Business users are building AI solutions themselves before calling the experts. Here’s what this means for your organisation.
SwissCognitive Guest Blogger: Mario Grunitz – “Beyond the Tech Team: How Business Users Became AI Power-Users”
The brief that surprised us
Something interesting started happening about eighteen months ago. Client briefs began arriving differently.
Not with vague requests for “AI transformation” or theoretical discussions about machine learning potential. Instead, we received functioning prototypes. Working MVPs. Sometimes, even full applications that teams had built themselves.
“We need help taking this to the next level,” they’d say. The shift was striking.
Business users weren’t waiting for permission anymore. They were building first, optimising later. This isn’t just anecdotal evidence from our client base, it represents something fundamental changing in how organisations approach technology.
The democratisation of AI tools has arrived, and it’s reshaping everything.
When everyone becomes a builder
The old paradigm was clean: Business teams requested. Technical teams built.
Clear boundaries, defined roles and predictable processes. But that world is disappearing rapidly. What we’re witnessing is the emergence of business builders: people who tackle technical challenges directly, armed with domain expertise rather than coding skills.
The evidence is everywhere:
- Marketing teams now build their own campaign workflows.
- Sales departments create lead scoring systems from scratch.
- Operations managers develop predictive dashboards.
- HR teams implement recruitment screening algorithms.
This trend is particularly pronounced in the startup ecosystem, where AI is disrupting traditional development approaches and forcing founders to rethink how they build and scale their businesses. Startups, with their resource constraints and urgency to validate ideas quickly, have become natural laboratories for business-led AI development.
This democratisation creates something remarkable. Business problems are now being solved by people who understand them intimately.
The new superpowers in action
Here’s what business-led AI development looks like in practice:
A retail client’s merchandising team built their own demand forecasting system using off-the-shelf AI tools. They understood seasonal patterns, customer behaviour, and inventory cycles better than any external developer could.
Their solution? More accurate than the enterprise software they’d been using for years.
In financial services, risk analysts are creating their own fraud detection models. They know suspicious patterns because they’ve been identifying them manually for decades. A manufacturing company’s quality control team developed an image recognition system for defect detection, training it using thousands of photos from their own production line.
Each case follows the same pattern: domain experts identifying problems, finding accessible AI tools, and building solutions that work.
The results often surpass traditional development approaches. Why? Because the builders understand the nuanced requirements that specifications rarely capture.
Almost 60% of custom apps are now built outside the IT department, with 30% built by employees with limited or no technical skills.
Enthusiasm meets complexity
This democratisation comes with challenges, one of them being a very real and sometimes steep learning curve. People dive in with tremendous enthusiasm but limited understanding of technical implications. They build solutions that work beautifully in isolation but struggle to scale or integrate with existing systems.
Security considerations often get overlooked. Data governance becomes an afterthought. User experience principles remain mysterious.
I’ve seen marketing teams build brilliant automation workflows that accidentally expose customer data. Operations teams create dashboards that look impressive but tell misleading stories.
The gap between “making it work” and “making it work properly” can be substantial.
This is where choosing the right development approach from the start becomes crucial. Whether it’s selecting appropriate low-code platforms for rapid MVP development or understanding when to scale beyond initial prototypes.
Yet here’s what’s fascinating: these business builders are incredibly good at knowing when they need help. They understand their limitations better than traditional developers sometimes do.
“We’ve proven the concept,” they’ll say. “Now help us make it professional.”
The compound effect of distributed intelligence
Organisations embracing this trend are discovering unexpected advantages, mainly because when business users become AI-literate, everything changes.
Problems get identified faster. Solutions emerge from unexpected quarters. Innovation cycles accelerate because you’re not waiting for development resources to become available.
The cultural shift is profound: instead of “wait for IT,” teams adopt “try it ourselves.” Instead of lengthy requirements gathering, they build prototypes that communicate needs more effectively than any specification document.
Traditional technical teams are evolving, too: First, from gatekeepers to enablers. Then, from builders to advisors and optimisers. The most successful technology leaders are embracing this change by creating frameworks for business-led development rather than trying to control it.
Nurturing the revolution
How do you harness this momentum effectively?
Here’s what works in practice:
- Start with curiosity, not training programmes. Formal AI education often overwhelms business users. Instead, encourage experimentation with simple tools and real problems.
- Celebrate small wins publicly. When someone automates a manual process or builds a useful dashboard, share it. This creates permission for others to experiment.
- Create safe environments for experimentation. Provide sandbox environments where people can build and break things without consequences.
- Connect learning to real business problems. Abstract AI concepts don’t stick. Practical applications do.
- Establish clear escalation paths. Define when and how business builders should seek expert help. Make this feel like progression, not failure.
The goal isn’t to turn business users into professional developers. It’s to help them solve problems more effectively using increasingly powerful tools.
Leadership’s role is crucial here: support the experimenters, provide resources, remove barriers, and resist the urge to control every aspect of this transformation.
The future is already here
This transformation represents more than just a technological shift. It’s fundamentally changing how work gets done. By 2026, citizen developers will outnumber professional developers by 4:1.
Organisations that embrace business-led AI development will outpace those that don’t. Not because the technology is superior, but because the human intelligence directing it is more distributed and more aligned with actual business needs.
The companies getting this right aren’t trying to manage every AI initiative centrally. They’re creating conditions where innovation can emerge organically.
The most exciting part? We’re still in the early stages of this transformation. As AI tools become more powerful and more accessible, the potential for business-led development will only grow.
The most profound changes rarely announce themselves. They begin with someone asking a simple question: “What if there’s a better way?” A marketing manager, tired of manual reporting. An operations team, frustrated with slow dashboards. A sales rep who knows exactly what makes a good lead but has no way to automate that knowledge.
These moments of curiosity, not strategic mandates, are driving the real AI revolution.
About the Author:
Mario Grunitz is a European tech entrepreneur and investor with two decades of experience in Cloud, SaaS, AI, and mobile. He co-founded WeAreBrain, an award-winning product development company and venture studio, and has launched several ventures, including Tur.ai, clevergig, SOLVING, and Grover. Alongside his business work, he chairs the WeAreBrain Foundation, supporting humanitarian, ClimateTech, and AI regulation initiatives.
Business users are building AI solutions themselves before calling the experts. Here’s what this means for your organisation.
SwissCognitive Guest Blogger: Mario Grunitz – “Beyond the Tech Team: How Business Users Became AI Power-Users”
Something interesting started happening about eighteen months ago. Client briefs began arriving differently.
Not with vague requests for “AI transformation” or theoretical discussions about machine learning potential. Instead, we received functioning prototypes. Working MVPs. Sometimes, even full applications that teams had built themselves.
“We need help taking this to the next level,” they’d say. The shift was striking.
Business users weren’t waiting for permission anymore. They were building first, optimising later. This isn’t just anecdotal evidence from our client base, it represents something fundamental changing in how organisations approach technology.
The democratisation of AI tools has arrived, and it’s reshaping everything.
When everyone becomes a builder
The old paradigm was clean: Business teams requested. Technical teams built.
Clear boundaries, defined roles and predictable processes. But that world is disappearing rapidly. What we’re witnessing is the emergence of business builders: people who tackle technical challenges directly, armed with domain expertise rather than coding skills.
The evidence is everywhere:
This trend is particularly pronounced in the startup ecosystem, where AI is disrupting traditional development approaches and forcing founders to rethink how they build and scale their businesses. Startups, with their resource constraints and urgency to validate ideas quickly, have become natural laboratories for business-led AI development.
This democratisation creates something remarkable. Business problems are now being solved by people who understand them intimately.
The new superpowers in action
Here’s what business-led AI development looks like in practice:
A retail client’s merchandising team built their own demand forecasting system using off-the-shelf AI tools. They understood seasonal patterns, customer behaviour, and inventory cycles better than any external developer could.
Their solution? More accurate than the enterprise software they’d been using for years.
In financial services, risk analysts are creating their own fraud detection models. They know suspicious patterns because they’ve been identifying them manually for decades. A manufacturing company’s quality control team developed an image recognition system for defect detection, training it using thousands of photos from their own production line.
Each case follows the same pattern: domain experts identifying problems, finding accessible AI tools, and building solutions that work.
The results often surpass traditional development approaches. Why? Because the builders understand the nuanced requirements that specifications rarely capture.
Almost 60% of custom apps are now built outside the IT department, with 30% built by employees with limited or no technical skills.
Enthusiasm meets complexity
This democratisation comes with challenges, one of them being a very real and sometimes steep learning curve. People dive in with tremendous enthusiasm but limited understanding of technical implications. They build solutions that work beautifully in isolation but struggle to scale or integrate with existing systems.
Security considerations often get overlooked. Data governance becomes an afterthought. User experience principles remain mysterious.
I’ve seen marketing teams build brilliant automation workflows that accidentally expose customer data. Operations teams create dashboards that look impressive but tell misleading stories.
The gap between “making it work” and “making it work properly” can be substantial.
This is where choosing the right development approach from the start becomes crucial. Whether it’s selecting appropriate low-code platforms for rapid MVP development or understanding when to scale beyond initial prototypes.
Yet here’s what’s fascinating: these business builders are incredibly good at knowing when they need help. They understand their limitations better than traditional developers sometimes do.
“We’ve proven the concept,” they’ll say. “Now help us make it professional.”
The compound effect of distributed intelligence
Organisations embracing this trend are discovering unexpected advantages, mainly because when business users become AI-literate, everything changes.
Problems get identified faster. Solutions emerge from unexpected quarters. Innovation cycles accelerate because you’re not waiting for development resources to become available.
The cultural shift is profound: instead of “wait for IT,” teams adopt “try it ourselves.” Instead of lengthy requirements gathering, they build prototypes that communicate needs more effectively than any specification document.
Traditional technical teams are evolving, too: First, from gatekeepers to enablers. Then, from builders to advisors and optimisers. The most successful technology leaders are embracing this change by creating frameworks for business-led development rather than trying to control it.
Nurturing the revolution
How do you harness this momentum effectively?
Here’s what works in practice:
The goal isn’t to turn business users into professional developers. It’s to help them solve problems more effectively using increasingly powerful tools.
Leadership’s role is crucial here: support the experimenters, provide resources, remove barriers, and resist the urge to control every aspect of this transformation.
The future is already here
This transformation represents more than just a technological shift. It’s fundamentally changing how work gets done. By 2026, citizen developers will outnumber professional developers by 4:1.
Organisations that embrace business-led AI development will outpace those that don’t. Not because the technology is superior, but because the human intelligence directing it is more distributed and more aligned with actual business needs.
The companies getting this right aren’t trying to manage every AI initiative centrally. They’re creating conditions where innovation can emerge organically.
The most exciting part? We’re still in the early stages of this transformation. As AI tools become more powerful and more accessible, the potential for business-led development will only grow.
The most profound changes rarely announce themselves. They begin with someone asking a simple question: “What if there’s a better way?” A marketing manager, tired of manual reporting. An operations team, frustrated with slow dashboards. A sales rep who knows exactly what makes a good lead but has no way to automate that knowledge.
These moments of curiosity, not strategic mandates, are driving the real AI revolution.
About the Author:
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