UK Lags Global in AI Adoption; CGI Leaders Lay Out a Trust-First Blueprint for Modernisation

AI Strategy: CGI Leaders on Trust, Talent and Transforming Legacy into Advantage

UK enterprises are behind global peers on advanced AI adoption. CGI's Voice of Our Clients 2025 points to a gap: 21% of UK firms report advanced AI adoption vs. 35% globally, with 63% citing legacy systems as the main blocker and a growing risk from misinformation.

CGI executives Tara McGeehan (UK and Australia President) and Russell Goodenough (Head of AI) offer a blunt assessment and a path forward: treat trust as a product feature, modernise with intent, and make AI part of everyday work-starting at the top.

Where UK Businesses Stand

The data is clear: legacy estates, skills gaps and trust concerns are slowing progress. That stalls productivity gains, delays automation benefits and weakens competitiveness.

There's also the external risk: 24% of leaders in CGI's research flag misinformation as a challenge, which can erode confidence in both brand and AI systems.

What CGI Brings (and why it matters)

CGI has delivered AI and intelligent automation within end-to-end services for over two decades. Their teams apply a Responsible Use of AI framework, secure model testing and training, and industry-specific IP-plus partner ecosystems across cloud and automation.

Translation for executives: proven playbooks, guardrails that regulators and boards can live with, and delivery models that move pilots into production without surprises.

The Adoption Playbook: From Pilots to Production

• Set the tone at the top: Senior leaders use AI in their own work. Make the intent visible and practical.
• Govern for trust: Establish clear policies, risk thresholds, and model approval paths. Document decisions and owners.
• Start small, scale fast: Phase rollouts, pick measurable use cases, and publish success criteria upfront.
• Build internal advocates: Create AI champions across functions; reward reuse of patterns and playbooks.
• Train in the flow of work: Hands-on sessions tied to live tasks beat classroom theory.
• Track outcomes: Cycle time, accuracy, customer impact, and cost-to-serve-not vanity metrics.

Closing the Talent Gap Without Waiting on Hiring

The issue isn't just headcount-it's the pace of change. Public sector teams feel this acutely while competing with private sector packages.

• Make AI part of daily work: Embed tools where tasks live (email, docs, service desks). Reduce change friction.
• Peer networks and champions: Share playbooks, patterns and do/don't lists. Normalize experimentation with guardrails.
• Cross-functional adoption squads: Pair process owners with engineers and data leads to target specific workflows.
• Partner for momentum: Bring in external experts to accelerate pilots and transfer capability-not dependency.
• Leaders model usage: Show your prompts, your dashboards, your decisions. Visibility creates permission.
• Protect learning time: Bake self-led learning into objectives and calendars.

If your teams need structured pathways, explore focused upskilling by role at Complete AI Training - Courses by Job or curated certifications at Popular AI Certifications.

Preparing for Quantum and Neuromorphic (without the hype)

Boards should treat these like AI: build governance, resilience and clear digital strategies first. Maintain visibility over your tech estate, invest in observability and plan for disruption.

Encourage responsible experiments and upskilling. The goal is to be adoption-ready the moment the cost-benefit tips in your favor.

Modernising Legacy: MSPs and Cloud as Force Multipliers

Legacy blocks agility and value capture. Managed-service partnerships and cloud migration create a path to modernisation while managing compliance, cost and risk.

The upside is continuous improvement: MSPs evolve systems as business needs shift and free internal teams to focus on innovation, growth and outcomes-rather than maintenance.

Digital Trust: Guard Your Inputs, Models and Brand

Misinformation can poison training data and public perception. Treat data quality, security and explainability as board-level priorities-alongside cybersecurity.

Useful references: the NIST AI Risk Management Framework and NCSC guidance on secure AI development (UK NCSC).

90-Day Executive Action Plan

• Days 0-30: Appoint an exec sponsor; agree AI principles and risk thresholds; inventory legacy and data; select three high-value use cases; define success measures.
• Days 31-60: Launch two controlled pilots with guardrails; stand up an AI governance board; train champions; begin MSP/cloud due diligence; implement data quality checks.
• Days 61-90: Decision gates for scale vs. stop; set budgets and operating model; roll out org-wide enablement; implement model monitoring and incident response; publish a trust report to the board.

Metrics That Matter

• Production use cases per quarter and time-to-value
• Cycle time and accuracy improvements by process
• % of employees using approved AI weekly
• Data quality score and model drift alerts
• Cost-to-serve and unit economics impact
• Security posture: patch latency, incidents, model access audits
• Reputation and trust signals: customer NPS, sentiment shifts

Executive Takeaway

Trust, talent and tech must move together. CGI's message is straightforward: build strong foundations, upgrade what blocks progress, and make AI useful for every role-starting with leadership behaviour.

Do that, and legacy becomes leverage. Delay, and the gap widens.