AI and the CFO lot’s upheaval
Markus Mäkelä
Artificial intelligence will thoroughly change the work of the corporate financial management function, as for other functions. CFOs are having a lot to unpack here. One valuable way to divvy these changes up to their component parts is to look at the various opportunities for operational improvement and for strategic improvement separately.
As for operational effectiveness, that is simply table stakes: companies will simply have to roll out AI-powered changes fast enough to match the competition. Start a comprehensive program early. A possible lead is usually short-lived, but progress there must be.
Prioritization of projects is key, though, since the necessary changes are so broad in a large corporation with all specialized subfunctions that they are too many to try at once. Many of them will also require work process changes, including under the CFO, since new IT should not simply be “dropped” unthinkingly onto an unaligned old process. A second key is adequate management of digital transformation, itself a complex process.
But just how to prioritize the AI projects, then? Simply by their projected standalone value?
Strategic improvement – the key to industry-abnormal profit
Many answers are around. The correct one is “as the strategy prescribes” – at least for any firm whose leadership is after above-industry-par value generation, since trying to outperform competitors financially on the longer term is really the only way to go. – Strategy is, in other words. Which is given that strategy is the language of precisely how that is able to happen, business-wise.
Strategy is how you differentiate your value proposition from those competitors, or how you garner a cost advantage. That top-level competitive advantage also needs a detailed and comprehensive strategic plan for selected market segments – unless going for untargeted “mass coverage” – and for how to position the offering within those segments.
Modern digital strategy should sometimes be brought into play within such specific strategy, such as by making use of digital revenue models, AI-driven enterprise agility, or a platform strategy with accompanying network effects. Backing all up, you’ll anyway need foundational capabilities – say, deep customer understanding with digital aids, platform ecosystem orchestration, or customer needs-attuned product development. Finally, you may need strategic risk management via business development (real) options, agile business process innovation, or a hedged, purposely suboptimal strategy.
Strategy is the deeply market-cognizant laying-out of how you’ll get to do business in an imperfectly competitive market – in a “bluer ocean” of profit-growing opportunities, risk accounted for.
To conclude, those AI projects that actively support your resulting overall strategy most strongly and complementarily are what to prioritize and are the strategic improvement you need, maximizing overall value.
M&A appraisal, for example, could be improved by your AI weighing in on these projects’ strategy-appropriateness itself, be it for business-unit strategy (my focus topic above) or for corporate strategy, another topic entirely. Are there synergies lurking, as in embedded into R&D IP? How realistic are proposed core competencies or other synergies?
Or, AI can be needed as a strong background support for your service features, say, data harnessing, valuation reporting, or securities research write-ups, improving differentiated value and also how you signal clients that you deliver it (signaling helps differentiate even commoditized services, an observation not irrelevant for e.g. many financial services today). I will later write more of supporting a strategy via AI.
Operational improvement
Here, options truly abound. Some are what I gave in a recent magazine interview (some available only after years):
In budgeting, revenue forecasting is much more accurate, as is consequently production volume planning, and appraising the need for major operating expense line proposals
Aspects of ongoing controlling will be automated, including AI-given red flags directly to P&L-responsible leaders
Investment appraisal will be clearly better as some input estimates of valuations improve, be it about M&A, greenfield factory development, or whatever project.
Risk evaluation – periodic or with major decisions – including alerts for black-swan events and compliance risks, ESG-plan misalignment, et cetera
AI might become able to tell users about the Holy Grail of stock market timing and of macroeconomic developments. But any advantage would, of course, quickly be negated via public estimates, to corporations as to others.
Saving time from more mundane work, like via distilling the essence from an M&A target’s data room documents for a first view, or saving 75% of the time of investor or internal report development, or by fully automating many customer-financing decisions
Finally, some non-financial functions often report via the CFO. AI has vast influences that manifest via operational process development and obviously IT. It also impacts the strategy function, such as enabling fast sourcing and synthesis of both external and internal business intelligence information.
Markus Mäkelä, M.Sc. (Econ.) 2001, is the President of AFA. Markus is an AI and strategy expert and AFA Quarterly’s FTS columnist.