The BCG article “Four Ways to Accelerate Growth with AI and Analytics” by Jeremy Kuriloff, with support acknowledged to Michael Wahlen and Rahul Desai, offers a timely view of how AI can move beyond efficiency and become a strategic growth engine. Its central message is highly relevant for internal audit and corporate governance because it shows that analytics is no longer just a reporting tool, but a mechanism for identifying opportunities, anticipating disruption, and strengthening oversight in fast changing markets.
The article argues that companies can use AI and analytics to uncover unexpected adjacencies, detect emerging customer priorities, reveal early competitive bets, and map vectors of disruption. That is an important shift in thinking, because growth strategy is often framed around intuition, management experience, or quarterly performance data. The BCG perspective suggests that richer data signals, including patent citations, social listening, investor materials, scientific publications, and company announcements, can materially improve strategic judgment. For internal audit, this matters because such tools can help test whether strategic assumptions are grounded in evidence and whether management is looking far enough beyond the core business.
One of the most interesting points is the use of patent citation networks and natural language processing to identify new uses for existing technologies. The example of a national oil company finding a cosmetics application for polyol esters is unusual, but it is also highly instructive. It demonstrates how overlooked data can reveal commercially meaningful adjacencies that traditional planning methods might miss. For audit committees and boards, the implication is that strategic innovation should not be judged only by headline revenue targets. Oversight should also consider whether management has a disciplined process for scanning adjacent markets and validating new growth options before capital is committed.
The section on emerging customer priorities is especially relevant for governance because it connects strategy with stakeholder trust. AI can analyze reviews, survey comments, call center transcripts, and web journeys to detect changing sentiment before it shows up in financial results. That kind of early warning is valuable for compliance officers and control functions, especially in regulated or reputation sensitive sectors. Internal audit can use these insights to assess whether the organization is reacting quickly enough to customer concerns, whether product decisions are aligned with evolving expectations, and whether data privacy and model governance standards are being followed in the process.
The article’s discussion of competitor signals and disruption mapping has direct practical value for supervisory bodies. By tracking investor days, research output, hiring patterns, patent activity, and funding flows, management can identify where rivals are placing early bets and where new technologies may alter business models. This is not only a strategy issue. It also affects risk governance, because delayed recognition of disruption can lead to poor capital allocation, weak M&A choices, and missed transformation windows. Audit committees should therefore expect periodic reporting on external intelligence methods, model reliability, and the assumptions used to translate analytics into action.
A final and forward looking takeaway is the growing importance of agentic AI and always on analytics. The article suggests that AI agents can automate data collection, cleaning, and monitoring, making strategic sensing continuous rather than periodic. For internal revision, that raises both opportunity and responsibility. It creates a stronger evidence base for advising on risk, resilience, and performance, but it also increases the need for oversight of model use, data quality, accountability, and escalation paths. The full article “Four Ways to Accelerate Growth with AI and Analytics” by Jeremy Kuriloff is available here.