The trust imperative: 5 levers for scaling AI responsibly

At a recent conference, in a room with over 100 Fortune 500 founders, only two had mission-critical generative artificial intelligence (GenAI) use cases in production. The reason? Quality, accuracy and hallucinations are holding them back.

This lack of trust in centring AI in their business was echoed in a recent Edelman study, which found that trust in AI companies in the United States has fallen from 50% to 35% over the last five years.

GenAI is undeniably transforming how we work but the stakes for enterprises couldn't be higher. Data protection and security, an influx of new AI regulations and the need for consistent accuracy in mission-critical work leave zero room for mistakes.

Bias, misinformation and a lack of transparency in AI decision-making mean enterprises have become more cautious about adopting AI.

AI is at a pivotal moment. Deployment cannot wait but trust is essential for success. Our work with hundreds of enterprises has identified five levers – transparency, reliability, alignment, privacy and security and fairness – as a blueprint for building scalable, responsible AI.

Transparency is one of the biggest barriers to AI adoption. Without it, trust in the data, outputs and system itself is impossible.

However, transparency must be built into the process from day one. This includes where models learn, how and why they make decisions, and whether those decisions align with business needs. Is there transparency in the chain of thought?

When leaders understand and can communicate how the AI they deploy works, they create a culture of collaboration and shared responsibility that drives real adoption.

Transparency also reframes the technology as a tool that complements our human capabilities, not a "black box" operating beyond scrutiny.

AI systems that can't deliver reliable results aren't just inefficient; they're a liability. Even small mistakes or inaccuracies can have serious consequences in high-stakes, mission-critical situations, particularly in industries such as banking or healthcare.

Generic models might be fast but they are not purpose-built to handle the complexity of enterprise data or deliver the accuracy required for production-ready software.

Your AI system should reflect your brand's voice, ensure consistency across outputs, meet industry-specific compliance requirements and stay adaptable so you don't constantly rebuild.

A related but vital component of accuracy is ensuring consistency across apps and workflows. Fragmented AI systems operating in silos can create oversight gaps and inconsistencies.

Think critically about your AI ecosystem and whether your models are tailored to your business. Are your systems aligned across workflows? Do you have mechanisms to monitor and improve performance? Can your systems adapt as models evolve or become outdated?

Reliability is not a trade-off for innovation. "Close enough" won't cut it.

Building trust means showing that AI's potential lies in enhancing how work gets done, not replacing the people doing it. For AI to succeed at scale, leaders must see the full scope of an AI transformation and ensure that vision is shared from the top down.

They must involve their teams in shaping organizational, process and collaboration changes during AI implementation. If people don't understand why or how you're changing how things are done, you won't see the kind of adoption we all want.

As we enter 2025, the focus will shift to rewiring organizations, remapping workflows and redefining roles to integrate AI effectively. Ambitious leaders understand this and are driving real buy-in and meaningful impact.

Data protection is central to any AI strategy. Leaders must make security, privacy, and compliance measures non-negotiable by building on preexisting frameworks to safeguard information and ensure systems operate within compliance requirements.

This approach provides the foundation to confidently scale AI responsibly without compromising safety and security.

Data protection isn't just a best practice; it's becoming a regulatory expectation as new policies manage AI's risks and potential. From the European Union AI Act to privacy and data protection laws, the global regulatory landscape is rapidly changing – and for good reason.

The pace and scale of AI are unprecedented. Organizations that treat compliance as fundamental to their AI strategies will be best positioned to build trust with their users and stay ahead in this shifting environment.

Hold your vendors to high standards. Ask them tough questions, such as how long they retain your data. Do they use your data for model training? How do they secure your inputs and outputs?

Setting the tone for data security from the outset establishes a foundation of trust for your AI initiatives.

A human-led approach is essential to ensuring that AI serves everyone. Ask yourself: who are you building this for? What problem are you solving? What goals are you trying to accomplish?

Your AI roadmap should focus on building representative datasets, mitigating bias, testing for reliable accuracy and maintaining a human in the loop.

This human-centric approach ensures that the AI systems you use and develop are trustworthy, reliable and technically sound.

Embedding humans into AI development and scaling ensures fairness and effectiveness, from unbiased loan approvals to equitable treatment recommendations.

This approach helps catch blind spots and ensures AI serves everyone.

AI will be transformational but only for those who are ready and it won't be easy. It's an ongoing journey that means challenging your operations, clarifying your values and prioritizing trust from the top down.

But organizations that get this right will not only avoid mistakes but also emerge as industry leaders. Those investing in a solid foundation are now equipping their teams to unlock AI's full potential in the critical years ahead.

Those who overlook these levers will inevitably be playing catch-up.