The finance function has been notably absent from Generative AI market maps and hot AI start-up lists. Yet finance is a key proxy for enterprise IT spending and, accordingly, is worth exploring as one indicator of AI’s long-term adoption curve.
I strongly believe that any forecasts of AI should be developed from a bottom-ups perspective - function by function and industry by industry.
In this note, I lay out my framework for AI generally:
Quality Volatility - Magical Or Mediocre
Present As Predictor
AI ROI = (Business Process Value x Volume) / AI Costs
Winning Buyers: Larger Orgs
Winning Users: Many Roles, Daily Gains = Higher Leverage
Winning Vendors: Specialists and Incumbents
AI Quality Volatility - Magical Or Mediocre
My experiments with a range of AI applications have ranged from magical to mediocre.
Even within the same app a few moments apart — pure excitement at the potential, followed by dismissing the tool as useless when garbled, hallucinated results are returned.
AI’s quality volatility will be especially important in enterprise use cases — enough poor experiences can lead to a “not enterprise ready” judgment that could take years to recover from.
Krishna Nandakumar, the founder of a customer-service AI app named Kili, captures this well:
“I think a lot, maybe a little too much, about how users build confidence with AI software products. If users lose confidence in software, it's brutal. Intuitively, this feels *more* important when you leverage AI. Users quickly realize that LLM output is not deterministic. And if this is the case, they are going to be even less forgiving when trust starts to go down.”
Present As A Predictor
While Generative AI represents a breakthrough, specific requirements of enterprise IT adoption remain unchanged:
A business case that justifies allocating budget to the problem
Robust security
Specific to AI, sufficient and quality data
Accordingly, enterprise adoption of AI prior to the late November 2022 launch of ChatGPT should inform any predictions of the future.
Deloitte’s “State of AI in the Enterprise” report from October 2022 is particularly useful for assessing AI adoption in the finance function. An analysis of Deloitte’s data for the broader “Finance + Operations” grouping shows the finance function lagging operations in terms of AI adoption. McKinsey’s “State of AI In 2022” from December 2022 also shows lower AI adoption in the finance function.
The Deloitte report shows the top finance AI use cases as:
Cloud pricing optimization
Accounts receivable management
Predictive risk and compliance management
Procurement
Financial reporting and accounting
Algorithmic supply chain planning
Churn/lifetime value prediction and optimization
Internal audit
With this pre-ChatGPT adoption outlined, I will get to my predictions later after laying out a few more frameworks.
AI ROI = (Business Process Value x Volume) / AI Costs
Robotic process automation (RPA) is another existing enterprise software category that can help predict AI’s enterprise future.
This slide from UiPath’s September 2022 Analyst Day illustrates a core driver of RPA adoption: the need for sufficient volume and task value to drive large enough returns:
This xkcd comic humorously reinforces the need for volumes to justify the time investment of automation:
In my view, the equivalent formula for AI will look like:
AI ROI = (Business Process Value x Volume) / AI Costs
While the numerator is straightforward, a few notes on the AI costs denominator:
Data: Cleaning and labeling data are real costs.
Quality assurance: Checking outputs for accuracy requires real resources. This is especially important in enterprise and finance use cases.
Setup: These include process mapping, prompt engineering, and security reviews.
AI vendor costs: Given compute costs, I expect usage-based pricing to be the default for AI.
Winning Buyers: Larger Orgs
Importantly, those “AI costs” are relatively fixed.
Using an existing AI use case of accounts receivable management as an example, the all-in costs for a $400 million revenue and $100 million revenue would be relatively similar, at least relative to that 4x revenue disparity.
For that relatively similar AI cost, the $400 million org is able to automate 4x the amount A/R processes.
Moreover, that $400 million org has more absolute dollars to invest in getting their AI strategy right. Let’s assume each firm allocates 0.5% of revenue to AI (a meaningful portion of their IT budget) — $2 million dollars of spend should win out versus $0.5 million.
Lastly, that $400 million org should have a larger pool of data to build models from.
These factors imply that larger-scale AI adopters should be the most advantaged.
However, quality data and (modern) interoperable tech stacks are also critical. Abraham Thomas captures this well:
“Quantity has a quality that’s all its own, but when it comes to training data, the converse is also true. ‘Data quality scales better than data size’: above a certain corpus size, the ROI from improving quality almost always outweighs that from increasing coverage. This suggests that golden data — data of exceptional quality for a given use case — is, well, golden.”
Winning Users: Many Roles, Daily Gains = Higher Leverage
Generative AI will drive further adoption and improvements upon existing AI use cases — like matching receipts to transactions in Brex today — reflected in the Deloitte AI report above.
Despite any clear net new killer apps at this moment, I think the finance function captures how AI will diffuse broadly into personal workflows with “human in the loop” as the default.
Like the subtle gains created by Calendly lowering scheduling friction, some examples:
Broaden surface area of data analysis — “interesting to look at” sensitivity (recent example here) and correlation work that would be impractical for a human analyst could be first attempted by an AI bot. I predict the first wave here will focus on “chat with your data” use cases (see Brex’s “OpenAI For Finance Teams” product announcement).
Automate more forms of internal reporting like weekly slide decks
Small gains from “bots in the background” will compound.
And those time savings will be redeployed into more strategic work instead of layoffs. Said differently, each worker will become higher leverage.
These AI-driven productivity gains will be especially important in teams that scale in size with transaction or customer volume, like:
Customer service
SDRs
And many other volume-linked operations functions, including many finance “back office” processes
Winning Users: Examples of AI Benefits
To frame the gains AI automation can produce, Blackline (a publicly traded financial close software company) estimates their automated transaction matching reduces manual efforts by 70%.
Similar automation - like receipt matching - is also at the core of Brex’s product suite and a prime example of shifting employees' focus from the “repetitive and routine” to “outliers and strategic.”
Here’s how Brex envisions AI benefits for specific users:
For employees
Automatically fills in documentation requirements, including receipts and memos
Automatically routes spend to the correct budget so employees don’t need to manually assign their budgets each time
Answers finance-related questions
For managers
Highlights anomalous or high-risk transactions so managers can focus on outlier expenses for review
Automatic follow-up with employees on missing documentation or policy violations
For travel managers
Assist in planning group offsites
Automatically book travel according to your preferences
For finance teams
Gain insights into company spend among different employees, departments, or even compared to other companies
Close the books faster with automatic categorization
Provides real-time insights based on spend trends
Highlights expenses that need a closer look
Automatically follow up with employees on missing documentation or policy violations
Easily build expense policies based on different risk levels/similar customers
Automate procurement workflows
See
for even more on Brex + AIWinning Vendors: Specialists + Incumbents
Emergence Capital — an enterprise-focused VC firm — has been publishing excellent content on AI that I agree with, especially the value of specialization:
“But in business settings, similar to all cloud software, AI’s value will be most powerful when tightly focused.” - Gordon Ritter, Emergence Capital
That said, I believe that incumbent software vendors are also advantaged due to:
Low barriers to adding AI: As shown by the explosion of AI product releases — see Ian Ito’s B2B SaaS Generative AI Tracker — and the existence of AI plugins in a short period of time.
Customer acquisition costs + capital: Achieving scale in SaaS was already slow and capital intensive — Key Banc’s flagship SaaS benchmarks show reaching $25 million of ARR requires over 7 years and $31 million of capital. Due to higher compute costs, AI startups will likely have lower gross margins than traditional SaaS models and therefore need even more capital to hit scale. Whereas incumbents only need to roll out — at negligible cost — their new AI modules to their existing customers.
Trust + security: Not only do incumbents have a security track record with their customers, but they are also financially aligned. A ServiceNow or Brex is less likely to take risks with a marquee, high ARR customer than a potentially desperate AI upstart might take.
Predictions: Business + P&L Impact
In the wave of hype and excitement over AI, framing the business and P&L impact of AI is a useful exercise.
Goldman Sachs research forecasts AI lifting economic productivity by 1.5% per year, raising global GDP by 7%, and corporate profits by 30% over a 10-year horizon. Central to this forecast is AI taking the place of roughly 25% of human workloads.
While not fully comparable, my own estimates of AI’s P&L impact are more conservative than Goldman Sachs’ due to:
Degree of human replacement: While the recent pace of innovation in AI has been staggering, I have more skepticism regarding full human labor replacement.
P&L flow through: Some AI savings will be shared with customers, AI vendors, and spent internally.
Existing non-AI optimizations: Businesses have not been asleep — back office and supply chain automation programs have been going on for decades, leading to less “low hanging fruit” and margins for AI to fix.
Implied AI ROI: Goldman’s 30% corporate-wide profit lift implies returns on AI investment far beyond traditional software ROIs.
My poll (n = 154) on AI’s profitability impact yielded similar results with EBITDA margin gains below Goldman Sachs estimates.
However, either forecast suggests AI is worthy of the hype and adjusting corporate priorities.
This post was written in collaboration with Brex and will also be published on Brex Journal.
Extra Content: Fresh AI Search Data
Like our SaaS Demand Index, we are tracking interest in AI for the Big 3 Clouds (AWS, Azure, Google) via monthly Google Search volume data across 479 AI-related, vendor-linked keywords.
While the surge in AI interest has been nearly chart-breaking to date, June 2023 saw a 4% sequential decline versus May 2023:
As always when working with search volume data, consider this directional and recognize its monthly volatility is inherent.