not a unified experience. A set of strong, separate hulls, each with its own data, its own experience, its own idea of the world. A collection of great products is not the same as a cohesive foundation, and pretending otherwise is a mistake. I’ve been guilty of this myself: combining products superficially, so they sit side by side under one roof but still behave as separate entities, rather than as one integrated experience.
In fairness to my past self, before AI even that superficial stitching was slow, labor-intensive and risky, and it often used up the time and budget we’d meant for real integration. AI has worn much of that cost away. The plumbing that used to take months now takes weeks, which frees us to focus on what customers value most: products that work as one.
A platform, in 2026, is three things stacked in order. A singular data layer underneath. One experience on top. And then AI that can reason across all of it. You cannot bolt credible AI onto a flotilla; it has nothing coherent to reason over. The unglamorous work of unifying data is what makes the glamorous AI work possible.
The part I’m most wary of talking about — because I would rather not tempt fate — is doing all of this safely. Moving fast on all fronts at once is a lovely thing to say in a strategy meeting and a dangerous thing to do in production. The mistake I’ve watched happen (and, if I’m honest, helped cause) is measuring efficacy too narrowly.
If you pick only a few metrics (that you know you can nail) and tell people that’s how they’ll be judged, teams will act accordingly. The side effects get ignored or missed until it’s too late. Often that side effect is quality, discovered downstream when it’s far costlier to fix, or market fit, because value was quietly descoped to hit a date. With AI coding agents accelerating everything, I wasn’t willing to repeat that. We needed 360-degree checks and balances.
The one thing I insisted on and set up from the first week with Veson was working with the engineering intelligence platform Plandek. We measure speed, quality, predictability and focus in real time, so we can see the progress we’re making and spot the side effects before they compound.
AI let us pick up the pace; comprehensive measurement is what lets us keep it without losing sight of the risk. As a result, we get to have our cake and eat it, we upskill the team while the ship is sailing.
Ten months in, here’s what we’ve learned so far.
Product lessons we’ve learned
- Don’t rebuild what already works. “Modernizing” something people rely on is the fastest way to destroy the value you paid for.
- AI only counts when it functions inside a workflow people already trust. An assistant in a corner nobody visits is a demo, not a product.
- Aim for a minimum lovable product, not a minimum viable one.
- Continuity is a feature. The moment a customer must “migrate” to your platform, you’ve lost them.
- One source of truth beats five clever ones. Data you unify late is a tax you pay forever.
Experiences from our engineering teams
- Adoption will be uneven. Don’t force the sceptics; convert them with proof.
- The job itself is changing from writing code to directing it — reviewing, steering and trusting AI as it writes, then verifying.
- Spread what works peer to peer. A network of AI champions beats a mandate from someone who clearly hasn’t done the work.
- Measure everything.
- The fastest teams are the ones who trust the platform beneath them.
- Faster is only a win if quality holds — protect the gates hardest exactly when the pressure to cut them is highest.
I don’t think we’ve cracked the code on this. We’re in the middle of it, plates still spinning. But this time I’m making new mistakes rather than old ones, and I’m more convinced than ever that we’re doing this the right way.