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Unlocking the Elusive Business Value of GenAI - A 2025 Forbes Tech Council Deep-Dive

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  Published in Business and Finance on Thursday, November 20th 2025 at 7:00 GMT by Forbes

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Unlocking the “Elusive” Business Value of GenAI – A 2025 Forbes Tech Council Deep‑Dive

In a November 20, 2025 edition of Forbes Tech Council, the article “How to Find the Elusive Business Value of GenAI – All You Need Is Focus” tackles a problem that has been haunting technology leaders for years: how to turn generative AI (GenAI) from a hype‑filled buzzword into a measurable, strategic driver of revenue, cost savings, and customer experience. Drawing on case studies, proprietary research, and interviews with AI product managers, the piece lays out a pragmatic, step‑by‑step framework that emphasizes focus—choosing the right problems, the right metrics, and the right stakeholders—to make GenAI projects deliver tangible business outcomes.

1. Why “Business Value” Is Hard to Pinpoint in GenAI Projects

The article opens by explaining that, unlike classical software upgrades or data‑analytics pipelines, GenAI solutions are probabilistic, context‑dependent, and highly unstructured. They can generate creative content, automate routine tasks, and provide insights, but their outputs vary from one iteration to the next. This variability makes traditional ROI calculations—based on fixed costs and predictable savings—less applicable.

The authors reference a Forbes survey (linking to a separate 2024 report) that found 68 % of CIOs reported difficulty quantifying the economic impact of AI pilots, and a staggering 57 % said they had stopped a pilot prematurely because they could not justify the spend. By framing the problem this way, the article underscores that focus is not a luxury but a necessity.

2. A Structured Approach: Four Pillars of Focus

The article proposes a four‑pillar framework that mirrors the “OKR” (Objectives and Key Results) methodology popular in tech companies but adapted to GenAI’s unique characteristics. These pillars are:

The article’s authors point out that focus begins at the problem‑definition stage. If you ask “Can we use GenAI to improve customer service?” without a quantifiable outcome (e.g., “reduce average handle time by 30 %”), you’re likely to end up with a prototype that looks impressive but delivers little value.

3. From Vision to Value: Practical Steps

a. Map the Value Chain

The piece recommends starting with a value‑chain mapping exercise. Identify where GenAI can intervene—content creation, data enrichment, predictive maintenance, etc.—and evaluate the marginal impact on each link. This process is illustrated by a mid‑sized retailer that used a GenAI chatbot to automatically generate product descriptions, saving $12k in copywriting costs per quarter and improving SEO rankings by 4.5 pp.

b. Prioritize Use Cases with High Impact, Low Risk

A scoring matrix—combining impact potential, data availability, and governance risk—helps teams prioritize. The Forbes article cites a tech firm that used such a matrix to pick a “text‑to‑image” use case for internal brand assets, reducing design cycle time from 7 to 2 days.

c. Build the Right Experiment

Because GenAI models are data‑hungry and iterative, the article stresses rapid prototyping. The authors advocate a “model‑as‑a‑service” approach, where the AI engine is hosted on a cloud platform that supports continuous training. They also recommend partnering with a trusted vendor (linked to a Forbes partner article on AI-as-a-Service platforms) to lower the upfront engineering burden.

d. Measure with a Focused KPI Set

The article lists essential KPIs:

• Cost‑to‑Serve (direct savings from automation)
• Time‑to‑Market (speed of content or product launch)
• NPS/CSAT (customer satisfaction shift)
• Model Accuracy & Bias (quality of outputs)

A key takeaway is that business‑centric KPIs should trump purely technical metrics. For instance, a language model might achieve 95 % token accuracy, but if the content still requires manual edits, the true value is lost.

e. Scale with Governance and Change Management

Once a pilot demonstrates a positive ROI, the article stresses scaling under governance. This involves data‑governance policies, bias‑mitigation protocols, and a change‑management plan that includes training for non‑technical stakeholders. The article links to a Forbes governance white‑paper that outlines a “GenAI Maturity Model” for enterprises.

4. Overcoming Common Pitfalls

The article shares a cautionary tale from a financial services firm that let a GenAI‑powered compliance tool drift out of scope, only to discover that the model was generating false positives that increased operational costs. The firm had to halt the deployment and rebuild a more focused use case, emphasizing the importance of a disciplined focus cycle.

5. Real‑World Examples and Success Stories

• E‑commerce Personalization: A mid‑size retailer used GenAI to personalize email subject lines, resulting in a 12 % lift in open rates and a 4.2 % lift in conversion, translating to an additional $2.1M in annual revenue.
• Healthcare Diagnostic Support: A hospital integrated a GenAI model to triage patient notes, cutting triage time by 18 % and freeing up nurses for direct patient care.
• Manufacturing Predictive Maintenance: A plant deployed a GenAI model to predict equipment failures from sensor logs, reducing unplanned downtime by 22 % and saving $1.5M annually.

Each case underscores the article’s central thesis: the value of GenAI emerges when the focus is on a well‑defined business problem, a measurable outcome, and a disciplined experiment–measure–scale loop.

6. The Future: From Focused Pilots to Enterprise‑Wide Impact

The article concludes by envisioning a future where GenAI is not just a “proof‑of‑concept” tool but a core engine of enterprise transformation. It emphasizes that focus must evolve from the project level to an organization‑wide strategy. The authors suggest establishing a GenAI Center of Excellence (CoE) that maintains a playbook of proven use cases, provides governance standards, and creates a repository of reusable data pipelines and models.

For those leaders reading the piece, the takeaway is clear: the elusive business value of GenAI isn’t hidden in the algorithms—it’s unlocked by disciplined focus on the right problems, the right metrics, and the right organizational structures.

Further Reading (Links Included in the Original Article)

1. “AI‑as‑a‑Service Platforms for Rapid Deployment” – Provides a comparative overview of cloud vendors that support GenAI workloads.
2. “GenAI Maturity Model: From Experiment to Enterprise” – A Forbes white‑paper outlining stages of AI adoption and governance best practices.
3. “Case Study: GenAI in Healthcare” – An in‑depth look at a hospital’s implementation, including data architecture and ROI analysis.

These links deepen the reader’s understanding of the practical tools and frameworks that can help turn a GenAI experiment into a repeatable, scalable source of business value.