Why Your Next CDO Might Be The CFO

I first saw a data team report to the CFO in 2016. It took me a while to warm up to the idea. At the time, I thought it only worked for small teams and early data maturity businesses. I changed my view as I watched finance leadership work with the data team.

Data teams need an executive-level leader, but I’m increasingly sold on EVPs of Data or AI and CDOs/CAIs/CDAOs being successful while reporting to the CFO. The ‘growth at all costs’ strategy movement has taken a back seat to efficient growth. CFOs have the dual responsibility for increasing the business’s growth and maintaining or growing margins.

I’m not the only one seeing a new role for CFOs in directing how technology supports both objectives. Alphabet promoted CFO Ruth Porat to President and Chief Investment Officer. Among her responsibilities is overseeing Alphabet’s Other Bets portfolio. This is where many of the company’s most forward-looking investments are kept. The CFO promotion indicates a new focus on efficient growth and ROI.

Airbnb promoted its CFO to a new role , the Chief Business Officer. His job is to drive growth in Airbnb’s existing business and new ventures. That includes internal expansion and new technology investments. Again, the focus is on finding ways to leverage technology for top and bottom-line impacts.

CFOs As The Data Team’s Link To Strategy And Budget

CFOs are seen as strategic partners in ways that most C-level technology leaders aren’t. Last year, Meta moved its CFO into the Chief Strategy Officer role , which appears to have paid off. The company has found ways to grow more efficiently. Investors are happy with Meta’s strategy for sustaining its growth trajectory with AI and the metaverse. The balanced focus on short-term gains and a long-term growth roadmap has significantly boosted Meta’s stock price.

According to Deloitte’s Q3 2023 CFO Signals , 42% of CFOs are experimenting with Generative AI, but they’re doing it with an eye on controlling costs. Two-thirds of CFOs are keeping the investment levels in Generative AI under 1% of next year’s budget. CFOs are willing to take intelligent risks on new technology but wait until returns materialize before putting significant resources into them.

One of the primary data and technology worries the CFO Signals survey uncovered was execution in the technology business. This reflects the low number of data and AI products that make it into customers’ or internal users’ hands. The high failure rates are one reason businesses hold back on investing in more advanced initiatives.

A direct link between the CFO and the data team means greater visibility into initiative success. Most data teams don’t do a good job evangelizing their success across the business. It’s one reason the function is seen as a cost center. CFOs are willing to advocate for technology if it delivers cost savings and revenue growth. They just need to see it firsthand.

Data And AI’s Direct Impacts On The Top And Bottom Lines

According to Steve Jurvetson, Co-Founder of Future Ventures, Google now spends more money on compute than on people . The implications are that hiring is no longer the most efficient way to grow the business. Technology can improve productivity at a lower cost than increasing headcount for a growing number of use cases.

With data and AI, technology can increase the business’s value and create opportunities for growth. AI-enabled products generate new revenue more efficiently. The unit economics of labor-enabled products don’t always work out, and scaling to meet demand is slow and expensive. Replace some labor aspects with AI, and more products will become feasible.

Data is also a novel asset class . It can be monetized multiple times without being depleted. The same data set can support multiple data products and train multiple models. Each new curated data set increases the business’s value. First-party data creates opportunities. Low-cost access to data-generating processes creates data sets that are competitive advantages.

Access to data-generating processes enables the business to curate data sets with business and customer context . The context is metadata about the system that generated the data, and it increases the signal quality. The result is simpler model architectures can learn that signal. It costs less to extract value from the data, and that’s why it’s a competitive advantage.

CFOs are crucial allies in getting the business to see data as an asset. They support the data monetization catalog’s creation. Even though data cannot be put on the balance sheet (yet), CFOs can connect the dots between data, use cases, and ROI.

Finance Is A Natural Data And AI Advocate

Traditional technology functions wouldn’t achieve the same success under the CFO, but data and AI use cases are different. The technologies can support workflows in two ways:

1. Automating And Augmenting Tasks
2. Supporting And Improving Decisions Associated With Tasks

Decision support systems provide data and inference that improve decision outcomes. One of the most significant barriers to realizing that value is getting people to use the data. Finance is data-driven by the nature of the job function. I was on a panel last week discussing AI use cases in finance . One of the consistent themes was how finance organizations can lead the way in this transformation wave.

Finance teams are data and model-literate out of the gate. Data and evidentiary support for decisions are expected. CFOs don’t have the same tendencies to ignore the data that other business leaders do. Finance is one of the best places for data and models to gain traction as a decision-support tool.

CFOs are also well-positioned to evangelize the benefits to other CxOs. CFOs have a seat at the strategy table. They are integral parts of planning, budgeting, and forecasting. All three functions benefit greatly from data and models. For many C-suites, the CFO will be the one who introduces decision support systems into these processes. That makes CFOs natural allies and advocates for the data team.

Data And AI Supporting Complex Finance Use Cases

Finance has several high-value use cases that can only be served by data and AI. The function has what I call ‘long-chain workflows.’ Those workflows have dozens of steps and/or run on long time spans. Traditional technology can automate some parts of the workflow.

However, only data can provide visibility into long-chain workflows to support improvements or detect issues. The distance between an action or decision and its outcome or business impact is significant in long-chain workflows. People have a hard time seeing these complex relationships. Data and models can support finance teams by tracking these relationships.

For example…

• A new hire must be justified in Q3 2023 during the budget planning cycle
• So the job description can be posted in Q1 2024
• And someone can be hired by Q2 2024
• So they can be onboarded and ready to contribute by Q3 2024
• To a project that will be delivered in Q1 2025
• And generate revenue to meet projections for Q3 2025 and beyond

Data and models can support the budgeting process by revealing the need for a new hire and facilitating the long-chain process. That’s why companies like SAP target finance teams as early adopters. The company makes a suite of planning tools to provide visibility into and automation support for long-chain processes. It’s complex and requires users to work with multiple apps and leverage data from multiple sources.

Finance teams can see the power of AI first-hand in budget optimization by measuring the impacts of decisions made during the planning process on outcomes that could occur a year or more later. Generative AI Assistants like SAP’s Joule orchestrate the apps and data access to streamline long-chain workflows like hiring planning. In the past, these workflows required significant amounts of labor. Generative AI is an excellent orchestration layer and handles the app switching. Users can do the work in a single place with an intelligent assistant coordinating with all the backend supporting apps.

Previous technologies weren’t ‘intelligent’ enough to break complex tasks into steps, assign steps to the appropriate app, and work with the user to progress through each step. Finance teams will be among the first to see how Generative AI creates value.

While the rest of the business follows social media’s lead and experiments with ChatGPT, finance will see how business AI supports them in new ways. They will better understand the new paradigm and be well-positioned to lead this transformation phase.

This post is free for everyone as part of my partnership with SAP . I appreciate their support for business-centric AI content that’s usually hidden behind paywalls.