Shadow AI a step too far, or an opportunity?

Date:
Fri, 19 Jun 2026 11:03:29 +0000

Description:
Businesses are facing a new challenge - shadow AI. Teams are unleashing the potential of AI, but IT isnt always aware and thats creating a real risk related to shadow AI.

FULL STORY ======================================================================Copy link Facebook X Whatsapp Reddit Pinterest Flipboard Threads Email Share this article 0 Join the conversation Follow us Add us as a preferred source on Google Newsletter Subscribe to our newsletter Businesses are facing a new challenge - shadow AI. For decades, enterprise teams struggled with shadow
IT, in which employees would bypass procurement processes and approvals to adopt their own cloud platforms and SaaS apps.

Today, employees are rapidly adopting generative AI, AI copilots, and automation platforms outside of controls put in place by centralized IT department. Teams are unleashing the potential AI, but IT isnt always aware and thats creating a real risk related to shadow AI. Claire Agutter Social Links Navigation

Founder of Scopism and author of the SIAM Foundation and Professional Body of Knowledge. The speed of AI adoption is outpacing governance. Yes, employees should absolutely be experimenting with AI. It can automate manual tasks,
help employees focus on higher-value work, and drive better decision-making. Latest Videos From Watch full video here:

The challenge is that companies arent always aware of AI usage. Unlike traditional software, AI models and automation tools dont require significant infrastructure or procurement. Users can adopt new tools right away, without ITs involvement. Operational visibility As adoption grows, AI is getting
baked into departmental processes without proper governance or oversight. Companies are no longer just struggling with procurement. Theyre struggling with operational visibility. You may like Shadow AI and agents like OpenClaw are hijacking corporate data too easily What is Shadow AI, and why should
your business be concerned about it? If everyone is rushing to board the AI ship why are so few workflows secure?

They dont know what AI tools their employees are using. They dont know what data is being uploaded. They dont know where sensitive data is stored and
that leaves companies exposed to operational, compliance, and reputational risk.

Vendor sprawl is creating additional complexity, as it is one of the biggest challenges with unmanaged AI usage. Many companies have woken up to the fact that AI is already being used throughout the business. The problem? Theyre trying to simplify operations by adding more AI tools on top of already fragmented technology landscapes. Are you a pro? Subscribe to our newsletter Sign up to the TechRadar Pro newsletter to get all the top news, opinion, features and guidance your business needs to succeed! Contact me with news
and offers from other Future brands Receive email from us on behalf of our trusted partners or sponsors By submitting your information you agree to the Terms & Conditions and Privacy Policy and are aged 16 or over.

Every department is procuring different AI platforms. Employees are creating custom automations. Suppliers are dropping AI capabilities into their
products with little oversight.

Its created a disjointed ecosystem of tools, suppliers, and automated workflows. Vendor sprawl is only exacerbated when you throw multiple
providers into the mix, such as outsourcing vendors, public cloud platforms, and SaaS vendors, where accountability is divided among parties. AI blind spots Add AI into the mix and youre creating additional blind spots. When an automated workflow breaks, produces incorrect results, or violates compliance standards, who is responsible? The AI model supplier? The underlying software vendor? The automation platform? The person who deployed it? The data source? What to read next More and more businesses are struggling to deal with 'uncontrolled' AI How enterprises can safely scale agentic AI The next AI
arms race: governance as trust

If companies dont have strong governance around AI activity and service integration, these questions will be difficult to answer. AI transformation
is an operating model problem, not just tech and many are looking at AI transformation the wrong way. Rather than just trying to deploy AI tech, leaders need to consider how AI tools are used across the business.

Building AI resilience isnt about using the most AI tools. Its about building governance, responsibility, and operational resilience into AI activity from the start.

It requires a shift in mentality. IT teams cant just be gatekeepers anymore. More teams will use AI tools with or without IT approval. Attempting to restrict AI usage will lead to more shadow IT.

IT and IT service management teams need to evolve to focus on service integration, governance, and operational oversight. This includes: Creating transparency into AI usage Setting responsible AI usage guidelines Managing supplier risk Integrating AI into operational workflows Setting
accountability for AI-driven decisions Enabling innovation with proper governance Organizations need end-to-end visibility into AI usage across teams, suppliers, automation tools, and third-party AI services. Without it, there will be cracks that appear in their operational resilience.

Governance will become even more important as regulatory pressure mounts. Governance is only going to become more important as legislators turn their attention to AI. With new regulations like the EU AI Act coming into play, as well as new interpretations of existing data protection legislation,
companies are going to be expected to account for how AI tools are monitored, governed, and used.

But most companies are adopting AI long before they have considered AI governance. By the time governing bodies step in to regulate how businesses use AI, businesses will likely be far behind on governance considerations.

Employees may inadvertently share sensitive information through public AI systems. Companies may start using AI-generated content for customer-facing operations without fact-checking or validating quality. Internal decisions
may be made by automated workflows with no visibility or auditability. 4
Steps to avoid a shadow AI crisis Its not too late for businesses to avoid a shadow AI crisis. However, they need to take action to responsibly manage AI tools and usage. Understand how AI is being used Gain an understanding of
what AI tools are being used across the organization, by who, and for what purpose. This includes informal or department-led initiatives happening outside of IT. Define responsible usage guidelines Set clear guidelines for responsible AI use, data practices, supplier risk management and accountability. You dont need to create restrictive approval processes. But you should create practical guardrails for teams to follow. Treat AI as an operational service AI is increasingly being integrated into
business-critical workflows. As such, it should be treated like any other critical service. Define whos responsible for AI activity, how suppliers are managed, and how security and compliance is enforced. Approach AI governance as a company-wide initiative AI governance shouldnt be the sole
responsibility of IT. Procurement, security, HR, operations, legal, and executive leadership all need to work together. The reality is organizations dont need to fear AI. But a starting point is recognizing how unknowingly theyre already losing visibility and control around the technology thats increasingly powering their business . We've featured the best AI chatbot for business. This article was produced as part of TechRadar Pro Perspectives , our channel to feature the best and brightest minds in the technology
industry today.

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