An interview with Gary Bischoping, Chief Financial Officer, Thomson Reuters.
Gary Bischoping joined Thomson Reuters as Chief Financial Officer (CFO) with a mandate to drive financial velocity – the speed and precision with which Finance turns data into decisions in an era of accelerating change. Having recently assumed the CFO role, Gary is focused on accelerating the next stage of Thomson Reuters AI-enabled transformation. He joins an organisation that has played a key role in the company’s AI transformation program; a structured AI deployment effort spanning 231 projects, 118 deployed initiatives, and more than 65,000 hours saved across the organisation.
As a hands-on user of the very tools Thomson Reuters brings to market, Gary’s team uses CoCounsel Tax for research and drafting and OneSource for tax provision and compliance integration. In this conversation, Gary shares what it really takes to drive confidence at scale, why Fiduciary-Grade AI ™ is non-negotiable in finance and tax, and what will separate the most effective CFOs of the next decade.
Q: How do you see the CFO role evolving in an AI-driven enterprise, and how is that shaping your priorities at Thomson Reuters?
The one thing that has been consistently true for the last five to 10 years is that the rate of change keeps accelerating but what’s also changed is that the fan of possible outcomes has widened, both to the upside and the downside.
Finance sits at the heartbeat of navigating that. Our job is to remove downside risk and to make bold decisions that capture the upside, and to do both of those things faster. That’s the velocity I mentioned; not just moving fast, but moving fast with direction.
That means shifting the organisation from simply explaining what happened to generating focused, actionable insights that drive outcomes. And AI is central to that. What makes this technology different from previous revolutions is its ability to dramatically accelerate time to value and surface the next best action.
The other major shift is that the remit has expanded significantly, and that’s intentional. Today, it spans enterprise risk governance, regulatory compliance, third-party risk, privacy, and organisational resilience. That includes everything from regulatory intelligence and data protection to crisis management and business continuity. That breadth reflects reality: risk cuts across products, people, customers, markets, and partners
“Velocity is the combination of moving fast and moving with direction. AI is what makes that possible at scale.”
Q: How is Thomson Reuters moving finance and tax beyond compliance to a more strategic, enterprise-wide role?
The compliance and tax customers we serve need trusted tools and technology, because the decisions they make are fiduciary in nature. That means the AI they rely on has to be domain-specific, auditable, and explainable. It is not sufficient to simply stare at an output and trust it. If I can’t understand it, I can’t explain it, and I won’t use it. That shift from general AI tools to what we call Fiduciary-Grade AI™, and it is where we’re seeing the biggest change.
More broadly across finance, we’re using conversational FP&A tools, AI-driven sales and revenue variance analysis, and AI assistance on accounting close. The common thread is that each of these tools is grounded, explainable, and operates with the human in the loop which frees the team to spend less time validating outputs, gathering data and producing reports, and more time understanding business implications, identifying risks and opportunities, and guiding strategic decisions. That shift from processing information to shaping outcomes is how you move from compliance to genuine strategic value.
Q: What does ‘confidence at scale’ look like in financial decision-making, and how are you building it?
There are four things that have made the difference for us here at Thomson Reuters:
- The first is governance built from the start. Guardrails cannot be an afterthought. You have to know that what you get back from your AI work is something you can trust, and that requires the governance model to be in place before you scale.
- The second is human capability built alongside technology. That means real investment: training, building different levels of technical capability across the team, and making sure people have the qualifications and conviction to actually benefit from the investment. This also requires leadership willing to make the tough calls about where and how to invest.
- The third is getting the organisation to think about the art of the possible, to operate in an unconstrained mindset and then work back to how AI helps get there. When you combine explainability, auditability, and a human alongside the tool, you can move quickly.
- The fourth is accountability. Like the customers we serve, we must understand the outcomes we generate; not just the successes. That requires conviction: staying the course, avoiding constant shifts in direction, and giving both our people and our technology the time they need to deliver meaningful results.
Q: Where have you seen AI most meaningfully improve the quality and speed of decision-making?
Gary: The most visible impact has been in financial planning and analysis. We’re now using plain language conversations within Microsoft Teams to get variance analysis on demand, decomposing the drivers of growth, cross-sell, upsell, new logos, pricing, at a very granular level. That analysis used to take days and required curated presentations to communicate. Now it happens close to instantly, and the team spends its time on insights, actions, and outcomes rather than the work of getting to them.
The second major area is complex accounting. We can now ingest transactional documents, apply accounting rules, and generate a suggested solution that a human then reviews. Purchase accounting, for example, is a technically demanding area, and AI has enabled a much faster path to a well-reasoned memo, with the human reviewing and owning the final output.
On the tax side, the scale story is striking. We have maintained broadly the same size tax organisation over the past five years, but the volume and complexity of what they’re working through – more legal entities, more complex transactions – has grown significantly. CoCounsel Tax has made that possible. And ONESOURCE has been fundamental to integrating tax provision and compliance processes across the enterprise, allowing the team to absorb significantly more volume and complexity while increasing accuracy and real-time determination capability.
Q: Where have you seen AI most meaningfully improve the quality and speed of decision-making?
The balance is built into how we approach adoption. The experimentation and learning-fast culture we’ve established through the work we’ve done as a team means that people are trying things, sharing what works, and building trust in the tools over time, not just being handed technology and told to use it.
The expert layer is never removed. In every deployment, the human reviews the output and owns the outcome. What AI does is get us to a better-prepared, better-informed starting point, so that the expert can focus on the genuinely high-value decisions, the ones they went into their profession to make
Q: Looking ahead, what will differentiate the most effective CFOs over the next three to five years?
The rate of change and the volatility around it will continue to increase. The finance organisations that win will be the ones that can see around corners faster than their competitors, and then lead and guide their business along the way. Listening to customers and reacting to competitors will not be enough. You will need conviction, faster time to value, and the boldness to act.
Three things will define the organisations that do this well: a genuine, reliable, governed data foundation; human capability that is truly trained to take advantage of the technology; and the focus and conviction to execute rather than to revisit.
Practically, that also means narrowing your competitive advantage windows. Access to data is becoming table stakes. What differentiates is the insight you derive from it and the speed at which you act on that insight.
“The CFOs who win will see around corners faster than their competitors – and have the conviction to act on what they see.”
Q: What three pieces of practical advice would you give a CFO navigating AI and tax technology right now?
First, ask whether you can trust it. Is it auditable? Is it explainable? If the answer to either of those is no, it is not ready for Finance and Tax. Second, invest in adoption, not just access. Get the tools into people’s hands. Train them. Some leaders are cautious about the cost, but this is an investment in your organisation’s future capability, and the return on that investment compounds over time. Third, build capability up and down the organisation and across functions not in silos. The CFO of the future will need breadth as much as depth, and the organisations that create cross-functional capability today will have a significant advantage.
