The proportion of Irish business leaders who dismiss artificial intelligence as overhyped has collapsed from 45% to 23% in just six months, according to new data from Grant Thornton’s International Business Report. That dramatic 22-percentage-point drop signals a decisive pivot from scepticism toward active deployment among mid-market firms across Ireland. Yet the same survey reveals a parallel spike in anxiety: 58% of executives now cite privacy as the top barrier to AI adoption, up from 35% six months earlier.

The findings, drawn from a survey of 10,000 mid-market businesses across 32 countries, paint a picture of Irish SMEs racing to operationalise generative AI tools like ChatGPT and Microsoft Copilot while grappling with the governance and security challenges that accompany them.

Shane O’Neill, Technology and Digital Consulting Partner at Grant Thornton Ireland, confirms that “executives are no longer debating whether the technology is ‘over-hyped’. Over the past six months, we’ve heard that Irish mid-size businesses are shifting from cautious experimentation to confident deployment of AI.”

Policies Surge as Firms Formalise AI Rules

The rush to adopt is matched by a sharp rise in governance activity. The report shows that more than half of all firms now require staff to follow a formal AI-usage policy when using generative AI tools, a jump from just 37% six months ago. O’Neill notes that companies are “writing policies, training staff and embedding tools into day-to-day operations. That practical focus is driving productivity gains, particularly in data analysis, customer support and internal knowledge-sharing.”

Concrete successes are feeding the momentum. The share of firms citing “difficulty determining productive uses” has plunged from 48% to just 22%, indicating that hands-on experience is rapidly revealing authentic, high-value applications. Use cases such as contract summarisation, customer email triage, and automated report generation are delivering measurable time savings and ROI that sceptical boardrooms can no longer ignore.

The Paradox: Soaring Confidence Meets Mounting Privacy Fear

But the optimism carries a hard edge. Privacy has surged to become the number-one barrier to AI adoption, cited by 58% of Irish executives—up from 35% half a year ago. The top worry: employees inadvertently uploading sensitive client data, payroll records, or intellectual property into public large language models. This data-exfiltration risk is far from theoretical; unsanctioned “shadow AI” usage is rampant, and even a single leak could trigger a GDPR breach notification, reputational damage, and significant fines.

“Optimism alone won’t close the trust gap,” O’Neill warns. “Until organisations can guarantee that sensitive data fed into AI systems remains secure and compliant, adoption will continue to bump against serious privacy concerns. The winners here will be those firms that treat governance and transparency as strategically important.”

Why the Shift Happened So Quickly

Four converging forces have compressed a years-long technology adoption curve into a matter of months.

1. Rapid diffusion of usable models and vendor pushes. Lower-friction access to capable large language models, coupled with the seamless embedding of tools like Microsoft Copilot into Microsoft 365, has moved AI from research labs to employee desktops. Vendors are pushing hard; Irish firms are clicking “enable” and witnessing results in minutes.

2. Measurable, early wins. Use cases with clear, short-cycle productivity gains—email triage, document summarisation, drafting of standardised replies—provide visible proof points. Once a department head sees a 30% reduction in report preparation time, scepticism evaporates.

3. Pressure to remain competitive. Irish mid-market firms often sit in global supply chains where customers and partners expect efficiency. Falling behind on AI-driven productivity can mean losing contracts. The IBR data shows technology investment intentions rising, and AI is the centrepiece of that spending.

4. A regulatory and reputational wake-up call. The EU AI Act, high-profile data mishandling incidents, and growing client demands for data-assurance statements have forced boards to treat governance as urgent. For many, establishing a policy is no longer optional—it is a prerequisite for scaling AI without catastrophic mis-steps.

What the Shift Means for Irish SMEs: Strengths and Opportunities

The emerging landscape offers three clear advantages for firms that act decisively.

Faster workflow automation. Irish SMEs can immediately attack high-volume knowledge tasks—contract analysis, customer response generation, standard report drafting—freeing skilled staff for higher-value work. Grant Thornton and PwC consistently identify this as the surest short-term return.

Lower barriers to advanced capabilities. Pre-built APIs and SaaS-based copilots allow smaller firms to access language, image, and data-analysis models without having to hire expensive machine-learning teams. This democratisation levels the playing field with larger competitors.

Governance as a competitive differentiator. Building clear policies, training employees, and embedding approved, secure tools reduces both risk and friction. Customers are increasingly asking vendors how they use AI and how they protect data; a well-communicated governance posture can win business.

The Downside: Risks That Keep Irish CIOs Awake

For all the promise, the same survey data underscores the hazards.

Data exfiltration and exposure. Shadow AI—employees pasting sensitive data into consumer-grade chatbots—is the single biggest operational hazard. Enterprise tooling without contractual data-protection clauses offers little improvement.

Compliance complexity. The EU AI Act and GDPR demand careful mapping of data flows. Firms that fail to record model usage, treat AI outputs as potentially containing personal data, or neglect data-subject rights risk enforcement action and contractual breaches.

Model reliability and “hallucinations.” Generative AI can produce plausible but incorrect outputs. When these outputs appear in customer communications, legal drafts, or financial decisions, the business impact can be severe unless robust human review is embedded in the workflow.

Skills and change management gaps. Most mid-market firms lack in-house AI expertise. Even when leadership supports adoption, frontline staff need hands-on training in prompt engineering, data governance, and output verification.

Inconsistent policy enforcement. A written policy is worthless if it languishes on an intranet page. Without technical controls—data loss prevention rules, proxy blocking of unapproved domains, and audit logging—shadow AI will persist.

A Practical Roadmap for Irish Mid-Market Firms

Irish SMEs can navigate the paradox of opportunity and risk by following a structured, pragmatic set of steps. These distill insights from the Grant Thornton data, PwC research, and real-world implementation experience.

1. Create a clear, concise AI-usage policy. Publish a one-page summary and a detailed operating guide. Specify approved tools, forbidden data types, and escalation paths. Make it job-specific and easy to understand.

2. Sanction and secure approved tools. Identify enterprise-grade AI tools with contractual data protections and block unapproved services via network or endpoint controls. Negotiate vendor SLAs that explicitly forbid use of customer data for model training.

3. Deploy technical controls. Use Data Loss Prevention (DLP) rules to detect and block uploads containing sensitive patterns—IBANs, PPS numbers, client codes—to cloud LLM services. Enforce data minimisation: never paste raw customer data into prompts.

4. Mandate human-in-the-loop sign-off. Require human review for all AI-generated outputs intended for external use or regulated processes. This reduces both hallucination and compliance risk.

5. Monitor and register AI usage. Maintain a central register of AI models, vendors, data flows, and update cadences. This will satisfy upcoming regulatory audit requirements and inform vendor risk assessments.

6. Invest in targeted training. Run short, role-specific training modules that cover when and how to use AI, data-handling rules, and methods for testing outputs. Appoint internal “AI champions” to support colleagues.

7. Run measurable pilots. Choose a bounded use case with clear KPIs—such as first-response time in customer support—and run a 6–8 week trial with control cohorts. Scale only after proving ROI and security robustness.

8. Prepare an incident response playbook. Develop templates for breach notifications to customers, regulators, and employees. Know who to call and what to say before something goes wrong.

Vendor Due Diligence: The Checklist That Can Save a Business

Vendor selection is a make-or-break exercise. Before signing with any AI provider, Irish firms should insist on answers to six questions:

  • Does the vendor offer a contractual non-use clause (customer data won’t train public models)?
  • Is bring-your-own-key encryption available for data at rest and in transit?
  • Can data residency be guaranteed within the EU/EEA to meet GDPR requirements?
  • Are comprehensive audit logs and transparency tools provided?
  • Has the vendor completed independent security certifications such as SOC 2 and ISO 27001?
  • How are model updates managed and communicated?

Prefer vendors that support enterprise deployment patterns—on-premises, virtual private cloud, or private cloud—where workflows involve sensitive information.

Where Irish SMEs Should Invest Next

The path from pilot to profit demands a deliberate investment sequence:

  • Governance foundation: policy, risk register, and alignment with the Data Protection Officer.
  • Secure tooling: enterprise LLM instances, private deployments, and contractual shields.
  • Training and change management: role-based playbooks and continuous skill-building.
  • Measurement capability: dashboards that track business outcomes (time saved, error reduction) rather than vanity metrics.

These priorities echo the IBR’s core message: governance unlocks adoption; adoption without governance invites severe downside.

Strengths and Blind Spots in the Current Irish Response

Irish mid-market firms deserve credit for moving rapidly from debate into practice. That speed can generate real competitive advantage. Many are now writing policies and training staff—an operational focus that is the single most important shift for converting hype into durable productivity.

Yet notable blind spots remain.

Overreliance on vendor marketing. Not all enterprise offerings carry equal protections. A “commercial” licence does not automatically mean data is ring-fenced from model training. Reading the fine print is essential, and due diligence is often under-resourced.

Insufficient technical enforcement. Policies without DLP, identity controls, and secure deployment configurations will fail to stop shadow AI. Industry studies repeatedly document a gap between governance on paper and technical reality.

Auditability and reproducibility challenges. Most generative models are black boxes, making audit trails hard to establish. For regulated processes, organisations must treat AI outputs as provisional until data provenance and model behaviour are demonstrably auditable.

The Strategic Choice Ahead

Ireland’s mid-market stands at a crossroads. The IBR data captures a single truth: Irish executives have decided AI is worth doing, but they are far from complacent about how to do it safely. The collapse in scepticism—from 45% to 23%—and the concurrent rise in privacy concern to 58% show a business community that is clear-eyed about both the prize and the peril.

The winners will be those firms that weave governance, transparency, and technical controls into their AI strategy from day one. They will invest in vendor due diligence, DLP, staff training, and measurable pilots. The losers will be those that treat AI as a buzzword, issuing a one-off policy memo without enforcement, or rushing a tool rollout without safeguards.

In the next twelve months, the Irish mid-market will be separated not by who experimented with AI, but by who scaled it safely and strategically. The foundation for that success is being laid now—through pragmatic policy, secure tools, and a relentless focus on turning anxiety into action.