Editorial
Agentic AI has quickly emerged as one of the most significant developments in enterprise technology. While the long-term impacts may ultimately be the transformation of end-to-end customer experience, truly data-driven decision-making and entirely new business models, the near-term reality is more pragmatic: automating existing processes and capabilities. This shift brings enterprise architecture to the forefront of agentic AI adoption, as companies rethink how their systems and data need to work together.
Research from Dresner Advisory Services shows how rapidly this shift is unfolding. In late 2024, agentic AI emerged as a distinct category, and in just one year it has become a strategic priority for nearly every major software company.
Already, 68.5% of software providers have introduced agentic AI capabilities — a striking level of adoption that points to both the scale of opportunity and the urgency of competition.
Embedded vs. Bolted-On AI
Workday offers a compelling case study. Earlier this year, the company hired Peter Bailis as CTO. Bailis, previously VP of engineering at Google Cloud, led initiatives around conversational analytics, NL2SQL and retrieval-augmented generation (RAG) for structured data — practical breakthroughs in applied generative AI. Now he's responsible for technology strategy and architecture across the company’s suite of products and AI agents.
During a press conference, Bailis drew a sharp contrast between the promise of generative AI and the reality of enterprise adoption. Citing MIT and Stanford research, he noted that many GenAI initiatives remain anemic, with projects stalling as organizations struggle to go it alone. In contrast, agentic AI offers a clearer path to value because it is purpose-built for business execution.
Bailis argued that the most effective deployments come when agentic AI is embedded directly into existing corporate workflows, rather than building AI on the side. That is the winning strategy — and the foundation of enterprise research planning (ERP) of the future.
"Over the next 5 to 8 years, agentic AI will transform ERP from a static data storage into a dynamic, autonomous system that manages complete workflows, makes proactive decisions and learns from its outcomes," said John Van Decker, distinguished analyst at Dresner Advisory. "This change will increase automation, improve decision-making, speed up operations and offer a smarter user experience, enabling human workers to focus on strategic tasks instead of manual work."
Related Article: How AI Rebuilds the Modern Company: From Hierarchies to Hyperloops
Early Adopters Already See ROI
Bailis is bullish on agentic AI — and not just in theory. He said that early adopters, including his company, are already seeing measurable ROI. As he put it, they’re “drinking their own champagne.”
In fact, my conversation last year with Salesforce’s head of customer revealed similar results. Still, Salesforce’s announcement last week of major layoffs in that same function is a reminder that adoption isn’t without turbulence.
Use Case: Performance Reviews
What stands out most are the use cases Bailis shared, which bring agentic AI’s potential into focus. One example: employee performance reviews. By integrating Workday systems with Salesforce, AI agents can automatically pull relevant data and create an initial draft of annual reviews.
This goes a step beyond what Elisa Farri and Gabriele Rosani described in "Generative AI for Managers," where GenAI was positioned as a “co-thinker” for leaders.Here, the AI isn’t just brainstorming with the manager — it’s doing the heavy lifting of assembling a fact-based review. Employees benefit, says Bailis, because the process moves beyond “recency bias,” where recent events overshadow the full year’s contributions.
Use Case: HR Case Management
Another use case is HR case management. Employee requests that historically took days — or even weeks — can now be resolved far faster. I can speak from experience: last year, a simple employment verification across three employers dragged out for two weeks, regardless of company size. Agentic AI has the potential to make such inefficiencies a relic of the past.
Use Case: Finance
Finance is another area where the impact is profound. Bailis highlighted the quarterly close, long the bane of CFOs. Traditionally, closing the books meant exporting data from systems of record into sprawling, error-prone spreadsheets. The process was so consuming that many CFOs described themselves more as “bean counters” than as strategic partners to the CEO.
With agents coordinating tasks between people and systems, much of that manual, error-prone work disappears. The result: finance leaders can focus less on reconciliation and more on strategy.
For Bailis, these advances are not just incremental improvements — they are the foundation of the next-generation ERP. If early adopters are already seeing ROI, the question for the rest of the market isn’t whether to adopt agentic AI, but how quickly they can embed it into their core processes.
Related Article: AI Agents at Work: Inside Enterprise Deployments
Behind the Bots: The Data Fabric That Makes It Work
Mark Woollen, Workday’s senior VP for partner innovation, argued that low-code platforms powered by APIs are critical for delivering effective agents. His company is hardly new to this space, having long offered low-code capabilities. But agents require more than just configurable workflows; they demand robust data infrastructure. That’s why Workday is emphasizing the role of cloud data platforms, Apache Iceberg and Zero Copy architectures, which make it possible to access and share data seamlessly across systems without duplication.
To power its agents, the software company touts strategic partnerships with Snowflake, Databricks and others. The open question is whether partners will accelerate its push or if Workday will follow ServiceNow’s lead (acquiring Data.World earlier this year to bolster data catalog and governance capabilities).
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From this reviewer’s perspective, additional acquisitions in the catalog, data lakehouse and federated query markets seem inevitable. As agents shift ERP systems from records to action, the underlying data fabric and semantic layer becomes the true battleground.
The Race to Agentic ERP Is On
The early ROI signals of agentic AI are promising, but the larger transformation is still unfolding. The winners will be those who can marry data, architecture and execution to scale agents enterprise-wide. As with every generational change in enterprise technology, not every vendor — or enterprise — will make the leap. But Workday’s bet is clear: the future of ERP belongs to agentic AI, and the race is already underway.
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