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Leadership, Not Technology, Drives AI Success

  //science-technology.news-articles.net/content/2 .. leadership-not-technology-drives-ai-success.html
  Published in Science and Technology on Wednesday, November 12th 2025 at 12:30 GMT by Forbes

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Back to Basics: Why Leadership, Not Technology, Determines AI Success

The latest Forbes Tech Council article, “Back to Basics: Why Leadership, Not Technology, Determines AI Success,” makes a clear and compelling case: the key to unlocking artificial intelligence’s full potential lies in the people who steer the ship, not the hardware or algorithms that power it. By weaving together research, real‑world examples, and practical guidance, the piece invites executives, board members, and AI practitioners to refocus their efforts on culture, governance, and strategic alignment.

1. The Premise: Technology Is a Tool, Leadership Is the Engine

The article opens with a stark observation: 70 % of AI initiatives fail to deliver meaningful ROI (source: McKinsey 2023 AI adoption study). The culprit? “A lack of clear ownership and strategic direction,” the author notes. Technology—whether it’s GPT‑style language models, reinforcement‑learning agents, or advanced analytics pipelines—is only as useful as the framework that integrates it into a business context.

To illustrate, the piece cites a case study from Acme Logistics, a mid‑size shipping company that invested heavily in predictive maintenance software. Because its leadership did not tie the initiative to a broader efficiency goal, the project stalled after two pilots. In contrast, TechNova, a cloud‑based SaaS firm, leveraged a dedicated AI steering committee that embedded AI outcomes into quarterly OKRs, driving a 23 % reduction in customer churn within a year.

2. Three Pillars of AI Leadership

The article identifies three foundational elements that leaders must cultivate:

These pillars echo the guidance found in the Forbes Tech Council’s AI Governance Framework (a link embedded in the article). The framework offers a step‑by‑step checklist—from data acquisition to model monitoring—that leaders can use to formalize their AI governance.

3. The Human Factor: Leadership Traits That Drive Success

A key segment profiles the leadership traits most correlated with AI success. Drawing on interviews with executives from NVIDIA, Google DeepMind, and IBM Watson, the article highlights:

1. Curiosity – Willingness to explore emerging AI possibilities beyond current use cases.
2. Resilience – Ability to navigate the inevitable setbacks of experimentation.
3. Communication – Translating complex AI concepts into business‑centric narratives.
4. Ethical Commitment – Prioritizing fairness and accountability over “first‑to‑market” advantage.

These insights dovetail with a Harvard Business Review survey (2019) that found 68 % of executives who regularly communicate AI outcomes experience higher stakeholder buy‑in.

4. Lessons from Failed AI Projects

The author doesn’t shy away from cautionary tales. One particularly vivid example comes from a consumer electronics firm that deployed an AI‑driven recommendation engine without a clear data governance plan. The result? Unintentional amplification of bias, leading to regulatory scrutiny and a 5 % dip in consumer trust. The article cites a Forbes link to a 2024 policy brief on AI bias mitigation, underscoring the urgency of embedding ethical checks early.

Another failure story involves a financial services provider that over‑reliant on proprietary AI models for credit scoring. Lacking transparency, regulators flagged the firm for “non‑compliant risk assessment.” The failure highlighted the need for open‑source tools and third‑party audits—resources the article recommends through the AI Now Institute (linked for further reading).

5. Practical Steps for Executives

For leaders ready to roll up their sleeves, the article lays out a concise 5‑step playbook:

1. Audit Current AI Landscape – Inventory tools, teams, and data pipelines.
2. Define Clear Success Metrics – Align KPIs with business outcomes (e.g., ROI, customer NPS, time‑to‑market).
3. Pilot High‑Impact Use Cases – Start small with measurable goals.
4. Scale Through Governance – Use the Forbes Tech Council’s AI Governance Framework to standardize across the enterprise.
5. Iterate & Communicate – Treat AI as an evolving capability; keep stakeholders informed of progress and lessons learned.

Each step is linked to deeper resources—such as a Forbes article on “AI Maturity Models” and a MIT Sloan review on “Scaling Responsible AI”—providing readers with a roadmap for action.

6. The Bottom Line

“Back to Basics” ultimately delivers a single, persuasive thesis: technology is the engine, but leadership is the driver. The article urges executives to view AI not as a silver bullet but as a strategic lever that requires vision, governance, and a culture that rewards experimentation. By following the outlined frameworks and learning from both successes and failures, companies can avoid the pitfalls that trap so many AI initiatives and instead build resilient, ethical, and high‑impact AI programs.

For those interested in a deeper dive, the Forbes Tech Council page hosts additional whitepapers, case studies, and an interactive AI maturity assessment—resources that can help leaders quantify where they stand and what steps are needed to advance.