What happens when AI allows finance teams to work at unprecedented speed, but enables mistakes to travel just as quickly?

In this episode of Consulting the Future, I speak with Graeme Fleming, Industry Principal for Governance, Risk and Compliance at Workiva, about how CFOs, CIOs, auditors, and risk leaders can capture the productivity benefits of AI without sacrificing governance, data integrity, security, or human judgment.

Graeme brings more than 25 years of experience spanning Big Four consulting, internal audit, risk management, and GRC. He explains why the current AI wave presents a different challenge from previous technology changes. Adoption is moving quickly, employees already have access to powerful AI tools, and companies that fail to provide approved options risk creating a growing problem with shadow AI and sensitive corporate data being entered into systems they do not control.

The conversation examines how AI is changing the role of the CFO. By automating data gathering, reconciliation, analysis, and reporting tasks, finance teams can spend less time managing spreadsheets and more time interpreting information and supporting business decisions. But faster analysis only creates value when leaders can trust the data going in, understand what AI systems are doing, and critically review the results coming out.

As Graeme puts it, companies also risk "making mistakes at the speed of AI."

We discuss why governance and controls should not be viewed as barriers to AI adoption. Graeme compares controls to the brakes on a bicycle: they do not exist simply to make you stop, but to give you the confidence to move faster because you know you can remain in control.

That becomes increasingly important as businesses introduce AI agents and give automated systems greater autonomy. Graeme explains why companies need clarity around what an AI system is designed to achieve, where its data comes from, what happens to that information, how the model operates, and who remains accountable for reviewing its output.

We also examine automation bias and the danger of trusting authoritative-looking AI responses when employees are already under pressure. Human review cannot become a box-ticking exercise. Finance professionals, auditors, and risk teams need to retain the ability to question outputs, identify anomalies, understand context, and take ownership of decisions.

Graeme shares practical examples of where AI is already delivering value in financial reporting and audit, including reviewing lengthy annual reports against disclosure requirements, checking consistency across documents, benchmarking reporting, automating reconciliations, and conducting compliance testing at scale. These applications can remove hours of repetitive work while allowing experienced professionals to concentrate on exceptions, insight, and judgment.

The conversation also looks at the emerging role of the AI auditor, both using AI to support audit work and auditing the AI systems businesses increasingly depend upon. Model governance, security, data provenance, controls, and accountability are becoming business concerns that CFOs and risk leaders can no longer leave solely to technology teams.

For CFOs, CIOs, auditors, consultants, and risk leaders, this episode provides a practical perspective on AI governance, financial reporting automation, shadow AI, automation bias, data integrity, and maintaining meaningful human oversight.

The opportunity is not to choose between AI-driven productivity and strong governance. It is to design controls that give companies the confidence to use AI at greater scale while remaining accountable for the decisions and information their systems produce.

What happens when AI stops being a tool that sits alongside a business and starts becoming part of the delivery model itself?

In this episode of Consulting the Future, I sit down with Esteban Sancho, CTO for North America at Globant, to explore how AI is changing the economics, structure, and expectations of modern consulting. Esteban shares how Globant's AI Pods combine agentic AI, automation, and experienced engineering talent into what he describes as a delivery system rather than a traditional project team.

We discuss why so many organizations remain stuck in AI pilot mode despite enormous investment and executive pressure to demonstrate results. Esteban explains the gap between what AI is capable of and what most businesses are operationally prepared to support. From rethinking workflows and team structures to creating governance models that build trust, he offers a practical view of what it takes to move from experimentation to production.

The conversation also examines the future role of consultants in an AI-powered world. Will AI replace consulting teams, or simply change what expertise looks like? Esteban argues that human judgment, systems thinking, and the ability to design reliable AI delivery models are becoming more valuable, not less.

We also explore why outcome-based delivery is gaining momentum, how financial institutions are adopting AI faster than many expected, and what business leaders should consider when balancing innovation with oversight and accountability.

If you're trying to understand where consulting, software development, and enterprise AI are heading next, this episode offers a thoughtful look at how some organizations are already adapting to a very different future.

What role do you think human expertise will play as AI becomes embedded in every business process? I'd love to hear your thoughts.

What does it really take to move enterprise AI from impressive demos to decisions that show up in quarterly results?

One year into his role as Senior Vice President, Americas Consulting, Neil Dhar sits at the intersection of strategy, capital allocation, and technology execution. Leading the firm’s Americas business and a team of close to 100,000 consultants, he has a front-row view into how large organizations are reassessing their AI investments.

From global healthcare leaders like Medtronic to luxury retail brands such as Neiman Marcus, the conversation has shifted. Early proofs of concept helped executives understand what was possible. Now the focus is firmly on proof of value and on whether AI can drive growth, competitiveness, and measurable return.

In this episode, I speak with Neil Dhar about what has changed in the boardroom over the past year and why ROI has become the central question.

Drawing on more than three decades in finance and private equity, including senior leadership roles at PwC, Neil explains why AI is increasingly being treated as a capital allocation decision rather than a technology experiment.

Every dollar invested has to earn its place, whether through productivity gains, operational improvement, or new revenue opportunities. Vanity projects no longer survive scrutiny, especially when boards and investors expect results on a much shorter timeline.

We also explore how IBM is applying these same principles internally. Neil shares how the company has identified hundreds of workflows across the business, prioritized those with the strongest economic impact, and used AI and automation to drive large-scale productivity gains. The result is a potential $4.5 billion in annual run rate savings by 2025, with those gains being reinvested into innovation, people, and future growth.

It is a candid look at what happens when AI strategy, leadership accountability, and disciplined execution come together inside a global organization.

If you are a business leader trying to separate real value from hype, or someone wrestling with how to justify AI spend beyond experimentation, this conversation offers a grounded perspective on what enterprise AI looks like when it is treated as a business decision rather than a technology trend.

Are you ready to rethink how AI earns its place inside your organization, and what proof of value really means in 2026?

Useful Links

• Connect With Neil Dhar
• IBM Institute for Business Value, “The Enterprise in 2030” study
• Learn More About IBM Consulting