Automation differs in corporate strategy because the function is low-volume and high-judgment, so the aim is rarely to automate the final decision.
Instead, automation is most useful for gathering market intelligence, tracking signals, synthesising internal and external data, building first-pass analyses, and keeping leadership aligned, which shortens strategy cycles while leaving prioritisation, trade-offs, and conviction with senior humans.

This AI Automation helps investment teams accelerate their deal sourcing by automatically analyzing company, industry, and performance metrics from pitch decks, enabling the review of 10× more opportunities through significantly faster and more efficient opportunity processing.
QuantumLight deploy Aleph, a proprietary ML model trained on 10 billion data points from 700,000 VC-backed companies, to identify and recommend every investment the fund makes, removing human bias from venture capital decision-making.
Warren County, Kentucky uses AI bots from Jigsaw (Alphabet) and statistical analysis from Polis to conduct large-scale resident consultations, surfacing areas of consensus to guide local government planning and reduce political polarisation.
S&P Global utilise a combination of Large Language Models to automate the identification of acquisition targets and the synthesis of vast datasets, allowing their strategy teams to evaluate potential deals with significantly higher speed.
McKinsey deploys Lilli, a proprietary generative AI platform, to give its 45,000 consultants instant access to over 100,000 internal documents and interview transcripts. More than 72 percent of staff use it monthly, reducing research and synthesis time by approximately 30 percent.