Table of Contents

• The Paradox of Generative AI: Widespread Adoption, Minimal Impact
• Déjà Vu from the Digital Transformation Era
• Hype Is Fading, but the Opportunity Is Still Real
• Agentic AI: The Strategic Evolution Beyond Generative Tools
• What Makes Agentic AI Different?
• Real-World Impact: Early Wins from Agentic AI
• $650 Billion in Value: A Redefinition of Business Operations
• From Experimentation to Execution
• Neodata’s Perspective

In our previous newsletter, we spotlighted a report from the MIT Media Lab/Project Nanda that made waves across the AI landscape. The headline was hard to ignore: “95% of investments in generative AI have yielded no measurable return.” For many, it seemed to confirm growing doubts that generative AI, despite all the hype, might not be delivering the transformational value that had been promised.

Now, a month later, that report continues to reverberate—not because its conclusion was entirely pessimistic, but because it captures a deeper, more nuanced paradox unfolding in real time.

The Paradox of Generative AI: Widespread Adoption, Minimal Impact

We are living in what could be described as the “Gen AI paradox”—a moment in which mass adoption of generative AI is happening across industries, yet the impact on business fundamentals remains frustratingly elusive. Companies are deploying Gen AI tools at record speed, investing billions into pilots and prototypes. Employees across functions—from customer service to content creation—are using tools like ChatGPT, Midjourney, or Copilot to boost their daily productivity.

And yet, these individual wins aren’t translating into measurable bottom-line improvements at the organizational level. As the MIT report reveals, most corporate investments in Gen AI remain focused on marketing or sales use cases—often chosen for their visibility rather than their transformational potential. Meanwhile, back-end processes—where ROI tends to be higher—are being neglected.

This disconnect is not just a failure of technology; it’s a failure of strategy.

Déjà Vu from the Digital Transformation Era

As pointed out in a recent Harvard Business Review article by INSEAD professors Nathan Furr and Andrew Shipilov, today’s corporate leaders are falling into the same trap they did a decade ago with digital transformation. Back then, executives were encouraged to “let 10,000 flowers bloom”—to launch countless innovation experiments in the hope that some would become unicorns.

But without a strategic framework to align those experiments to core business goals, most of them fizzled out. The result was a proliferation of pilots and POCs with little to show in terms of structural change or scalable value.

The same pattern is emerging with Gen AI today. Many organizations are enthusiastically embracing experimentation—often driven by FOMO or pressure from boards and stakeholders. But experimentation without orchestration leads to scattered efforts, redundant tools, and eventually, frustration.

This is not just inefficiency—it’s a strategic misfire. Generative AI is being treated as a shiny object rather than a tool for solving real customer problems or transforming business models.

Hype Is Fading, but the Opportunity Is Still Real

According to Gartner, Gen AI has now entered the “Trough of Disillusionment”—the third phase in the hype cycle, where inflated expectations give way to hard realities. While this might sound negative, it’s actually a necessary stage in the maturation of any disruptive technology. It’s a signal that the market is shifting from speculation to validation, from excitement to execution.

But there’s a risk: many business leaders may interpret these growing pains as proof that AI can’t create value. That would be a mistake.

The truth is, AI can and is creating real value—just not always in the flashy ways we first imagined. And this brings us to a critical evolution in the AI landscape: agentic AI.

Agentic AI: The Strategic Evolution Beyond Generative Tools

If generative AI has sparked a wave of experimentation but struggled to deliver consistent enterprise value, then agentic AI may represent the next critical shift—from augmentation to autonomy, from output to outcomes.

Unlike traditional generative systems that focus on content creation or summarization, agentic AI introduces goal-oriented, decision-capable agents that can operate independently across complex business environments. These agents don’t just assist—they act, learn, and optimize.

And where generative AI may have disappointed in scale, agentic AI is beginning to show real, measurable results.

What Makes Agentic AI Different?

Agentic AI combines three transformative capabilities:

1. Autonomy – Agents can pursue goals, make decisions, and adapt to changing conditions without constant human input.
   
2. Integration – They can be embedded deeply into enterprise systems, not just sitting at the interface layer.
   
3. Execution – Beyond analytics or recommendations, agentic systems can directly initiate and complete tasks across workflows.
   

These are not futuristic ambitions—they are already in play. According to McKinsey research, a growing number of industrial and logistics firms are deploying agentic systems in mission-critical processes, and the results are impressive.

Real-World Impact: Early Wins from Agentic AI

• Manufacturing: Automated visual anomaly detection agents have dramatically improved defect identification, reducing waste and increasing throughput.
  
• Logistics: Intelligent routing and scheduling agents have optimized fleet usage and inventory levels, cutting logistics costs by more than 20%.
  
• Operations: Agents embedded in document-heavy workflows (like invoicing or compliance) have slashed cycle times from days to hours—or even minutes.
  

Beyond these immediate efficiency gains, agentic AI is enabling a new kind of enterprise agility—one where systems continuously learn, adjust, and improve without requiring end-to-end human orchestration.

$650 Billion in Value: A Redefinition of Business Operations

McKinsey estimates that agentic AI could generate $450 to $650 billion in additional annual revenue by 2030, particularly in advanced sectors like automotive, industrials, and energy. This includes:

• 5–10% revenue uplift through faster innovation and improved customer responsiveness
  
• 30–50% cost savings through task automation, reduced errors, and streamlined operations
  

And perhaps most importantly, agentic AI opens the door to new business models—from outcome-based contracts to performance-as-a-service platforms—transforming not just how companies work, but how they compete.

From Experimentation to Execution

The Gen AI paradox highlights a critical truth: technology alone doesn’t create value—strategy does.

Agentic AI represents not just a new wave of tools, but a new way of thinking about AI deployment. It demands that organizations move beyond experimentation for its own sake, and instead anchor their AI initiatives to:

1. Clear customer problems
   
2. Scalable business opportunities
   
3. Cross-functional implementation plans
   
4. A long-term vision for transformation
   

For companies ready to shift from fragmented pilots to coordinated, high-impact execution, agentic AI offers a roadmap.

Neodata’s Perspective

At Neodata, we see this moment not as a setback, but as a turning point. As organizations exit the hype phase of Gen AI, those who invest in agentic, goal-driven systems will gain a decisive edge—not just in productivity, but in innovation, resilience, and growth.

The path forward isn’t about abandoning AI. It’s about making it work—for your people, your processes, and your customers.

Because the purpose of any technology, ultimately, is to solve real problems—and that begins with intelligent design, not blind adoption.

This is where Neodata comes in. Our AI Enablement Framework is designed to guide companies from experimentation to impact. We combine deep expertise in data science with a modular, scalable approach to help companies identify and map high-value use cases, integrate AI into their operational workflows, and ensure that every solution aligns with strategic and business problems.

From training and data readiness assessment to the development of intelligent systems tailored to the business context, we help organisations integrate artificial intelligence where it matters most, achieving measurable results and long-term value. Contact us to find out more.