Why value and process matter more than technology
When we talk about AI in business, the conversation almost always starts with speed. Faster content creation. Faster automation. Faster everything.
But when I sat down with Lasse Rindom (AI Lead at Basico and host of The Only Constant), he challenged that instinct
“Sometimes it’s good that things are slow,” he told me. “It means we can predict what’s going on.” – Lasse Rindom
The myth of the greenfield
Too often, companies approach AI as if they’re starting from scratch. But as Lasse pointed out, the real world is brownfield. You already have processes, systems, and customers to serve. The soil is there for planting. Dropping a shiny new AI tool on top of that doesn’t transform the organisation. At best, it adds noise. At worst, it creates disillusionment.
“If you don’t start with outcomes, you won’t get outcomes. You’ll just get another underused pilot,” says Lasse.
We have to seed projects that can grow in the local environment.
From hype to hard numbers
What stood out most in our discussion was that we both agreed on the need to insist on metrics.
“We’ve treated AI like play money,” Lasse said. “But if you don’t attach KPIs, how will you know if it worked?”
That resonates with my client experience. We’re used to measuring everything else in business, yet AI projects often skate by without accountability. The danger is obvious: without metrics, you don’t know whether AI is truly improving quality or just giving the illusion of progress.
It’s rarely, if ever, my direct client contacts that are advocating these quick wins. More often, pressure comes from other teams who want simple, fast, and magical, and we need to mount structured counter-arguments.
Access isn’t adoption
Another myth Lasse challenged is that providing access equals adoption. ChatGPT alone has 127 million daily users – this technology doesn’t need cheerleaders. But making AI work for people in the flow of their jobs is a different story. Adoption comes from integration, not novelty.
I recently posted on LinkedIn about the infamous MIT “95% of AI projects fail” headlines. In a recent Forbes article by Jason Alan Snyder, he added context to this, saying that the failure comes from an avoidance of friction. Friction refers to the three types of resistance that actually help GenAI initiatives succeed when handled well:
- Human friction: which comes from habit, fear, or uncertainty, forces buy-in and ensures tools are used with intention.
- Organisational friction: which is rooted in policies, governance, or incentives, prevents premature scaling and keeps pilots aligned with accountability and process, and
- Technical friction:which is the challenge of systems lacking memory, learning, or context, highlights the need for adaptability so tools can handle complexity and growth.
In other words, rather than being obstacles to remove, these frictions act as checkpoints that slow things down just enough to encourage intentional design, proper alignment, and scalable technology, making them valuable in driving real ROI.
Quality beats speed
For Lasse, and I, the real promise of AI isn’t in acceleration but in reducing chaos and democratising access to understanding and insights. Auto-tagging, contract reviews, journey mapping, content restructuring, knowledge databases, and transactional analysis may not be glamorous, but they add structure and context where organisations need it most. That’s how you build trust, and trust is the real currency of long-term adoption.
What I appreciated most about this conversation is that we both questioned how we can make AI dependable inside the messy, brownfield reality that most organisations actually live in. Not the glossy keynote version, but the day-to-day grind where systems don’t line up, processes clash, and humans still need to trust the output.
So maybe the smartest move right now isn’t to run faster, but to slow down, define value, and build processes where AI and humans catch each other’s mistakes.
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Listen to the full episode
Listen to the last episode of the OmnichannelX Podcast to learn how to build the right foundations first, so that when technology is introduced, it drives real impact instead of chaos.
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