From overlooked shifts in model strategy to the rise of MLOps and regulatory tailwinds, this is a firsthand account of how the AI landscape is evolving, what most are getting wrong, and where the next real breakthroughs are likely to come from.



When I was starting out in 2021, AI—especially large language models—wasn’t mainstream. It felt experimental, niche, and in many ways, still academic.

Fast forward to 2023, and over $50 billion was invested globally into generative AI. But here’s what’s often left out of that headline: less than 10% of enterprise deployments made it to production with measurable ROI.

That disconnect has been hard to ignore.

Over the past 12–18 months, I’ve found myself questioning many of the default assumptions about where this so-called age of artificial intelligence is actually heading. Beneath all the hype, I’ve noticed quieter—but more meaningful—shifts unfolding. Especially in how enterprises are choosing to build, deploy, and govern their AI systems.

Some of these changes have genuinely surprised me. Others have challenged long-held beliefs I used to share. And all of them have shaped how I now think about the future of this space—not as an observer, but as a builder in the thick of it.    

In this piece, with Windrose Capital, I’m sharing what’s shifted, what’s misunderstood, and where I believe the next real wave of AI value is going to emerge.

What Surprised Me Most in the Last Few Months    

If you’d asked me 18 months ago what the next big thing in AI would be, I probably would’ve echoed what the entire ecosystem was chasing larger models, bigger benchmarks, more generative magic.   

But what actually played out on the ground was different.

Working closely with enterprises, experimenting with deployment architectures, and sitting in on late-night troubleshooting calls—what surprised me most was not what was being built, but what was quietly starting to work better.

It forced us at Data Neuron to re-evaluate a few assumptions we once held as obvious truths.

 

I’ll get into all these specifics in the next section—but this shift changed the way I approach product thinking, customer value, and the kind of AI systems we choose to invest our time building.


Trends Quietly Winning in AI Right Now


• Smaller Models Are Making Big Impact   


I've observed that, while large language models (LLMs) have dominated headlines, it's the smaller, specialized models that are making substantial impacts in enterprise applications.     

A notable example is OpenAI's GPT-4o Mini Model, introduced in July 2024. This model is designed for cost-efficiency and speed, offering a balance between performance and resource utilization. Remarkably, GPT-4o Mini is over 60% cheaper than its predecessor, GPT-3.5 Turbo, making it an attractive option for businesses seeking to integrate AI without incurring prohibitive costs.

Performance-wise, GPT-4o Mini doesn't compromise. It achieved an impressive 87.2% on the HumanEval benchmark, which assesses coding capabilities, outperforming competitors like Gemini Flash and Claude Haiku. This level of proficiency demonstrates that smaller models can deliver high-quality results in specific tasks.

At DataNeuron, we've observed similar trends. For instance, in a project with a healthcare data platform, we replaced a general-purpose LLM with a domain-specific model tailored to medical data. The outcome was a reduction in costs and improved accuracy in patient report classifications.

These examples underscore a broader industry movement towards models that are not only efficient but also finely tuned to specific domains.

The focus is shifting from creating ever-larger models to developing smarter, more specialized systems that deliver objective specific tangible value in real-world applications.

 

• AI Engineering Is Overtaking AI Research   

 

Another big shift I’ve seen lately isn’t about algorithms—it’s about what happens after.

In the rush to build flashy AI prototypes, a lot of teams—especially in traditional enterprises—are now grappling with something we internally call “AI debt.”

These are models that worked in demo environments but became headaches once deployed: no proper monitoring, no feedback loops, no way to debug why the results changed last week.

And I get it—when we started at DataNeuron, we were equally drawn to pushing the boundaries of what was possible. But over time, it became painfully clear: building AI that’s useful is very different from building AI that lasts. 

Today, the hard problems aren’t just about model performance—they’re about governance, scalability, and maintenance and building the infrastructure and discipline to keep it smart—and safe—at scale.

This is where AI engineering comes in.

The adoption of platforms like MLflow, Weights & Biases, and Kubeflow is rising — not because it’s trendy, but because without them, things break fast. These platforms help track experiments, version data and models, and enforce reproducibility. In production environments, that’s non-negotiable.


• Regulation Is Accelerating Adoption, Not Slowing It Down

This was one opposite to almost all pre-conceived theories.


For a long time, “AI regulation” sounded like a roadblock, but it has flipped that idea on its head.

India’s approach has been more principle-based than punitive. The Digital Personal Data Protection Act (DPDP), passed in 2023, laid out a privacy-first framework, but without stifling experimentation. What it did, though, was push enterprises—especially in BFSI and healthcare—to take explainability and data governance seriously. And that has created room for more confident AI adoption.

At DataNeuron, we’ve worked with clients who were previously hesitant to deploy machine learning due to compliance concerns. Post-DPDP, their internal audit and legal teams now see structured guardrails, which ironically makes deploying AI less risky, not more. 

Globally, you’re seeing this shift play out even more visibly. The EU AI Act, which classifies AI systems by risk and requires transparency for high-risk use cases, has already led to a spike in demand for governance tools. 

But in India, the shift feels more foundational—regulation is helping businesses go from AI prototypes to production-grade systems.



What Most Get Wrong About AI Adoption

 

If there’s one thing I’ve learned from working with enterprise clients, it’s this: adopting AI isn’t just a tech upgrade—it’s a mindset shift. And that’s where many go wrong.

 

1: Thinking AI is Plug-and-Play

Too many teams treat AI like an app you can install. But in reality, it’s a full-stack transformation. From data pipelines and model logic to compliance workflows and team training—it touches everything.

When companies skip the foundational plumbing, they end up with flashy demos and zero outcomes. This isn’t just anecdotal. According to IBM’s 2023 Global AI Adoption Index, 28% of businesses cited a lack of tools or platforms for developing AI models, and 27% said their AI projects were too complex or difficult to integrate and scale. That’s nearly one in three organizations struggling with the basics—before even getting to outcomes.


2: Chasing PoC Wins Without Thinking Long-Term

Proof-of-concepts are tempting. They’re fast, measurable, and often impressive. But I’ve seen too many projects succeed in a sandbox, only to collapse at scale. Gartner predicts that by the end of 2025, at least 30% of generative AI projects will be abandoned after the PoC stage due to factors like poor data quality and unclear business value.

Why? Because governance, monitoring, and post-deployment iteration weren’t part of the plan. AI that can’t be maintained doesn’t stay useful for long.

3: Trusting Black Boxes Blindly 

Some AI models are brilliant—but also opaque. When things go wrong, teams are left scrambling. Without fallback logic, human override, or explainability, trust erodes fast.

In regulated industries, that’s not just a mistake—it’s a liability.

And the numbers echo this. A Capgemini Research Institute report (2023) found that 51% of organizations cite lack of clarity on the underlying data used to train generative AI as a key challenge. Meanwhile, a 2024 Deloitte survey revealed that explainability of outputs and worker mistrust are among the most common barriers to AI adoption.

If people don’t understand how decisions are made, they won't trust the systems—no matter how advanced the tech behind them.

 

Where I Believe AI Is Headed

 

We’ve moved beyond the race for bigger models. The next frontier lies in building systems that are smarter by design, not just larger in size.

I see three clear directions emerging:


1. Modular, Workflow-Aware AI

Instead of generic tools, we’ll see AI systems tailored for specific industry contexts—contract analysis in law, diagnostic support in healthcare, anomaly detection in supply chains. These systems won’t just perform tasks—they’ll embed deeply into enterprise processes, with plug-and-play modularity that allows businesses to scale what works and replace what doesn’t.

2. Synthetic Data Will Power the Next Wave

As access to real-world data becomes constrained—due to regulation, privacy, or fragmentation—synthetic data will shift from experimental to essential. In fields like finance, insurance, and clinical trials, synthetic datasets will unlock safe, scalable training pipelines that are otherwise impossible to build.

3. AI Will Co-Create, Not Just Predict

The future won’t be about pattern recognition alone. AI systems will start proposing novel hypotheses—whether in materials science, drug discovery, or climate modeling. The boundaries between research labs and AI companies will blur, giving rise to a new era of augmented scientific exploration.

 

It’s not the models that will define the future—it’s the architecture, the trust, and the real-world use cases they’re built around.

 

The DataNeuron Philosophy    

  

We’ve always believed that the real value of AI doesn’t come from the model alone—it comes from the entire pipeline: how data is collected, curated, monitored, and ultimately put to work.   

At DataNeuron, our approach is rooted in this belief. We don’t just chase hype cycles or benchmark scores. Instead, we focus on building systems that can deliver measurable, repeatable impact—especially in complex enterprise environments.    

We’ve seen firsthand how small improvements in data quality or model explainability can have outsized effects on business outcomes. So we’ve built our tools to prioritize three things: transparency, adaptability, and operational fit.   

We’re not here to build “magic” black-box solutions. We’re here to help teams build reliable, auditable AI systems that users can trust—and leaders can justify.

 

Final Takeaway: Building the Right Kind of Intelligence  

 

We’ve reached a point where it’s no longer enough to ask, “Can we build this model?”

The more important question is: “Does this model align with how humans make decisions, trust outcomes, and create value?”

In my view, the future of AI isn’t about building artificial intelligence—it’s about building aligned intelligence.

Intelligence that adapts to the real-world messiness of enterprise environments. Intelligence that understands the trade-offs between accuracy and explainability, automation and accountability. 

Intelligence that doesn’t just solve problems, but earns trust. And building that kind of intelligence will take more than just code. It’ll take better questions, deeper thinking, and systems that are designed as much for people as they are for machines.