AI is making progress every single day while addressing tasks currently performed by humans. Interestingly, automating corporate strategy has become a new milestone for AI. Looking at the progress of DeepMind’s AlphaStar, one can recognize the recent progress in automatically generating corporate strategy against uncertainties.
For the matter of fact, AI is already being applied to model decision making of government and corporations. China has set itself as an example while using the ML model for country’s decision making known as Policy Change Index.
AI agents are improving themselves at hard problems they couldn’t solve before. Specifically, DeepMind’s AlphaStar and certain other similar systems are upgrading the algorithms and the brains that run the economy of the world. Such advent will impact the way services are delivered as the ‘strategy’ of delivering services still has a dependency on humans at keyboards wearing headsets. The advancement also impacts the finance sector while 80 percent of market affairs is algorithmic trading agents.
Government and corporate projects possess limited scope to model one part of corporate strategy. Even if it is credit risk models, recommendation systems, customer segmentation, legislative risk models, or trading algorithms, such scope-limited technique benefits from being conveniently abstracted into a box in a flowchart with a label on it.
The technology of automation has been in corporate strategy for a while now but recent advancements in AI can make it better. So far we can predict that corporate strategy development is going to benefit from more automation AI in particular.
To design a system that develops strategies, building a simulator to define the limits of the world where the agent can explore and make decisions, can be a good commencement. Such simulator requires a ton of data on past decisions and their outcomes to gain experience. This process also involves deciding how much of the world to simulate.
On the other side of the coin, there exist some key challenges that hold back the wider adoption of AI for autonomous corporate strategy development and implementation.
Humans need to understand the learning of machine from the data, its processing and its final molding as a decision. Several experts and researchers are currently working to address such interpretability issues.
Dataset Handling Tools
Another challenge is handling datasets for training these AI agents and labeling the data without much involvement of human effort. Snorkel.org is one remarkable project for labeling, transforming, and organizing datasets for wide corporate user base and US Government support. Additionally, pre-built datasets and models are becoming more available. Such datasets are put into the public domain by researchers, corporates, and governments adopting open data policies.
The third challenge is automating the process of building machine learning models, which today requires a data scientist, a machine learning engineer, and a DevOps engineer, along with a project manager for proper functioning. Many active automated machine learning projects are aiming at the removal of such profiles from the process as much as possible.
Lastly, biases are also a threatening challenge in this regard. AI systems are quite good at learning and perpetuating bias that prevails in the existing datasets. These biases restrict some important business processes from easily adopting strategies generated using AI.
Even after certain constraints, AI will be able to develop a corporate strategy which would be quite popular. From autonomous cars to autonomous corporate strategy, AI as a field of study is not done yet rather it has highly innovative possibilities to explore further.