As AI becomes more embedded in business decision-making, the need for trust and transparency is rising fast, says Richard Farrell, CIO at Netcall.
While AI offers significant opportunities, it also brings risks such as bias, opacity, and increased regulatory scrutiny. That is where AI governance comes in: a framework of policies and practices that ensure AI is used responsibly. More than 75 per cent of customer service leaders say they feel pressure to implement AI, but pressure and FOMO is not a strategy. AI is here to stay, and as it advances, the way it is adopted is going to change. As pressure to implement mounts, so does the likelihood of something going wrong. Without proper governance, businesses risk rolling out AI in ways that damage customer trust rather than strengthening it.
Adoption at speed, risks at scale
AI adoption is accelerating. In just two years, businesses have gone from cautious testing to embedding it in daily operations, from language generation to intelligent docs and autonomous agents, all to cut costs, speed up resolution, and improve CX. Wider adoption means greater risk. Bias, black-box decisions, and weak safeguards can rapidly break trust. In regulated sectors such as healthcare or insurance, small AI errors, missed escalations, wrong discharges, or flawed outputs, can spark reputational damage or regulatory fallout. In healthcare the stakes are even higher.
The mistake many businesses make is treating AI as a standalone tool. The smarter approach is embedding AI directly into the workflows and systems where it operates, with governance built in from the start – often through platforms designed to support this by default.
Building AI on a foundation of governance
Many organisations start with an AI tool and then try to force it onto a use case. This often leads to wasted investment, ineffective solutions, and unresolved business challenges. The approach needs to be flipped. Organisations should begin by identifying their biggest pain points and then determine how AI can help. By grounding adoption in real business needs, they avoid chasing hype and instead build AI applications that deliver value and can be governed effectively.
Governance should be part of the process from the very beginning. Clear benchmarks and guidelines – such as audit trails, escalation rules, and privacy protocols – need to be embedded as part of the system design, not bolted on after deployment. The most effective implementations make these elements part of the user journey through intuitive, adaptable frameworks. Embedding governance upfront reduces the risk of rushed rollouts that later require costly fixes.
Prioritising human judgement
Trust in AI depends on knowing when to escalate. A well-governed system ensures that AI defers to human judgment in complex, emotional, or high-stakes scenarios. This blend of automation and oversight is essential to maintaining both control and confidence in any organisations AI implementation.
To build confidence, suppliers should adopt responsible practices and support customers as they complete their own data ethics documentation. While suppliers don’t dictate ethical frameworks, their role in supporting customers through best practice processes reinforces a shared commitment to responsible implementation. This approach helps buyers embed ethical safeguards more confidently, signalling that governance is not just a checkbox, but a shared priority.
Whilst governance is often seen as red tape, it can actually accelerate adoption when framed as a trust-building tool. Customers are more likely to embrace AI services when they know accountability is built in. Regulators are reassured by transparent oversight, and employees gain confidence when they know safeguards exist. Companies that treat governance as a competitive advantage, rather than a compliance burden, will move faster and earn greater trust in the long run.
Proof in practice: How governance drives competitive advantage
That’s the theory. But how does governance play out in practice? The organisations seeing the greatest impact from AI are those that treat governance as a starting point and implement the right frameworks, often supported by experienced partners. As AI models evolve, organisations need implementation approaches that can pivot quickly without losing transparency, oversight, or trust.
At Hampshire Trust Bank, an AI-powered sentiment analysis application was implemented, combining low-code configurability and AI governance. By quickly and efficiently identifying and addressing emails which show customer dissatisfaction, and might escalate to a formal complaint, HTB can react quickly to resolve the issue.
This direct control meant the business could govern how customer communications were processed, prioritised, and escalated in real time. The result was not just operational efficiency, it was assured regulatory compliance and a clear, defensible audit trail that proved how every decision was made. Governance did not sit on the sidelines; it was wired into the workflow.
For NHS Rotherham Foundation Trust, a governance-first mindset laid the foundation for exploring responsible AI. Using an integrated platform approach, the Trust piloted the use of AI to predict which patients were at the highest risk of missing outpatient appointments. By analysing historic attendance data, the system segmented patients by Did Not Attend (DNA) risk and tailored messaging for those most likely to miss their slot.
The results were powerful: the DNA rate in the highest-risk group dropped from 48% in April to just 16% by July. This marked improvement wasn’t achieved by overhauling services, but by applying intelligence to a persistent challenge: missed appointments. By communicating more proactively with those at risk, the Trust was able to drive behaviour change at scale.
The approach freed up valuable clinical time, reduced wasted appointments, and ensured patients received care when and where it mattered. The success offers a compelling case for how AI, when guided by strong governance, can tackle inefficiency and unlock better outcomes for both systems and citizens.
That’s because embedding capabilities into existing business logic with flexible, user-led tools makes it easier to stay in control, adapt to regulation, and respond to challenges as they arise. Since not every organisation can hire data scientists or build bespoke AI engines, governance must be scalable, designed to bake transparency, auditability, and safety into business systems from the very beginning.
From risk to responsible AI
As AI moves from pilot to production, the companies that win won’t be the fastest adopters. They’ll be the ones that embed trust at every level – in systems, decisions, and customer interactions. That means treating governance not as a compliance exercise, but as core infrastructure for performance, resilience, and growth.
The C-suite mandate is clear: build AI you can defend, explain, and scale. That’s how you move faster – without compromising what matters most.
