80% of GenAI Apps on Data Platforms by 2028: Gartner

Imagine a world where nearly every business application powered by generative AI (GenAI) is not built from scratch, but rather emerges seamlessly from the digital bedrock companies already rely on. That’s not the distant future—it’s a reality that’s rapidly taking shape according to Gartner’s latest forecast. By 2028, a staggering 80% of GenAI business applications will be developed on existing data management platforms, signaling a monumental shift in how enterprises deploy artificial intelligence[1]. This isn’t just about saving time or cutting costs—although that’s certainly part of it—it’s about unlocking the latent value in the data that organizations already own. And, as someone who’s followed AI’s evolution for years, I can tell you: this is a game-changer.

To understand why this matters, let’s rewind a bit. Generative AI burst into the mainstream consciousness with the launch of ChatGPT in late 2022, but its roots go deep. For decades, companies have invested billions in building robust data infrastructure—ERP systems, CRM platforms, data lakes, and analytics tools. All along, these were seen as the backbone for better decision-making and operational efficiency. But now, with GenAI, they’re poised to become the launchpad for a new wave of innovation.

The Data-Driven Foundation of GenAI

The idea that most GenAI applications will be built on existing data platforms is both practical and profound. Gartner’s prediction highlights a fundamental truth: enterprises are sitting on mountains of data, and GenAI offers the tools to mine that data for unprecedented value[1]. This approach isn’t just about efficiency—it’s about sustainability. Why reinvent the wheel when you can leverage what you already have?

Let’s face it: building new AI infrastructure from the ground up is expensive, complex, and time-consuming. By contrast, integrating GenAI with existing data platforms allows organizations to accelerate deployment, reduce risk, and focus on solving business problems rather than wrestling with technology. As someone who’s seen too many “AI projects” get bogged down in technical debt, this shift feels like a breath of fresh air.

Why Existing Data Platforms Matter

There are several compelling reasons why enterprises are turning to their current data management systems as the foundation for GenAI:

• Cost Efficiency: Developing new platforms is costly. Leveraging existing infrastructure saves both time and money.
• Data Quality and Governance: Organizations already have processes in place for data quality, security, and compliance. Integrating GenAI here ensures these standards are maintained.
• Faster Time-to-Market: By building on what’s already in place, companies can deploy GenAI solutions more quickly and scale them more effectively.
• Seamless Integration: Existing platforms are already connected to critical business processes, making it easier to embed GenAI capabilities where they’re needed most.

Gartner’s research underscores that this isn’t just a trend—it’s a strategic imperative. “By 2028, 80% of GenAI business applications will be developed on existing data management platforms,” the firm states, and I’m thinking that this is where the rubber meets the road for digital transformation[1].

The GenAI Landscape: Players and Platforms

The GenAI market is bustling with activity. Beyond the headline-grabbing giants—Microsoft, Google, Amazon, and IBM—there’s a vibrant ecosystem of startups, open-source projects, and specialized providers[5]. Microsoft and OpenAI are at the forefront, embedding GenAI into products like Copilot and Azure AI. Amazon has partnered with Hugging Face, a leader in open-source large language models (LLMs), and offers GenAI via AWS Bedrock. Google is integrating GenAI into its cloud and productivity tools, while IBM continues to push the envelope with Watson and enterprise AI solutions[5].

Let’s not forget the rise of domain-specific models. By 2027, Gartner predicts that more than 50% of GenAI models used by enterprises will be tailored to specific industries or business functions—up from just 1% in 2023[3]. That means we’re moving beyond generic chatbots and content generators to AI that truly understands the nuances of healthcare, finance, manufacturing, and more.

Real-World Applications and Success Stories

So, what does this look like in practice? Consider a global retailer that uses its existing customer data platform to power a GenAI-driven personalization engine. Instead of building a new system, they integrate GenAI to analyze purchase history, browsing behavior, and social sentiment, delivering hyper-personalized recommendations in real time.

Or take a financial services firm that leverages its data lake to train a GenAI model for fraud detection. By using the data they already collect, they’re able to spot anomalies faster and with greater accuracy—all while maintaining strict compliance with regulatory requirements.

Healthcare is another sector where this approach is taking off. Hospitals and clinics are embedding GenAI into their electronic health record (EHR) systems, enabling clinicians to generate patient summaries, suggest treatment plans, and even predict health risks based on historical data.

GenAI and the Developer Experience

The impact of GenAI isn’t limited to end users—it’s transforming the way software is built. Gartner predicts that by 2028, 75% of enterprise software engineers will use AI code assistants, up from less than 10% in early 2023[4]. These tools aren’t just about generating code—they’re collaborative assistants that improve efficiency, spark creativity, and help developers upskill across frameworks.

“Software engineering leaders must determine ROI and build a business case as they scale their rollouts of AI code assistants,” says Philip Walsh, Sr Principal Analyst at Gartner. “However, traditional ROI frameworks steer engineering leaders toward metrics centered on cost reduction. This narrow perspective fails to capture the full value of AI code assistants.”[4]

By the way, this shift isn’t just about productivity. It’s about job satisfaction, retention, and the ability to tackle increasingly complex challenges. As someone who’s talked to developers about these tools, the enthusiasm is palpable.

Sustainability and the Future of GenAI

As GenAI adoption grows, so does the need for sustainable practices. By 2028, Gartner expects that 30% of GenAI implementations will be optimized using energy-conserving computational methods, driven by the push for greener technology[3]. This is a critical development, given the enormous energy demands of training and running large AI models.

Companies are being urged to diversify their suppliers, adopt composable architectures, and use renewable energy sources to reduce their carbon footprint. The message is clear: the future of GenAI isn’t just about what it can do, but how it’s done.

Comparing Major GenAI Platforms

To help make sense of the crowded GenAI landscape, here’s a quick comparison of leading platforms and their key features:

The Road Ahead: Challenges and Opportunities

Of course, this transition isn’t without its challenges. Data silos, legacy systems, and concerns about privacy and bias remain significant hurdles. But the opportunities are vast. By building GenAI on existing data platforms, companies can unlock new levels of agility, insight, and innovation.

Looking forward, the lines between data management and AI will continue to blur. We’re entering an era where every enterprise application will be, in some sense, an AI application. The question isn’t whether to adopt GenAI—it’s how to do it in a way that maximizes value and minimizes disruption.

Conclusion: A New Era for Enterprise AI

As we stand on the cusp of this transformation, it’s clear that the future of GenAI is deeply intertwined with the data platforms that companies have spent years—and billions—building. By 2028, the vast majority of GenAI business applications will be built on these very foundations[1]. This isn’t just a technical shift; it’s a strategic one, reshaping how organizations innovate, compete, and deliver value to their customers.

In the words of Gartner, the message is clear: “80% of GenAI business applications will be developed on existing data management platforms by 2028.” For enterprises that want to stay ahead, the time to act is now.

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