Today's Opportunities: Key Automation Benefits
When leaders respond to immediate panic, new business risks and mitigation often emerge. Two recent examples highlight the results of rushing to implement and publish positive results from AI adoption. The Wall Street Journal reported in April 2025 about companies struggling to achieve AI returns. A few weeks later, MIT retracted its technical paper on AI, which failed to demonstrate the results that led to its publication.
These reports show the pitfalls of overreliance on AI without common sense guardrails, but not all are on track on the land of corporate AI adoption. We see incredible results when viewed from the wise use of AI and the wise use of related technologies in automating processes across the industry. Now we can go through the “fear of missed” stage and reach the business, so where is the best place to look for value when applying AI to automation in your business?
While chatbots are almost as extensive as new app downloads for mobile phones, AI applications are in line with the unique purpose and architecture of the underlying AI systems being built. The dominant patterns that AI acquires are currently boiled down to two things: language (translation and patterns) and data (creating new formats and searching for data).
Example 1: Natural Language Processing
Manufacturing Automation Challenge: Fault Modes and Effectiveness Analysis (FMEA) is important and often labor-intensive. FMEAs are very likely to occur in stressful production line down scenarios, as they are not always performed before manufacturing equipment failures. With Intel, the global footprint of manufacturing facilities, separated by a wide range of distances, along with differences in time zones and preferred languages, makes this even more difficult to find the root cause of the problem. A few weeks of engineering effort is spent on each repeated FMEA analysis with the large amount of tools spreading between these facilities.
Solved: Utilizes CPU Computing Servers (NLPs) that are already deployed throughout the manufacturing tool log. There, observations regarding the operation of the tools are maintained by local manufacturing technicians. The analysis also applied sentiment analysis to classify words as positive, negative, or neutral. The new system runs FMEA with six months of data within a minute, saving weeks of engineering time, allowing production lines to actively serve on a preemptive schedule rather than causing unexpected downtime.
Issues for financial institutions: The programming languages commonly used by software engineers are evolving. The mature previous institutions often form over the years through a series of mergers and acquisitions, and continue to rely on critical systems based on 30-year-old programming languages that today's software engineers don't know much about.
Solved: Using NLP, it translates old and new programming languages, providing the boost software engineers need to improve the maintenance of critical operational systems. It uses the power of AI rather than dangerous rewrites or large-scale upgrades.
