Hito Steyerl was recently crowned the most powerful contemporary artist by ArtReview. The Professor at the renowned UdK Berlin cofounded the UdK Research Centre for Proxy Politics. Her latest works at the Documenta and the Skulptur Projekte continue to dissect the digital world. It was a critique of her that inspired to span the bridge from Arts to Artificial Intelligence (AI) to Corporate Planning and Analysis:

“Statistics have moved from constructing models and trying to test them using empirical data to just using the data […] They keep repeating that correlation replaces causation. But correlation is entirely based on identifying surface patterns, right? The questions–why are they arising? why do they look the way they look? – are secondary now. If something just looks like something else, then it is with a certain probability identified as this “something else,” regardless of whether it is really the “something else” or not.” (Hito Steyerl, full article)

AI-driven decision as  a "Black  Box"

The indication that AI-driven decisions are merely understood is widely discussed. Two examples focus on the societal effects and responsibility for

• Social media in creating Echochambers– the algorithmic incarnation of the confirmation bias to generate advertisement revenues - and handling hate speech,
• Compensation claims on machine learned driverless cars involved in accidents.

How can the risks these algorithms post be economically grasped if the algorithm is understood as a black box immune to insights and legal claims? This is one question to ask in the coming planning cycles when these concepts are to be integrated into the value chains.

Can the Technology-Driven Methods Bring Value to FP&A?

Another aspect is considering AI in the FP&A process itself. The AFPs Survey on Budgets ranks the logistics team last in “value perceived from budgeting”. Logistics is the corporate unit most exposed to AI. The communicative, non-deterministic aspects of corporate planning are undervalued when planning mainly concerns dead matter. Key question is, can their technology-driven methods bring value to the very lively corporate budgeting and forecasting? They can and they should because AI provides for the basic heuristics evolution prove most fit. This includes the misuse like the German saying to use a cannon to shoot a sparrow “mit Kanonen auf Spatzen schiessen”.

AI in FP&A: Exploration-Exploitation Tradeoff

One intersection of artificial intelligence and budgeting is the exploration-exploitation tradeoff. Exploration is the acquisition and collection of data while exploitation is making use of it. The selection of a casino machine involves this consideration. Explore by using coins on many machines, exploit your lucky one until the luck leaves. The conundrum lies in the interaction of the terms: continued exploration requires reevaluation of the exploitation. Two valuable FP&A insights from the tradeoff emerge: there is no optimal strategy and the timeframe is crucial.

The next cycle resets the existing one, makes it obsolete. Additionally, the timeframe of the corporate plan exceeds most Las Vegas stays. This holds true for the complexity of the planning scope. Compared both to choosing “your” machine a casino hall. The lesson can be applied to investment decisions as decisions on exploitation. The decision process should reflect explorative changes rather early than late. A strong argument for the Rolling Forecast where the near future weighs heavier than the distant one. One AI representation reflecting this is called “Least Regret”. It converts the valuation form maximizing future potential gains to minimizing future regret. Least Regret is not about chasing the best, but about preventing the worst. Regular investment reviews and divide & conquer approaches to big projects are representations of this method. Exploitation must start to yield results, although in a month – don’t make it a year – exploration may a turning tide.

Neomania in FP&A

People over-explore. The former “quant” Nassim Taleb described Neomania as focus on “to be justified innovations” at the expense of proven methods. The concept was described in detail as part of the not so obvious facets on budgeting and forecasting. Does AI qualify for over-exploration? It is a humane survival instinct, as the unknown options have a strong upside with limited downside. The math behind the bias was proven by the Operations Research Professor John Gittins from Oxford University. Gittins index structures decisions on which path into the unknown to take. It helps to identify the exploitable casino machine. The index is calculated out of the available explored information of each option/ machine. Gittins embedded a concept called discounting of future rewards. That concept sounds familiar to FP&A. The results apply well to gambling: a winning turn urges to stay, a streak justifies a couple losses before switching to more promising machines the gambler knows less about. It shows, there is value in finding out. Results are suboptimal with switching cost and timely calculation requirements, which depict the main requirements in Capital Expenditure decisions: well calculated, because of high costs in switching. Flexibility by design diminishes this. One example of a regulatory induced flexibility is the Data Portability concept in the European Data Protection Law. It seems to improve chances if a vendor selection goes sour.

Two options are cited to optimize under the mentioned limitations: the first is the mentioned Least Regret. The second one is the simplification. Simplification as an alteration to Gittins Index skips discounting future rewards calculated out of past information. It focusses solely on the potential of the new. These upper confidence bound algorithms are a perfect metaphor for Neomania. The emphasis is on exploration, the unknown strongly favored. Incubators, Accelerators and Break Outs are organizational means to harness the next big thing hidden in plain sight. A stringent funding methodology coupled with experience should navigate these means into the drawer or out into the business world.

Combining the Now and the Future requires a well-prepared FP&A team

In general, the explore-exploit offers more practicable insights to FP&A:

1. For the past, good teams stay away from exploring late in the game and exploit the best option. For FP&A it then is to lay out the efficacy of handling the restless reality of past decisions telling the story via the financial statements.
2. The reality of tomorrow is best prepared for with a restless mind that exploits late while exploring the less mature fields. The reality assigns 90% of FP&A to pursue the first, omitting the potential to better understand the future economically.
3. Artificial intelligence tackling the explore-exploit tradeoff so far has not and is believed to never will be able to solve this dilemma.

Future coordination is a "brainer "- not a no-brainer. Combining the Now and the Future requires a well-prepared FP&A team. AI as Robotic Process Automation (RPA) can support with the “no-brainers”. Looking ahead requires diligence and thinking time, which RPA can provide space for. Also, computer algorithms prescribe to keep exploring for future gains. This includes looking to the Arts and the Artificial Intelligence developments.