Humanising AI
The future is not made better by meeting it faster. It is made better by meeting it wisely.
This is the largest, fastest, most consequential technology shift most of us will see in our working lives (and we’ve witnessed a few haven’t we). The opportunity is real and it is right here.
The hard part is rarely the technology. It is the people and how the organisation actually puts it to work. This is where we help. Not to slow you down, but to make sure the thing you do actually has the desired short, medium and longer term impact.
The sameness trap
Here is the part the rush hides. When the answer comes out of a box, everyone gets the same answer.
An off-the-shelf AI solution gives you what it gives everyone else. The same capability, the same limits, the same experience your competitor just bought. Whatever edge you imagined you were purchasing dissolves the moment the tool is deployed as intended, identically, everywhere. Sameness is not an advantage. It is the short road to commodification, where the only thing left to compete on is price.
Complex organisations do not have off-the-shelf problems. They have particular people, particular histories, particular constraints a general tool cannot see. Meeting those needs is not a matter of better prompts. It is a matter of judgement, and judgement is the one thing the box does not come with. The question to ask is “should we” rather than “how can we”.
Design the right thing, then design the thing right
There is a way to capture the upside without buying the generic. It rests on two embarrassingly simple principles the rush routinely ignores.
Design the right thing. Start by understanding real needs. Not the needs you assume from a distance. Not the ones that would make the technology look impressive. The actual, messy needs people have in their real contexts. Skip this and you spend heavily on things nobody wanted.
Then design the thing right. Build small. Prototype with real people. Learn from what fails. Improve as you go. This is not only about avoiding expensive mistakes. It is how you find the solutions people actually want, the ones you could not have imagined from the boardroom.
Ask the ones who rushed
The organisations that moved fastest are already quietly walking parts of it back. Their experience is the clearest evidence of where the value actually lives.
Ford is the sharpest case. It spent three years and a great deal of money on AI tools meant to lift production quality, confident the technology would do the work. It did not. The company ended up rehiring around three hundred veteran engineers, the ones Ford itself calls its “gray beard” engineers, to fix what the automated systems could not.
Its own engineering leadership named the mistake without flinching.
“Mistakenly, we thought that by just introducing artificial intelligence and ingesting the design requirements that we had, that would produce a high quality product.” ¹
There is a detail here that should give any leader pause. Many of Ford’s most experienced technicians had already left before their knowledge could be captured, so the AI was trained on everything except the expertise that mattered most. The machine learned the process. It never learned what the veterans knew.
The rehired engineers helped Ford top an industry quality ranking it had not led in sixteen years. The expertise it had tried to automate away turned out to be the thing that fixed the problem.
Klarna went further and faster. The fintech replaced some seven hundred customer service agents with an AI assistant, froze hiring, and spent the better part of two years held up as the poster child for what AI could do to a headcount. Then quality fell, and the story changed.
Its chief executive was unusually candid about why.
“As cost unfortunately seems to have been a too predominant evaluation factor when organizing this, what you end up having is lower quality.” ²
The reversal was not a retreat from AI. Klarna still leans on it heavily for routine work. What changed was the recognition that stripping the humans out entirely, to chase a cost saving, cost the company something it valued more.
Neither Ford nor Klarna abandoned the technology. Both learned the same lesson the hard way. AI is only as good as the human expertise behind it, and pulling that expertise out to save money is a false economy that bills you later, with interest.
This is not two unlucky companies. It is the pattern.
IBM’s study of two thousand chief executives found nearly two thirds, sixty-four percent, admit that the fear of falling behind drives them to invest in technologies before they understand the value those technologies will bring ³ .
The rush is not a strategy. It is an anxiety.
We have been here before
The pattern is not new, only the vocabulary. Some of us cut our teeth in those heady internet years when everything seemed possible. We also remember the bust.
That era built some of the largest organisations alive today. It also burned through fortunes on ideas that never resonated, systems that made simple tasks hard, digital services that left customers longing for the days of reaching a person on the phone. And the lesson was forgotten almost as fast as it was learned. Organisations taught a generation to dread the technology by giving them good reason to, then seemed surprised when people pushed back, resisted, or quietly opted out.
We are at risk of doing it again. Every poorly built solution adds a little more frustration and a little more distrust. We are teaching people to distrust automation by handing them automation worth distrusting.
Many of us have seen this film, and we know how it ends. The excitement will meet the hard facts of human behaviour and economic gravity, and many of today’s AI initiatives will fail. But some will not, just as the internet eventually produced genuinely transformative companies. The work is telling the signal from the noise. The organisations that thrive will not be the ones deploying AI fastest. They will be the ones deploying it most thoughtfully.
This is the work we do at brkrs. We help leaders understand what people genuinely need before committing to a solution, and build the particular rather than buy the generic. The future is not made better by meeting it faster. It is made better by meeting it wisely. Just because AI can build something does not mean it should. But understand what people actually need, rather than what impresses technologists, and you might build something extraordinary.
1. https://www.foxbusiness.com/technology/ford-rehires-experienced-engineers-after-ai-misses-mark
2. https://www.aol.com/klarna-ai-replaced-700-workers-210647896.html
* The image is Ned Ludd (meant to provoke rather define my mantra)