AI Works Best When You Can Measure the Problem | Week 3 to 9 Aug 26

This week, two stories show why clarity matters when you deploy AI. One company solved a concrete physical constraint and restored a dying asset to full capacity. Another faced unspecified failures in an education product—and paid a reputation cost. The difference teaches SMBs where AI actually delivers, and where it risks creating more problems than it solves.

The win this week

Zanskar deployed AI optimization at a geothermal power plant in New Mexico that was slowly becoming uneconomical. The plant's underground reservoir water was cooling down day by day, which meant less heat available to generate electricity. At some point, the cost of running the plant would exceed the revenue it produced, and the operation would shut down entirely.

Instead of abandoning the asset, Zanskar used AI-driven optimization technology to restore the plant's performance. The system worked—the plant is now running at full capacity again. The win wasn't about making the software faster or automating paperwork. It was about solving a measurable physical problem with a clear economic outcome: full operation or closure.

(Source: MIT Technology Review, 29 Jul 2026)

The lesson this week

Saving Grace, an online homeschool, faces allegations of AI curriculum and service failures. The news reports the allegations, but the source does not specify what actually went wrong—whether the AI-generated lessons were inaccurate, the system crashed frequently, student outcomes dropped, or something else entirely.

What matters is that the company now carries the weight of unspecified failure allegations tied to AI. Even without documented details, the mere association with "AI curriculum and service failures" creates doubt. For a smaller education company, trust is fragile. Once damaged, it takes time and evidence to rebuild, regardless of whether the root cause was poorly vetted content, system reliability, or something external entirely.

(Source: News24, 27 Jul 2026)

What to do about it

Zanskar's success points to a simple rule: use AI when you have a concrete performance metric and a specific bottleneck you can measure. The geothermal plant had throughput (megawatts generated), cost per unit (dollars per MW), and a clear constraint (reservoir temperature). The AI system optimized against those real numbers. For an SMB, this means asking: Can I track the metric before and after? Can I isolate what changed? If the answer is yes, AI has a fighting chance.

Saving Grace's trouble suggests the opposite risk. When you deploy AI into a domain—like education—where outcomes are slower to measure and where external validation takes time, you inherit both the technology's blindspots and the liability of its failures. An online education product sold to parents relies on trust that content is accurate and systems are reliable. Allegations of AI-related failures erode that trust even when the specifics remain unclear. The lesson is not that AI education is impossible, but that smaller companies need quality gates and external validation *before* content reaches students, not after complaints surface.

The difference between these two stories comes down to transparency and measurement. One company solved a clear problem and can point to the result. The other faced unspecified failures and paid in reputation. If you're considering AI for your business, start by asking whether you can measure success in the way Zanskar did—with numbers you can show. If the outcome is harder to quantify or slower to appear, invest in validation first, deployment second.

Disclaimer

This article is based on LinkFeed Issue 37 (3 Aug to 9 Aug 2026) — two verified case studies (one AI win, one AI failure). Sources: AI Win: MIT Technology Review, 29 Jul 2026 · Lesson: News24, 27 Jul 2026. For informational purposes only; verify critical claims at the source.

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