Today’s opportunity: Significant automation gains
When leaders respond to immediate panic, new business risks and mitigations often emerge. Two recent examples highlight the consequences of rushing to implement and publish positive results from AI adoption. The Wall Street Journal reported in April 2025 on companies struggling to realize returns on AI. Just weeks later, it covered MIT’s retraction of a technical paper about AI where the results that led to its publication could not be substantiated.
While these reports demonstrate the pitfalls of over-reliance on AI without common-sense guardrails, not all is off track in the land of enterprise AI adoption. Incredible results being found from judicious use of AI and related technologies in automating processes across industries. Now that we are through the “fear of missing out” stage and can get down to business, where are the best places to look for value when applying AI to automation of your business?
While chatbots are almost as pervasive as new app downloads for mobile phones, the applications of AI realizing automation and productivity gains line up with the unique purpose and architecture of the underlying AI system they are built on. The dominant patterns where AI gains are realized currently boil down to two things: language (translation and patterns) and data (new format creation and data search).
Example one: Natural language processing
Manufacturing automation challenge: Failure Mode and Effects Analysis (FMEA) is both critical and often labor intensive. It is not always performed prior to a failure in manufacturing equipment, so very often FMEA occurs in a stressful manufacturing lines-down scenario. In Intel’s case, a global footprint of manufacturing facilities separated by large distances along with time zones and preferred language differences makes this even more difficult to find the root cause of a problem. Weeks of engineering effort are spent per FMEA analysis repeated across large fleets of tools spread between these facilities.
Solution: Leverage already deployed CPU compute servers for natural language processing (NLP) across the manufacturing tool logs, where observations about the tools’ operations are maintained by the local manufacturing technicians. The analysis also applied sentiment analysis to classify words as positive, negative, or neutral. The new system performed FMEA on six months of data in under one minute, saving weeks of engineering time and allowing the manufacturing line to proactively service equipment on a pre-emptive schedule rather than incurring unexpected downtime.
Financial institution challenge: Programming languages commonly used by software engineers have evolved. Mature bellwether institutions were often formed through a series of mergers and acquisitions over the years, and they continue to rely on critical systems that are based on 30-year-old programming languages that current-day software engineers are not familiar with.
Solution: Use NLP to translate between the old and new programming languages, giving software engineers a needed boost to improve the serviceability of critical operational systems. Use the power of AI rather than doing a risky rewrite or massive upgrade.