N※N

AI Isn't the Product--Value Is: Preventing the Shiny-Object Trap

85

  //science-technology.news-articles.net/content/2 .. t-value-is-preventing-the-shiny-object-trap.html
  Published in Science and Technology on Tuesday, December 9th 2025 at 9:55 GMT by Forbes

• 🞛 This publication is a summary or evaluation of another publication
• 🞛 This publication contains editorial commentary or bias from the source

2025-12-09 224 x 224 / 12014 Bytes

2025-12-09 178 x 224 / 7510 Bytes

2025-12-09 224 x 224 / 14388 Bytes

2025-12-09 198 x 224 / 13616 Bytes

AI Isn’t the Product—Value Is: Preventing the Shiny‑Object Trap
Forbes Tech Council, December 9, 2025

The Forbes Tech Council article “AI Isn’t the Product: Value Is Preventing the Shiny‑Object Trap” tackles a recurring pitfall that plagues many organizations today: the temptation to adopt the newest artificial‑intelligence technology simply because it’s the next big thing. The author—an industry veteran and member of the Forbes Tech Council—argues that the true success metric for AI initiatives isn’t how impressive the underlying algorithm looks, but the concrete business value they deliver.

1. The Core Premise: AI as a Value Engine, Not a Product

The piece opens by challenging the prevailing narrative that AI itself is a standalone product. “AI is an enabler,” the writer writes, pointing out that an algorithm or a cloud‑based service only becomes a product when it solves a specific problem for a defined user. This distinction mirrors the classic software‑as‑a‑service (SaaS) model, where the value lies in streamlining operations or opening new revenue streams rather than in the code base itself.

The author underscores that companies often treat AI as a silver bullet, measuring success by the novelty of the model—chatGPT‑style language generation, computer‑vision image classifiers, or generative design tools—rather than by the metrics that matter to the bottom line. By doing so, they fall into the “shiny‑object trap”: a cycle of hype, experimentation, and short‑lived pilots that never translate into sustainable business outcomes.

2. The Shiny‑Object Trap: A Cautionary Tale

Through a series of real‑world anecdotes, the article illustrates how organizations can get distracted by flashy AI demos. One example features a Fortune 200 retailer that deployed a generative‑AI recommendation engine after seeing a slick pitch. The system was technically impressive but suffered from data quality issues, user distrust, and a lack of integration with the existing e‑commerce platform. The result? A spike in page views but no measurable increase in sales, followed by a costly rollback.

The writer also references a Forbes partner article—“Why the AI Hype Cycle Ends in Burnout”—to provide empirical context. That piece highlights that the average AI adoption cycle is less than 18 months before enthusiasm wanes. By integrating the insights from both pieces, the author builds a compelling case that sustainable AI deployment hinges on disciplined strategy rather than enthusiasm.

3. Value‑First Strategy: Aligning AI with Business Objectives

The heart of the article offers a pragmatic framework:

The author emphasizes that AI should be treated as a capability rather than a commodity. For instance, a predictive‑maintenance model can be bundled into a broader “Digital Twin” service that drives continuous improvement across manufacturing lines. By packaging AI as part of a larger business solution, companies create recurring revenue streams and deepen stakeholder trust.

4. Metrics That Matter: From “Cool” to “Cost‑Effective”

One of the article’s most valuable contributions is a discussion on how to quantify AI value beyond “AI‑ness.” It references a Forbes Tech Council whitepaper, “Measuring the ROI of AI Projects,” which presents a three‑tiered model:

1. Economic Value – Direct revenue impact or cost avoidance.
2. Operational Value – Process improvements, efficiency gains, and error reduction.
3. Strategic Value – New capabilities, market differentiation, and future‑readiness.

Using the ROI matrix, the article walks readers through a case study of a logistics firm that introduced AI‑driven route optimization. By measuring fuel cost reductions (economic), on‑time delivery rates (operational), and brand loyalty (strategic), the company documented a 27 % increase in profitability over two years—proof that AI can deliver measurable business outcomes.

5. Common Pitfalls and How to Avoid Them

The author lists five “red flags” that signal an organization might be chasing the next shiny object:

The article warns that these pitfalls can erode the initial enthusiasm and drive companies to abandon their AI projects prematurely, thereby missing out on incremental gains that could have accumulated over time.

6. The Role of Leadership and Culture

Leadership is singled out as the linchpin for sustaining AI initiatives. The article cites a Forbes survey where only 34 % of executives reported high confidence in AI adoption, highlighting a leadership gap. The writer argues that senior leaders must champion AI not as a technology fad but as a strategic enabler. This involves:

• Creating an AI Vision that aligns with the broader corporate strategy.
• Allocating Budget for Iteration rather than one‑off pilots.
• Incentivizing Cross‑Functional Collaboration through shared KPIs.
• Encouraging a Culture of Data Literacy so that all employees can engage with AI insights.

The article concludes by noting that the most successful AI deployments often mirror successful enterprise initiatives in other domains—clear objectives, stakeholder buy‑in, iterative execution, and a relentless focus on value.

7. Take‑Away: From Shiny to Sustainable

In sum, “AI Isn’t the Product—Value Is” presents a compelling argument that AI’s true worth lies in its ability to deliver measurable business outcomes. The article urges organizations to:

1. Anchor AI initiatives in clearly defined business problems.
2. Measure success against economic, operational, and strategic KPIs.
3. Build resilient governance and ethical frameworks.
4. Promote a culture of continuous learning and cross‑functional collaboration.

By adopting this value‑first mindset, companies can escape the shiny‑object trap and transform AI from a buzzword into a sustainable engine of growth.

Further Reading

• Forbes Tech Council: “Measuring the ROI of AI Projects” (Whitepaper)
• Forbes: “Why the AI Hype Cycle Ends in Burnout” (Partner Article)
• Forbes: “AI Governance: From Ethics to Execution” (Expert Opinion)

These resources deepen the article’s core themes and offer actionable frameworks for executives, data scientists, and product managers looking to harness AI responsibly and profitably.