At re:imagine 2025, Gartner’s Danielle Casey delivered a clear roadmap for product and technology leaders navigating the generative AI curve: not all use cases are created equal—and not all will succeed.
Drawing from hundreds of case studies across industries, the session broke down where GenAI is already delivering value, where it’s just beginning to show promise, and where adoption may never scale due to complexity, risk, or lack of ROI.
For product leaders, the takeaway was simple: If you’re not being deliberate about use-case strategy, you’re already falling behind.
The majority of current enterprise deployments fall into a tight band of feasibility:
Think content generation, summarization, retrieval, and surface-level customer interactions.
Gartner spotlighted:
The lesson? Start with simple use cases—but plan for scale.
Gartner identified three forces accelerating the next wave of enterprise AI:
Forget general-purpose LLMs. DSLMs are:
Example: A document LLM designed to understand complex financial documents by reading both the text and the document layout. It outperforms general AI models in tasks like contract analysis and compliance, helping teams work faster and more accurately.
DSLMs enable smaller, cost-effective models tailored for real-world business logic over general knowledge.
Gartner projects that by 2030, nearly every enterprise system will support multimodal interaction. That includes:
One example: a Canadian wealth management firm using GenAI to process and generate reports across text, tables, and charts—cutting report time by 80%. It expands automation potential by up to 50%, unlocking tasks that weren’t previously AI-compatible.
This is where automation becomes intelligent.
Gartner defines agentic AI by six traits—goal-setting, planning, autonomy, collaboration, reasoning, and adaptability. It’s a shift from “responding to inputs” to executing toward outcomes.
Example: an Australian water utility using three autonomous agents—managing water levels, optimizing energy usage, and scheduling pump maintenance—all operating with interdependent goals.
Gartner called out barriers that are slowing or stalling adoption:
Interoperability suffers when AI agents don’t speak the same language. Without common protocols, collaboration between specialized and general systems is difficult.
Organizations still struggle to tie GenAI to measurable outcomes. Many pilot programs look impressive, but fall short of proving sustained value or ROI.
Not every task fits a GenAI-first approach. For use cases requiring ultra-high accuracy (e.g., prediction, simulation, digital twins), hybrid models—rules-based, classical ML, neuro-symbolic AI—are still essential.
Gartner offered three actions to focus on now:
Look beyond volume. Are they delivering ROI—or just outputs?
Adopt platforms that balance automation with governance, observability, and model flexibility.
The message is clear: success in AI won’t come from isolated use cases—it will come from how intelligently and intentionally organizations build.
At Kore.ai, we’re aligned with Gartner’s vision and proud to support enterprise teams in deploying systems that are not just generative, but orchestrated, agentic, and ready for real-world complexity.
If you missed the keynote, now’s your chance to catch up.
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