Make the case for agentic AI in your enterprise

Agentic AI offers autonomous, goal-seeking systems capable of executing tasks and making decisions on behalf of humans. The upside for operations is clear—enabling enterprises to automate complex, multi-step workflows with minimal human oversight—driving faster decision-making, lower operational costs, and significantly higher productivity across functions. But how to cut through the hype and make meaningful progress? This paper offers two simple frameworks: one to help you decide on suitable use cases and another mapping the route to the widespread application of agentic throughout the enterprise.

1.   A-G-E-N-T: Your framework to identify suitable use cases

Agentic will not always be the answer. Autonomous multi-step reasoning, the power at the heart of agentic capability is expensive—as much as 150x more resource-intensive than traditional automation.

Enterprise leaders need a structured way to assess whether a use case is agent-ready. In other words, could it be a suitable use of agentic capabilities?  We have identified five challenges to help enterprises make that initial call.

Exhibit 1: The ‘A-G-E-N-T’ decision framework

Source: HFS Research, 2025

Identifying suitable use cases primes the pump. Next, you must set it running.

2. The five pillars supporting scalable agentic AI

Armed with suitable use cases, agentic success throughout the enterprise relies on five pillars to support a scalable, governable agent ecosystem (see Exhibit 2).

Exhibit 2:  Focus on business value, trust and control, context-awareness, adoption, and repeatable delivery to succeed at scale

Source: HFS Research, 2025

The five pillars of agentic progression

1. A-G-E-N-T use cases must be focused on business outcomes

Don’t start with the tech. Start with the problem. Agentic AI pilots that begin with ‘let’s see what it can do’ will fail fast. No one wants a repeat of the POC and pilot purgatory that has plagued the adoption of GenAI (read more in our report, ‘Break free from GenAI pilot purgatory’). So, ensure you are targeting friction-rich, outcome-driven use cases where the benefits of autonomous decision-making are measurable and material.

• To ensure value outcomes, prioritize tasks with decision latency, high-volume manual drudgery, or excessive handoffs.
• Set outcome metrics up front: time saved, reduced cycle time, and improved accuracy.

2. Trust and control by design

Agents acting on behalf of humans create accountability gaps. Enterprises require embedded guardrails and explainability. To solve this:

• Implement bounded autonomy—define task limits per agent.
• Embed human-in-the-loop (HITL) checkpoints at key control stages.
• Enable observability and reversibility: full logs, digital signatures, and rollbacks.

This is how trust gets engineered—one successful, explainable decision at a time.

3. Intelligent data enablement

Most enterprises don’t have the contextual foundation agents need. That must change. Agents need semantic context, not just access to data lakes.

• Invest in knowledge orchestration: taxonomies, ontologies, and graphs.
• Enable real-time access via vector databases and RAG pipelines.
• Accept that some unstructured data is usable as-is—cleaning everything is a trap. Your data will never be perfect.

Remember: Agents aren’t just consumers of data—they can help surface valuable insights from fragmented knowledge.

4. Seamless integration where people work

The arrival of agentic AI should make you rethink how work gets done. Processes will likely need re-engineering to take advantage of the support agentic offers. These new workflows must place agents at the heart of the work your people do – complete with frictionless interfaces. This will drive both user adoption and business impact.

• Rethink and reshape processes with agents woven in.
• Co-pilots should feel like assistants, not additional systems outside your workflow.
• Avoid building parallel ecosystems—drive impact through augmentation rather than replacement.

This is your opportunity to re-engineer business processes around human-AI collaboration.

5. Scalable delivery

Currently, building agents means duct-taping LangChain to OpenAI with some brittle API glue (for example). That’s not a platform—it’s a science project. Enterprises need composability and reuse, not custom-built spaghetti. Anthropic’s recent development of Model Context Protocol (MCP) is being eagerly embraced by engineers for precisely this reason. You can learn more about it in our article, ‘Come to grips with MCP to succeed with agentic AI’.

Enterprises need reusable, composable agent frameworks that support governance, templating, and integration.

• Push for modular architectures with pre-built agent templates.
• Explore services-as-agents (Services-as-Software) or outcome-based pricing.
• Treat agents like a digital workforce: managed, monitored, and upgraded over time.

3. From kick-off to scale: Your five-step playbook

The Bottom Line: Build agentic AI with intent, to redefine how work gets done.

Agentic AI is not another feature to add—it’s a foundational capability around which to rebuild an enterprise. But you won’t get there through opportunistic pilots and fragmented tooling. The enterprises that win will be those treating this as operating model transformation, not AI experimentation.

Start with high-value, autonomy-appropriate use cases. Build trust through guardrails. Industrialize with repeatable and reusable composability. You must redefine how work gets done with Agentic AI, and build for that with conviction and intent.