For AI early adopters, agentic workflows are dissolving traditional data silos. But what does that mean for human departments?
Netiro partners Luke Hutchison and Chloe Foy explore why breaking data silos may be easier than breaking habits, hierarchies and behaviours.
A glimpse of the future
“The vast majority of businesses are at level one with their AI use. They’re using it to allow everyone to work a bit faster,” says Luke Hutchison, Technology & Transformation Partner at Netiro.
“AI vendors would have them believing they’re behind the curve, but it’s OK. I’d recommend first getting your data security and training in place. Not everyone needs to be a first mover.”
Level two introduces AI agents to take on whole chunks of a workflow. “Right now, without careful planning this can become expensive and fiddly,” says Luke. “There’s some advantage to waiting, the technology will get better and early adopters will iron out the bugs.”
And those early adopters are already offering a glimpse of the future. They’re showing that AI works best when it has access to the right data, regardless of silos.
Most businesses aren’t set up for this yet. Work is organised around departments, each with its own systems, information and responsibilities. Processes and data cross those boundaries, but there’s no complete picture from beginning to end.
So as early adopters have begun mapping workflows and preparing for AI to work across human boundaries, a question emerges – do the traditional boundaries still make sense?
Data silos are dissolving
“Some early adopters are showing how traditional silos of people (sales, IT, operations, HR, etc) begin to dissolve once information is connected across a workflow,” says Luke Hutchison.
“This creates an opportunity to reconsider how people are organised. Instead of separate teams passing information between them, you can start thinking about one team supporting the whole process.”
He predicts four significant changes in how businesses organise their people.
- Broader roles. Instead of staying in one department, specialists retain their expertise but apply it across different projects and challenges.
- More fluid teams. Instead of fixed departmental responsibilities, people with complementary skills work together around shared objectives.
- Flatter management structures. Some traditional management layers become less necessary when information flows freely and AI coordinates routine activities.
- Greater emphasis on human capabilities. Judgement, creativity, empathy, adaptability and collaboration become increasingly valuable as AI takes on more routine work – more on this later.
Human value
As AI takes on more of the routine work, strategic human value becomes about people applying their expertise more broadly, connecting knowledge and capabilities across functions.
“You’ve spent ten years learning an expertise, and that’s still valuable,” says Luke Hutchison. “So instead of staying in your own department, we’re going to put you in a project team where you can apply that knowledge to challenges across the entire process.”
But redesigning the flow of information is one thing. Changing the deeply ingrained behaviours, hierarchies and identities built around departmental boundaries is altogether harder.
“We’ve organised people into roles and they’ve become territorial,” says Luke. “‘I’m marketing, you’re sales, you shouldn’t be touching that.’ Now we need to help them look beyond that.”
Strong collaboration from the start
Technology moves quickly, but redesigning roles, responsibilities, and ways of working – not to mention preparing people – is a longer game.
Culture and Behaviour change expert Chloe Foy says the collaboration between people and technology teams is key.
“The CIO and CHRO need to become best friends,” she says. “The risk is that businesses implement the technology without planning how people’s roles and skills can evolve into something more meaningful and then communicate that with them.
“Without that, it feels like cold, transactional adoption instead of embracing a more relational approach towards ‘transformation’.
“And instead of calling it ‘AI transformation’,” she says. “Take the time to break it into smaller, more manageable ‘transitions’ to help people move through the change more easily.
“That’s behavioural science 101.”
A practical experiment: Start with the work
There are practical steps businesses can take today to get those conversations moving.
Luke, who advises businesses in how to navigate the people aspects of technological transformation, suggests choosing one process to start with:
Map how it happens, from beginning to end. Get the people responsible into the same room and deconstruct it. Where does information get held up? Why are decisions passed between departments? Where is human judgment genuinely required?
Then experiment. Combine specialists from across that process and give them shared responsibility for finding a better way to deliver the outcome. If all the relevant data was available at all times, which steps could disappear? How might responsibilities change?
Where could human judgement and combined expertise create something better?
“Try something, see if it can work, move to the next thing,” says Luke. “You don’t have to jump into all of it straight away.”
The immediate value might be a faster process or an opportunity to automate. But the longer-term prize comes from helping people grapple with the question of what the future looks like for them.
“The barrier to entering this future won’t be technology,” says Luke Hutchison. “It will be our ability to imagine and define new ways of working.”
Is your organisation ready to work differently?
At Netiro, we help leaders look beyond existing structures to understand the people, skills and ways of working they’ll need for the future.
