Irena Teneva, an associate technical director at the Association of International Certified Professional Accountants, joins the FM podcast to share insights gathered from a new AICPA and CIMA report, Future Ready Finance: Productivity at the Human-Technology Crossroads.
Teneva illustrates the types of tasks experiencing clear efficiency gains from artificial intelligence (AI) adoption, where finance leaders are more cautious about implementation, and steps those leaders are taking to expand how tools can be used.
Teneva also highlights why productivity still hinges on finance talent, priority areas for skills development, and how finance leaders are collaborating across business functions.
What you’ll learn from this episode:
- Process and people priorities for finance leaders in 2026.
- Where AI implementation is leading to time savings for companies.
- Some steps leaders are taking to optimise and expand AI use.
- Why productivity is still a “people game”.
- How finance leaders are approaching cross-business collaboration.
- Three key steps to improving efficiency, according to leaders.
Play the episode below or read the edited transcript:
— To comment on this episode or to suggest an idea for another episode, contact Steph Brown at Stephanie.Brown@aicpa-cima.com.
Transcript
Steph Brown: Hi, listeners. Welcome back to the FM podcast. I’m Steph Brown. On this episode, we’ll be diving into a new AICPA and CIMA report based on in-depth conversations with a collection of senior finance leaders globally. Irena Teneva, associate technical director at the Association of International Certified Professional Accountants, will be joining me to analyse the report’s findings.
We’ll be looking at how finance leaders are harnessing AI, priorities for upskilling finance teams, and plans for improving collaboration and cost efficiency.
Welcome to the podcast, Irena.
Irena Teneva: Thanks, Steph. Pleasure to be here.
Brown: It’s great to have you on.
The report’s title is Future Ready Finance: Productivity at the Human-Technology Crossroads, and it relied on a wide survey of global finance leaders and in-depth interviews with a smaller group.
To start, what are two to three key takeaways from recent roundtable discussions with CIMA members?
Teneva: I think takeaway one would be before you implement AI, nail the basics. And by that, I mean clean data and smart processes. So, our members stress that getting the foundations right is priority number one, because without reliable data and streamlined processes, even the best new tech falls flat. So, AI will not magically fix poor data or clunky processes.
When it comes to the focus for the next 12 months, finance leaders, I believe, will double down on data quality and process standardisation. That means cleaning up messy data, upgrading clumsy legacy systems, and automating only well-defined repetitive tasks that are ready for it. So, this groundwork will ensure that any AI or advanced tool will actually deliver value rather than producing what we call garbage outputs.
I think takeaway two would be embrace AI but with pragmatic optimism. By that I mean that finance leaders will probably be piloting, refining, but also keeping humans in the loop. CIMA members are really excited about AI’s potential, but at the same time, I found them realistic about the current maturity of the technology. So, AI can be the game changer in the profession because it can deliver such speed and insight, but only if adopted carefully.
Roundtable stories highlighted small-scale pilots that delivered clear efficiency wins — and all this with human judgement at the centre. Because as we know, at the moment, no one is letting an algorithm run the month-end close or make a big decision unsupervised. In fact, participants shared cautionary tales of AI tools that are producing plausible-sounding but incorrect numbers. From that perspective, everyone talked about the need for human review and professional scepticism.
Maybe finance leaders will use 2026 as a period for controlled experimentation. They will identify specific use cases with clear return on investment and pilot AI tools, but under tight guardrails. They will make sure to validate AI outputs but also invest time in separating hype from reality because we do need to know what AI can deliver today versus what it can deliver tomorrow.
Last but not least, I will say as takeaway number three: Productivity is actually a people game. So, we need to invest in team skills and a culture of trust and agility. A lesson from the member conversation is that technology alone won’t drive productivity — people will do that.
Many finance professionals have realised already that adoption is often the hardest part of digital transformation. One participant noted how colleagues, for example, still defaulted to manual Excel spreadsheets out of habit, even though new dashboards have been rolled out. Other colleagues mentioned finance professionals being worried that automation and AI threaten their jobs, and, of course, that slows down uptake.
At the same time, success stories come when leadership actively manages the human side, providing the right environment to experiment, building trust and transparency. So, I will say that culture and skills proved equally important as the tech itself. From that perspective, maybe the focus in the next 12 months will be upskilling and engagement.
Brown: Irena, I guess predominantly leaders are embracing AI, albeit cautiously. Can you tell listeners a bit about how finance leaders are using digital tools, specifically AI, and their impact on productivity?
Teneva: That is a very interesting question. What we discovered is that the picture is perhaps less dramatic than headlines suggest. So, what we are seeing from CIMA member discussions is that digital tools, including AI, are already improving productivity, but mostly in very practical, targeted ways, almost at the level of individual tasks rather than big transformations.
A lot of that impact comes from automating routine, well-defined tasks, and that includes activities like invoice processing, expense approvals, and producing some reports. In some cases, finance teams have been able to reduce time and effort quite significantly. Some of them mentioned double-digit percentages, and that means time freed up for analysis and decision support. At the same time, tools like BI dashboards and ERP-integrated systems are shortening reporting cycles and giving teams faster access to data, and that helps improve responsiveness and decision-making.
But what has become equally clear from the discussions is that AI’s role is still largely supportive rather than transformational. Most teams are using AI for assistance and to accelerate some routine processes: helping with documentation, cleaning data, coding transactions — rather than replacing core finance processes. I would say there’s also a strong emphasis on human oversight: Finance leaders were very clear that AI outputs still need to be reviewed and, more importantly, challenged. In fact, examples were shared where tools produced plausible but incorrect results. So again, professional judgement remains essential.
Perhaps the most important insight is that the real driver of productivity isn’t the technology itself; it’s how well it’s embedded into the organisation. Where companies had clean data, streamlined processes, and teams were trained to engage, the tools delivered clear benefits. But of course, where those foundations weren’t in place, the impact was much more limited.
While the more transformative potential of AI is still emerging, it depends heavily on people and processes.
Brown: As you mentioned, the data highlights that data quality is quite significant in how optimised AI use can become. Currently, are there any other limitations or obstacles when it comes to AI integration? And what steps are leaders taking to leverage AI across more-complex workflows?
Teneva: Yes, when we talk about limitations and obstacles, our conversations with CIMA members are always candid, and that’s why I believe they’re grounded in reality. One of the messages is that the main barriers are not the tools themselves, but the context in which they’re being introduced.
A recurring issue is data quality and process readiness, as you very well said, Steph. And finance leaders pointed out that if underlying data is fragmented or unreliable, AI simply scales the problem. It can produce faster outputs, but not necessarily better ones.
Another constraint is trust and also risks, particularly around accuracy, security, and governance. Finance teams, as we know, are dealing with sensitive data and high-stakes decisions. So, there’s a natural reluctance to rely on tools that can produce incorrect or opaque results. Some members shared examples of AI generating clearly wrong outputs. And once again, I will here mention professional scepticism because it is so important.
Of course, as always, there’s also human and organisational barriers. We’ve discovered that adoption can be uneven. Some teams are early adopters. They embrace new tools quickly, but others revert to familiar ways of working. In some cases, concerns about job security or even lack of clarity on the benefits can slow down implementation.
Because of all these factors, AI integration into more-complex processes and workflows like month-end close, forecasting, strategic decision-making is still relatively limited at this stage. In terms of what leaders are doing about it, there’s a strong focus on building the foundations. Many are investing in ERP upgrades, improving data quality — sometimes AI can help with that as well — and standardising processes so that when advanced tools are implemented, they have reliable data to work with.
Second, rather than trying to automate end-to-end processes straight away, what leaders are running, organising at the moment are lower-risk and contained applications, and expanding from there only when the benefits are clear. And then, as already mentioned, the human side of transformation also requires investment. That includes upskilling finance teams, sometimes embedding tech expertise like hiring data scientists and embedding them within the finance team, creating space for experimentation so people build confidence in these tools.
Again, I believe the strongest point here is the sense of pragmatism, because some leaders directly mentioned that they are deliberately not rushing it off. They would rather wait and see, watching how the technology evolves, learning from others, and scaling adoption when the tools and when the organisation is ready for it.
Brown: One theme across the report that you have highlighted is that technology’s success in finance really does still depend on people.
We’ve talked about pathways to optimising AI use that leaders are pursuing. But what are the priority areas when it comes to upskilling finance talent, based on those conversations?
Teneva: Yes, upskilling is really a top priority, and I guess it has got several aspects.
The first one, I would say, [is] AI fluency. CFOs want their teams comfortable with advanced analytics and with AI tools; not just using them, but understanding how to supervise and how to question their outputs and to ensure that these technologies are applied responsibly.
Related to that, but slightly different, [is] data integrity and governance. Leaders are really ramping up on training so that finance teams can manage data quality and controls, because AI is only as good as the data and controls behind it. So, strengthening data governance and process know-how is really foundational for technological success.
And, as always, there’s deeply human skills, things that technology cannot replace like curiosity, professional judgement, communication, business partnering, [adaptability], which help finance professionals interpret insights, collaborate effectively, and drive change with confidence.
Brown: Like you just said, the report also emphasises the importance of C-suite collaboration to transform the finance function. But what does that collaboration look like in practice?
Teneva: I think these days, C-suite collaboration comes through as much more practical and embedded in day-to-day work. In practice, this means finance working alongside other functions, not operating separately. Finance teams collaborate with operations, IT, HR — with all business segments — to redesign processes and implement AI, so that those productivity gains are realised end to end, not just within finance.
That also involves co-owning data, decisions, and outcomes. Finance helps to translate insights: They challenge assumptions [and] link decisions to business performance rather than just reporting numbers. Importantly, that collaboration also links in with continuous and ongoing business partnering. So, regular interactions with other teams to align priorities, reshape workflows, and redirect efforts towards higher-value activities.
I would say, overall, collaboration is less about formal co-ordination and formal roles and more about finance being integrated into how the business operates and makes decisions.
Brown: From those conversations, reducing headcount to cut costs did not stand out as a key strategy for leaders right now. So, what are their top plans for saving money and improving operational efficiency?
Teneva: Cutting headcount didn’t emerge as a priority. Instead, finance leaders are focusing on different levers for efficiency.
First, as already mentioned, automation and process redesign. Teams are targeting high-volume routine activities and integrating them into end-to-end workflows, so gains go beyond isolated time savings and come from real performance improvements.
Second, I think investing in better systems and data. Investing in new ERPs or upgrading existing ERPs, improving data quality, streamlining processes, they were seen as essential when it comes to unlocking efficiency. Because these are the foundations, technology simply does not deliver without them.
And then improving how teams work. CFOs are reshaping roles now that human beings work, co-create with machines. Sometimes they are centralising expertise; sometimes they are thinking of new ways of improving collaboration across functions. Because one of the benefits of AI is that it gives you the chance to reduce duplication and make better use of capacity.
So, yes, the focus is not on just doing [more] with fewer people, it’s much more on working smarter. That often comes through streamlined processes, better systems, better co-ordination, motivation, and better skills.
Brown: Thank you so much, Irena.
We’ve discussed at length quite a lot of the different elements within the report, but is there anything that we’ve not touched on in this conversation that you think is important to mention?
Teneva: As always, I’m impressed by the work ethic and motivation of finance leaders. I’m always impressed how people outside the profession think that our job is very boring, while in reality it’s much more dynamic than people think.
Our members have a real sense of purpose, and they deliver all those important transformations with a lot of agility and perseverance.
Brown: Thank you so much, Irena. Thank you for coming on the podcast today.
Teneva: Thanks for having me.
