The Ethics Imperative: Embedding Accountability into AI

As AI moves rapidly from experimentation to autonomous decision-making, leading lights are building with discipline, transparency and accountability from the very start. Ethical innovation has become a defining factor influencing whether AI transformation efforts succeed or stall. 

According to Informatica's CDO Insights 2026 report, 47% of organisations are adopting agentic AI, raising the stakes considerably when it comes to accountability, trust and regulatory readiness. At the same time, while around two-thirds (65%) of leaders say employees trust AI-driven data, 71% admit their workforce lacks the skills to use that data responsibly.

“This trust paradox, where confidence outpaces capability, is one of the biggest ethical challenges organisations now face,” explains Greg Hanson, Group VP and Head of EMEA North at Informatica, part of Salesforce.

“Companies cannot outsource ethics to AI. With AI often serving the ‘front door’ to brands, the first decisions customers encounter must reflect company values. That’s why ethics cannot be bolted on later.”

Ethics as a daily discipline

Integrating solid ethics into innovative, AI-powered projects must show up in how companies are building, testing and deploying the technology. Intelligence should be grounded in data that accurately reflects the environments in which it will operate, with performance validated under real conditions.

Johannes Maunz, SVP of AI at Hexagon, emphasises: “Ethical innovation is not something you appoint a single owner for or capture in a framework and move on from. In practice, it is a daily discipline that shows up in how AI is built, tested and used across the organisation.

“Ethics becomes real at the point where organisations decide how much responsibility they are willing to take for systems in deployment. If an AI system cannot be trusted in the real world, then using it at scale is not an ethical choice.”

For Roop Singh, CEO at Version 1 – the Ireland-based IT services firm – innovating in an ethical fashion begins with purpose. 

"Ethical innovation means starting with a purpose, not just adopting AI because everyone else is,” he says. “In practice, you have to define from the outset whether an AI initiative will create real benefit and serve a higher business objective, if it requires upskilling and if people are set up to succeed alongside it.”

Accountability enables speed

A persistent myth suggests accountability can slow innovation. 

Johannes challenges this misconception, explaining that what really harms innovation is uncertainty: “When teams are unclear about how a system behaves, where its data comes from, who is responsible when something doesn’t work as planned and where accountability lies.

“Clear guiding principles and their application through the process landscape remove that friction. When expectations around data use and ownership are defined early, teams are able to move faster with greater confidence.”

Roop’s assertion is that speed without clarity tends to lead to confusion. 

“It is so important to match technological ambition with leadership, honest communication and support,” he says. “The trust comes from helping people understand from the outset what’s changing, why it matters and how they fit into the plan.”

Similarly, Greg highlights the need to balance speed with trust, which itself requires transparency into how AI systems use data, make decisions and evolve over time.

“Without clear visibility into data sources, context and decision pathways,” he continues, “organisations cannot confidently guarantee the safety or ethics of their autonomous systems.”

Leadership sets the tone

Clearly, ethical responsibility for AI cannot be delegated solely to technical teams.

Johannes outlines the need for leadership figures to set clear direction and “ensure accountability is where it needs to be”.

He adds: “Leaders establish the boundaries within which AI can operate and make clear where human judgement must remain part of the decision-making process. “They also set the expectations for how risk is understood and managed across the organisation.”

Leadership is what truly sets the tone, according to Roop. If treated as “just another procurement decision, that is how the organisation will approach it”.

Instead, embedding AI in a strategy encompassing people, purpose and customers enables it to become transformational. 

“As leaders,” Roop adds, “we have to show that we're not just chasing efficiency – we’re building capability and empowering people.”

Then there is the AI literacy piece. Greg argues leaders must invest so employees understand how to question, challenge and apply AI responsibly. Fear, he says, often stems from misunderstanding, making education the most effective antidote.

Identifying and mitigating risk

In industrial AI, ethical risks often emerge when systems encounter the complexity of the physical world in ways not fully represented during development. Real-world conditions introduce variability that only becomes visible once AI is connected to physical assets or human activity.

Johannes offers a solution: “Technologies such as spatial models and digital twins provide a way to continuously compare what an AI system predicts with what is actually happening on the ground. This ongoing validation makes it easier to spot bias, drift or unintended effects early.

“Where AI outputs influence decisions with lasting consequences, human judgement must remain central. Feedback from live operational environments becomes essential for maintaining trust and ensuring systems continue to behave as intended over time.”

Greg places emphasis on governance and data quality, stating that ethical risk mitigation must begin with embedding clear guidelines and accountability into every stage of AI development.

“Strong governance structures, including oversight, ownership and auditability, create a foundation of trust and transparency,” Greg notes. 

Organisations must therefore understand where data comes from, how it is used and how it changes over time.

The technique employed by Roop and Version 1 can be considered people-first: “We approach it by being deeply intentional about the problems we’re solving and ensuring humans stay firmly in the loop. 

“If you view AI purely as a cost lever, you are likely to miss the unintended consequences. But when AI is used to augment people, not replace them, it changes how you design, monitor and course-correct. That’s where trust is built.”

Ethical innovation as an advantage

Ultimately, as AI becomes more embedded in operations and regulation matures, organisations that demonstrate reliable and accountable use of AI will be better positioned to adapt.

Roop observes that customers and regulators are asking tougher questions than ever before, adding: “Organisations that treat AI adoption as a technical sprint will struggle to build long-term credibility, but those that embed ethics, trust and capability into how they deploy AI will be better positioned – both competitively and culturally. 

“Ethical innovation isn’t a side conversation – it’s becoming a defining factor in whether transformation efforts succeed or stall.”

Greg’s belief is that ethics and governance are set to become defining factors of competitiveness. Without a trusted data foundation, he says, AI is “effectively guessing”. 

He adds: “The businesses that build strong ethical foundations will be better positioned to innovate faster, meet regulatory expectations and earn lasting trust from customers, employees and stakeholders.”

Johannes is in agreement with his peers, arguing that regulation will favour organisations with embedded discipline. 

“Clear standards reduce uncertainty and allow teams to innovate with confidence,” he concludes. “When ethics is embedded into how AI is designed and deployed, it stops acting as a brake on progress and becomes the mechanism through which innovation earns the right to scale.”

Ethical innovation now stands as the foundation upon which sustainable, scalable and trusted AI is built. Organisations that recognise this early will be well placed to move with confidence, credibility and the trust needed to succeed amid tighter regulation.