When public cloud first became widely accessible, it helped teams move faster. Developers could spin up services, test ideas and remove friction without waiting weeks for infrastructure. But as adoption accelerated, governance did not always keep pace. Services were sometimes bought and used outside central IT oversight, creating Shadow IT: technology used within a business, but not managed through approved processes.
AI is now creating a familiar pattern.
Employees are using it to write, summarise, analyse and automate everyday tasks. The intent is productivity, not recklessness. But without clear rules, approved tools and visibility over data use, AI risks becoming the next generation of Shadow IT.
We spoke to our Redcentric Cloud experts, about why AI adoption feels similar to the early days of public cloud.
The pattern is familiar. In the early days of cloud, people used it because it helped them move faster. AI is being adopted for the same reason. The difference is that this time, it’s not just developers. It’s everyone.
Key points
- AI is creating a familiar challenge for business leaders: how to support fast adoption without losing visibility, governance or control
- Shadow AI is rarely driven by bad intent; it often happens when people use helpful tools before approved routes are in place
- Strong AI outcomes depend on strong data foundations, including clear ownership and trusted sources
- Approved processes should help people use AI safely, with appropriate tools and controls, and the right model for each use case
From shadow IT to shadow AI
To understand the risk, it helps to revisit the meaning of shadow IT.
Shadow IT was rarely about bad intent. It often came from friction. People needed a faster way to get something done, and cloud services made that possible. The issue was not always the use of cloud itself, but the lack of control around it. Shadow AI works in a similar way. It happens when people use AI tools outside approved processes, often because they are trying to streamline their day. The tool may be useful, but the organisation may not know what data is being uploaded, where it’s going, how long it’s retained, or whether it could be used to train models.
The question for business leaders isn’t whether people will use AI. Many already are. The question is whether they have a safe, clear and governed way to use it.
Why AI creates a wider governance challenge
The biggest difference between early cloud adoption and AI adoption is scale.
Public cloud initially put infrastructure into the hands of technical teams. AI puts powerful automation into the hands of almost everyone. Marketing, finance, HR, operations, customer service and IT teams may all have reasons to experiment.
That creates more points where risk can enter the organisation. Someone may paste sensitive information into an unapproved tool. A team may start relying on AI-generated outputs without checking the quality of the underlying data. A workflow may become dependent on a platform that hasn’t been assessed by IT or compliance teams.
That doesn’t mean businesses should block AI. If they do, usage may simply move further into the shadows. A better response is to understand how people want to use AI, then give them safer routes to do it.
People want to use AI because it helps them do their jobs. If organisations do not provide a secure and practical way to use it, they risk relying on common sense as their main control.
AI cannot fix poor data foundations
AI is only as useful as the data it can access. If information is duplicated, outdated, poorly labelled or difficult to govern, AI will still produce an answer. The problem is that the answer may be based on the wrong version of the truth.
A person may spot three versions of the same document and pause to check which one is correct. AI may not apply that same judgement unless the environment around it has been designed to do so. It may simply treat accessible information as valid.
That makes AI readiness about more than choosing a model. Organisations need to understand where data sits, who owns it, how it’s updated and which sources are authoritative.
AI doesn’t fix poor data governance. In many cases, it exposes it.
Should businesses move AI to the data?
One of the most important questions is where AI activity should take place.
For some use cases, employees may need access to external information. Research, market scanning and public-facing content may need tools that work with wider public data. But for regulated or business-critical work, organisations may need a different approach.
Instead of moving governed data into external AI tools, they could explore bringing AI closer to the data. That might mean using private AI applications or controlled environments where existing security, access and audit controls already apply.
This is where AI cloud architecture and the wider mix of hybrid cloud environments become important. Different workloads need different levels of control. Some may suit public platforms. Others may be better supported in private or sovereign environments, especially where data compliance or resilience matter.
For some use cases, the safer model may be to move AI to the data, not move the data to AI. If the data is already protected and governed, why create another copy somewhere else?
Governance should enable AI, not block it
AI guardrails shouldn’t make adoption harder. It should help people use AI with more confidence.
If official routes are unclear, people will find their own. That’s what happened with cloud, and it can happen again with AI. Governance needs to reflect how people actually work.
A safer AI operating model may include:
- Approved AI tools for different use cases
- Clear rules on what information can and can’t be shared
- Data loss prevention controls to reduce the risk of sensitive information leaving the organisation
- Role-based access, so people can use the tools they need without unnecessary exposure
- Education that explains the reasons behind the rules
- Clear ownership across IT, security, data and operational teams
The lesson from cloud isn’t that organisations should slow everything down. It’s that the safer route needs to be the easier route.
Sovereignty matters when AI becomes business-critical
As AI becomes more embedded in business processes, resilience and sovereignty become more important.
If a team uses AI occasionally, losing access to a tool may be inconvenient. If AI supports critical workflows, the impact could be much greater. Organisations need to understand what would happen if an external platform changed its terms, restricted access or became unavailable.
As AI moves from experimentation into business-critical workflows, sovereignty becomes a question of resilience as much as data location. For organisations in regulated industries, private or sovereign approaches may help reduce reliance on platforms and decisions outside their control.
The key question isn’t simply where data sits. It’s whether the organisation understands who controls access, who can change the rules and what happens if a service changes or becomes unavailable.
Build AI adoption confidence with Redcentric
AI adoption doesn’t need to become another shadow IT story. The opportunity is real, and businesses shouldn’t ignore the productivity gains their teams are already finding. But AI needs the same level of thought now applied to cloud, cyber security, data governance and resilience.
The strongest approach isn’t to ban AI or adopt it accidentally. It’s to enable it deliberately.
At Redcentric, we help organisations build secure and well-governed technology environments across public, private and sovereign platforms. As AI becomes part of the next phase of digital transformation, those foundations will matter even more.
The goal is simple: give people the tools they need to work smarter, while protecting the data, systems and services the business depends on.
