Why companies should use AI to influence entire workflows, not just complete simple tasks

Ever since artificial intelligence (AI) has gone mainstream, debate has centred on what large language models (LLMs) can or cannot do – and what they should or should not do. And as organizations have moved beyond underrealized pilot programmes, the focus has shifted to AI-powered corporate strategy designed to improve productivity, collaboration and insights across every organization’s business.

But with global uncertainty as the defining theme of the World Economic Forum’s 2026 Global Risk Report, the conversation must now turn decisively toward what enterprises require to use AI with confidence at scale: trust.

LLMs have become the new productivity layer for knowledge work. They help teams draft, analyze and synthesize data faster than ever. But too often, teams must pull work out of AI environments and onto desktop applications and servers to make it usable and prevent the sharing of sensitive information. This behaviour may feel safer in the moment, but it also strips the work of context, makes it obsolete faster and adds manual steps and an increased risk of human error back into the process.

AI can generate useful output, but can that output stay connected to the trusted environment that makes it usable?

AI creates the most real business impact when it influences entire workflows – not just single tasks – and when AI-ready data and responsible governance are in place. Findings from a recent McKinsey survey on the state of global AI underline this. Organizations that capture more value from AI are more likely to redesign workflows as they deploy it and put governance in place.

For AI to add value, it must be grounded in trusted data and shaped by the standards and context specific to the organization using it. This trusted data trains AI models, which reveal insights and drive automation. More importantly, trusted data creates integrity between what an organization claims, reports and does.

But too much critical business information still sits in siloed systems and disconnected tools. Until that changes, AI's usefulness will remain limited.

This is where an old problem returns in a new form: garbage in, garbage out – only faster. If AI runs on fragmented, unreliable or decontextualized data, it does not accelerate insight, it accelerates the wrong answer. In an enterprise setting, that's not just inefficient, it's risky.

So, after building a trusted data foundation, how can organizations transmit that trust into their work? AI-powered platforms are integrated, cloud-based workspaces that can automate and secure complex processes. These platforms can support corporate trust in four ways:

Instead of working from generic inputs, AI uses the business's own data and operating standards. That makes outputs more relevant, more reliable and more actionable.

Fewer than one in five organizations consider themselves data-ready for AI, underscoring how hard it is to generate enterprise value without a trusted foundation. Workiva's 2026 Executive Benchmark Survey also found that 79% of business leaders are prioritizing data automation and governance to close enterprise-wide data gaps – a sign that more organizations recognize the need to give AI a stronger, more trusted foundation.

While not necessarily removing people from a work process, AI-powered platforms remove manual work that slows them down.

To fully capture the value of AI, organizations must redesign operating models and governance structures so teams and technology can work together in a connected ecosystem. Results from McKinsey's 2025 global survey on the state of AI support this, revealing that companies generating more bottom-line impact from AI are more likely to redesign workflows as they deploy it.

Reskilling teams and reimagining work are not new concepts. From the calculator to the internet, to email, to the cloud, as technology has evolved, the enterprises that last have led the charge and evolved with it.

Enterprise AI must be transparent, auditable and secure. A platform makes those controls native to the environment, so governance travels with the work instead of being applied after the fact.

As the World Economic Forum recently reported, as AI systems move from answering questions to taking action, governance becomes the key to using them responsibly, with autonomy calibrated to context, risk and organizational readiness.

Hardworking teams often turn to unapproved AI tools because they are quick and easy to use, helping them get more done. While these tools can create efficiencies, this use of "shadow AI" also creates a much larger governance problem by moving sensitive work and customer data outside trusted systems.

The best response to shadow AI is not to slow people down, but to give them AI inside an environment built for trust, where speed, security and accountability can work together. While off-the-shelf AI gives people agency to generate output, creating an AI-powered platform gives enterprises agency over the data, standards, governance and auditability that make that output usable.

For governance, risk and compliance (GRC) teams, governance is not adjacent to the work, it is the work. That’s what makes GRC one of the clearest proving grounds for AI-powered platforms. Teams already understand risk, while the cost of unmanaged AI is easy to see and the value of traceability, controlled sharing and defensibility is immediate.

GRC teams are not looking for AI outputs that are fast or well-written. They need trustworthy AI that delivers outputs they can trace to source data, review in context, share with the right stakeholders and defend under scrutiny.

While governed AI is already gaining traction in finance, some of the clearest platform AI use cases are emerging in GRC – from identifying duplicative controls across a risk and control matrix to generating flow charts from process narratives. According to PwC and the Institute of Internal Auditors, AI’s value in internal audit and risk work lies in process understanding, governance oversight and evidence review.

If AI can prove itself in an environment where evidence, controls, standards and oversight shape every decision, it can prove its value anywhere. And as AI moves deeper into decision-making, more functions will need the same things: trusted inputs, usable outputs and a clear line from one to the other.

In today’s global markets, organizations face greater scrutiny, volatility and pressure to move quickly.

AI will continue to get faster, more capable and more ubiquitous. But speed alone will not create enterprise value. The differentiator will be whether organizations can apply AI in ways that keep outputs connected to trusted data, organizational standards and governed environments.

AI-powered platforms go beyond delivering auditable, defensible and usable outputs by building the trust that gives enterprises the confidence to act.