University of Kwazulu-Natal

How To Maximize Roi On Ai In 2026 Mission

Bonisiwe Shabane
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how to maximize roi on ai in 2026 mission

The promise of agentic AI has been massive, autonomous systems that act, reason, and make business decisions, but most enterprises are still struggling to see results. In this episode, host Chris Brandt sits down with Sumeet Arora, Chief Product Officer at Teradata, to unpack why the gap exists between AI hype and actual impact, and what it takes to make... From the shift toward “AI with ROI” to the new era of human + AI systems and data quality challenges, Sumeet shares how leading enterprises are moving from flashy demos to measurable value and... 06:05 Redesigning the Human AI Interface 09:15 From Demos to Real Economic Outcomes To listen to explicit episodes, sign in.

Sign in or sign up to follow shows, save episodes, and get the latest updates. As we close out 2025, most leadership teams are in the same place. They know AI is clearly important, their plan is fuzzy, the team is anxious, and “wait and see” is starting to feel risky. This issue breaks down 6 leadership moves to turn AI from experiments into measurable ROI in 2026, without losing the human side of your culture! The 6 leadership moves for AI ROI in 2026: “Wait and see” has been a strength in our region.

We don’t chase shiny objects. We watch, we learn, and we move with purpose. But with AI, the risk is falling behind. Move intentionally and start building internal capability now. How C-Suite Leaders Can Transform AI Experiments into Measurable Business Value. This blog is based on NStarX engagements with various enterprises through their AI journey

The boardroom conversations have shifted. What began as excited discussions about AI’s transformative potential in 2024 has evolved into more sobering questions about actual returns. As we enter 2026, C-suite leaders face a critical juncture: How do we move beyond the pilot phase and create systematic, measurable value from our AI investments? The numbers tell a compelling story. While 58% of data and AI leaders claim their organizations have achieved “exponential productivity gains” from AI, the gap between aspiration and measurement reality has become impossible to ignore. It’s time for a more disciplined approach.

The convergence of market research and executive priorities has crystallized around three critical investment areas that will define competitive advantage in 2026: Current enterprise AI initiatives cluster around four strategic areas, each with distinct ROI characteristics: Every vendor promises unprecedented productivity, automation, and efficiency from their AI tool. Boards expect it. CEOs demand it. Budgets are shifting toward it.

And yet, very few sales organizations can prove that their AI investments are delivering meaningful ROI. AI's value is obvious. Its capabilities are accelerating. And it's already embedded in nearly every tool sellers touch. The real issue is that the outcomes customers see today aren't yet strong or consistent enough to justify scaling AI across the revenue engine. Most teams are being told to "use AI," but they aren't given clarity on:

Because every AI decision today carries real operational and reputational risk, many revenue leaders choose what they deem to be the safest route: stick with incumbent vendors and platforms. That caution is understandable, but it also explains why AI ROI is so inconsistent. AI strategy best practices for 2026 focus on five pillars: governance and risk management, data and platform readiness, high-ROI use case prioritization, operating model and skills, and scale-through-delivery (MLOps and security). Align these with business outcomes, measure ROI continuously, and deploy AI workers to automate end-to-end processes. Board conversations have shifted from “Should we use AI?” to “Where does AI deliver ROI this quarter?” Yet many organizations remain stuck in pilots, tool sprawl, and governance debates. According to McKinsey’s State of AI report, adoption and investment in genAI surged in 2024–2025, but only a fraction of companies captured material financial impact.

This guide distills AI strategy best practices for 2026 into a practical blueprint LOB leaders can execute now. You’ll learn how to build a durable AI governance framework, create a prioritized AI roadmap, operationalize MLOps for generative and predictive use cases, and transform teams and processes for scale. We’ll also show how an AI workforce model—AI workers that execute full workflows—bridges the gap between strategy and shipped results. Throughout, we connect each step to measurable outcomes and risk-aware execution. Most AI strategies fail because they are tool-first, IT-only, or pilot-bound. Success in 2026 requires business-led goals, risk-aware governance, use case prioritization, and an operating model that ships value in weeks, not months.

Leaders cite three recurring blockers: unclear business outcomes, fragmented data/platforms, and lack of an operating model that spans experimentation to production. Many organizations still treat AI as side projects rather than capability building. Meanwhile, regulation and risk concerns slow momentum without improving controls. The result is stalled pilots, duplicate tooling, and “AI theater.” 4 minutes ago • by Mark J. Greeven, José Parra Moyano, Michael R.

Wade, Amit M. Joshi, Jialu Shan, Didier Bonnet, Robert Hooijberg in Artificial Intelligence December 9, 2025 in Artificial Intelligence AI is reshaping cybersecurity, arming both hackers and defenders. Learn how to stay ahead in the fast-evolving AI cybersecurity arms race.... December 1, 2025 • by Tomoko Yokoi in Artificial Intelligence

Vibe coding lets anyone build apps in plain English using AI, unlocking innovation and speed—but businesses must manage security, compliance, and quality risks.... Daily stocks & crypto headlines, free to your inbox By continuing, I agree to the Market Data Terms of Service and Privacy Statement

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Sign in or sign up to follow shows, save episodes, and get the latest updates. As we close out 2025, most leadership teams are in the same place. They know AI is clearly important, their plan is fuzzy, the team is anxious, and “wait and see” is starting to feel risky. This issue breaks down 6 leadership moves to turn AI from experiments into measurable ROI in 2026, without losing the human side of...

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