10-minute read

Contents

• What are AI agents and how do they work?
• Real-world agentic AI examples in business
• How to set up agentic AI governance from day one
• Agentic AI risks to address
• How to build your agentic AI roadmap
• Where should you start with agentic AI in your business?

Where should you start with agentic AI in your business? Start with a focused use case that addresses a real operational pain point, build governance into the foundation from day one, and adopt a phased approach: Plan, Architect, Operate.

The companies I work with day in and day out – healthcare, education, public sector – are all grappling with this. And here’s the reality: over 40% of agentic AI projects will be canceled by leadership by the end of 2027 (Gartner) due to escalating costs, unclear business value, or inadequate risk controls. The biggest mistake I see? Teams jumping straight to technology without understanding where agents fit in their workflows. But identifying business value is only part of it. You also need an operating model that supports successful enablement. Diversified Energy is a standout example: they invested in a VP of AI, established a council, built an intake process, and secured business support before rallying around their first use case.

This isn’t about chasing the latest trend. This is about implementing AI thoughtfully, with a clear path from pilot to production. The shift is happening now: 40% of enterprise apps will feature task-specific AI agents by 2026, up from less than 5% in 2025 (Gartner). The question isn’t whether you’ll adopt agentic AI. It’s how to do it without becoming one of the 40% that fail.

What are AI agents and how do they work?

The easiest way to understand AI agents is to look at how they differ from traditional automation and chatbots:

Traditional automation follows rigid, pre-programmed rules. If X happens, do Y. No reasoning, no adaptation.

Chatbots rely on pattern recognition and rule-based or retrieval-based systems to generate responses. Their functionality is generally limited to answering queries rather than executing tasks or initiating actions.

AI agents plan, reason, take action, and learn, but they operate with human oversight. They don’t just respond to prompts: they prepare and execute multi-step workflows within defined boundaries, surfacing decisions for review when needed.

Think of it as a shift from “decision support” to “decision preparation and assisted execution.” A chatbot might tell you which invoices are overdue. An AI agent reviews those invoices, drafts follow-up emails, and prepares CRM updates, then routes everything to you for approval before anything is sent.

Or take something every organization deals with: RFPs. An intake agent receives and categorizes incoming requests. A review agent compares requirements against prior submissions. A drafting agent assembles a first-pass response. Each agent handles its step, surfaces decisions for review, and hands off to the next. That’s agentic AI applied through a process lens.

The technical foundation typically includes:

• Large language models (LLMs) for reasoning and natural language understanding
• Standardized tool and context interfaces (e.g., Model Context Protocol) to enable scalable, modular integration with APIs, databases, and applications
• Memory systems to maintain context across interactions
• Orchestration layers to coordinate multi-step workflows
• Guardrails to keep agents operating within defined boundaries

People think AI is hard because the models are complex. The models aren’t the hard part. What’s hard is stitching together agents, prompts, model access, security, and data into something that runs reliably.

Real-world agentic AI examples in business

Let’s move from theory to practice. These are examples I keep coming back to with clients – because they show what thoughtful implementation looks like.

Healthcare: Transforming clinical workflows

The Georgia Department of Behavioral Health and Developmental Disabilities (GA-DBHDD) faced a common challenge in healthcare: clinical evaluators were spending 8-12 hours per risk assessment, manually reviewing and summarizing unstructured data across multiple systems. With over 300 assessments annually, the workload was unsustainable.

The solution was an AI platform that consolidates up to 20 documents per patient across EHR systems and SharePoint and generates first-draft summaries (case study). Clinicians review and refine rather than starting from scratch.

The results:

• 4+ hours saved per assessment
• 1,200+ hours saved annually
• $100,000+ projected annual cost reduction
• Clinicians freed to focus on direct patient care

“The partnership with Quisitive and Microsoft has been instrumental in helping us take a thoughtful, secure, and measurable approach to AI adoption,” said Jason McSwain, Deputy Assistant Commissioner of Operations and CIO.

This is the kind of implementation that gets me excited. Smart technology, real impact – clinicians get time back, citizens get faster access to care.

Public sector: 24/7 citizen services

A county animal services department was drowning in call volume. Residents searching for lost pets, exploring adoption options, and seeking animal welfare assistance faced long wait times. Staff were pulled away from urgent cases requiring hands-on care.

The solution was an AI chatbot as a digital front door – available 24/7 with GIS integration for jurisdiction-specific responses (case study). Lost-and-found, licensing, adoptions, and welfare guidance – all handled without waiting on hold.

The technology supports the mission by making the path to help faster and more compassionate. Residents get answers faster. Staff focus on the animals and situations that need human attention.

Cross-industry: Rethinking the RFP process

RFPs are a universal pain point. Every organization deals with them, and the process is almost always manual, repetitive, and slow.

An agentic approach breaks the RFP workflow into coordinated steps: an intake agent categorizes incoming requests, a review agent compares requirements against prior submissions and highlights key differences, and a drafting agent assembles a first-pass response using relevant past content. Each agent operates within defined boundaries, surfacing decisions for human review at critical points.

This is what it looks like to explore agents from a process lens rather than a technology lens. Microsoft ratifies this approach: start with the workflow, identify where agents add value, then build.

AI success stories like this are starting to pop up everywhere

These aren’t isolated examples. McKinsey is seeing this pattern across industries – 23% of organizations are now scaling agentic AI systems, with an additional 39% experimenting (McKinsey). The adoption curve is steep, but so is the learning curve.

How to set up agentic AI governance from day one

Don’t treat AI like a side project. It’s a system now. And systems need architecture.

The organizations that succeed with agentic AI build governance into the foundation. Those that struggle bolt it on later, after agents have already proliferated across the enterprise in ways nobody fully understands.

And right now, almost nobody has this figured out. Only 44% of organizations have formal AI agent policies. Comprehensive agent identity management? 0%, according to Microsoft’s 2024 research.

The seven pillars of agentic AI governance

Agentic AI governance isn’t one thing – it’s use cases, identity, data, security, and more. At Quisitive, we focus on seven interconnected areas:

1. Agentic AI use cases: Which processes are right for automation? Where does human judgment remain essential?
2. Identity and access management: Agents need identities just like employees. Who can an agent impersonate? What systems can it access? How do you revoke access when needed?
3. Applications: Which business applications will agents interact with? How do you manage integrations securely?
4. Data and information protection: What data can agents access? How do you prevent sensitive information from leaking through agent actions?
5. Security: How do you detect and respond to agents behaving unexpectedly? What’s your incident response plan?
6. Risk and compliance: How do agent actions map to regulatory requirements? Healthcare, financial services, and public sector all have specific constraints.
7. Agentic AI governance: Who owns the AI strategy? How are decisions made about deploying new agents? What’s the review process?

You don’t need all seven to start

You don’t need to solve all seven pillars before launching your first agent. But you do need:

• Access controls defining what each agent can and cannot do
• Audit trails capturing every action for review
• Human oversight points for high-stakes decisions
• Clear ownership of each agent and its behavior

Supervising every action defeats the purpose. The goal is to set clear boundaries so agents can act confidently within them, with human oversight where it matters most.

Agentic AI risks to address

Let’s be honest about what can go wrong. Too much AI content is pure hype. The reality is messier.

The reality gap

Roughly 80% of organizations have deployed generative AI, but approximately the same percentage report no material impact on earnings (McKinsey). That’s the gen AI paradox. Lots of activity, not much value.

Why? Three reasons keep coming up:

Cost escalation. Projects balloon without clear boundaries. What starts as a focused pilot becomes a sprawling initiative with unclear ROI.

Unclear business value. Teams deploy AI because they can, not because they’ve identified a specific problem worth solving.

Inadequate risk controls. Security, governance, and compliance are afterthoughts. Then something breaks.

Security risks are real

Unauthorized access. Sharing inappropriate data. Taking actions outside their intended scope. This isn’t theoretical. It’s happening now.

The key risks to address:

• Identity misuse: Agents acting with permissions they shouldn’t have
• Data exfiltration: Sensitive information leaving the organization through agent actions
• Workflow corruption: Agents making decisions that cascade into broader system failures
• Regulatory non-compliance: Agent behavior violating industry-specific requirements

The agent washing problem

Agent washing is everywhere – vendors rebranding existing products with AI terminology. Only about 130 of thousands of agentic AI vendors are actually “real” (Gartner).

How do you tell the difference? Real agentic platforms handle multi-step reasoning, tool orchestration, memory persistence, and governance. If a vendor can’t explain how their agents reason through complex tasks, you’re probably looking at a chatbot with a new label.

There’s a related problem: agent sprawl. As excitement grows, teams across the organization start building their own agents without coordination. Each one adds technical debt: inconsistent governance, duplicated data connections, conflicting workflows. Left unchecked, agent sprawl creates the same mess that shadow IT did a decade ago, except now the ungoverned tools can take actions, not just store data.

We’re duct taping LLMs into our workflows and hoping nothing breaks. That’s the current state for most organizations. The ones that succeed are the ones that acknowledge this honestly and build the architecture to do it right.

How to build your agentic AI roadmap

Here’s the framework we use at Quisitive: Plan, Architect, Operate.

Phase 1: Plan

This is where most organizations want to skip ahead. Don’t. A structured planning phase prevents the 40% failure rate.

1. Current state assessment
   • Inventory existing AI initiatives (including shadow AI)
   • Map business processes to automation potential
   • Assess data readiness and quality
   • Evaluate technical infrastructure
2. Future state vision and gap analysis
   • Define target operating model for AI
   • Identify skill gaps and organizational changes needed
   • Prioritize use cases by value and feasibility
3. Prioritization and roadmap
   • Sequence initiatives based on dependencies
   • Define success metrics for each phase
   • Allocate resources and establish governance
4. Implementation playbook
   • Document architecture decisions
   • Create deployment templates
   • Establish monitoring and feedback loops

Phase 2: Architect

This is where Quisitive’s AI operations team uses the Airo AI Accelerator to deploy, govern, and scale agentic AI.

The goal: pilot to production in weeks, not months.

• No-code to pro-code support for different skill levels
• Built-in governance and security controls
• Multi-model access through Azure AI Foundry
• RAG architecture for document and data integration

What we’ve seen: 50% faster time to production and 30% reduction in AI project costs when organizations follow a structured approach versus ad-hoc experimentation.

Phase 3: Operate

Launch isn’t the finish line. It’s the starting point for continuous improvement.

• Monitor agent performance and behavior
• Gather user feedback and iterate
• Expand successful agents to new use cases
• Sunset what doesn’t work

The animal services deployment we mentioned earlier? It started with a focused scope: lost-and-found inquiries. Once that worked reliably, the team expanded to licensing, adoptions, and welfare programs. Start small. Prove value. Scale what works.

Where should you start with agentic AI in your business?

Focus on a real operational pain point, build governance into the foundation from day one, and adopt a phased approach: Plan, Architect, Operate.

Key takeaways:

• AI agents plan, reason, and act – they prepare and execute multi-step workflows, not just answer questions.
• 40% of enterprise apps will feature AI agents by 2026. The opportunity window is now.
• The organizations that succeed start with a real problem, governance, and a phased approach – not technology.
• Governance from day one lets agents act confidently within clear boundaries.
• A structured roadmap (Plan → Architect → Operate) beats random pilots every time.

Clinicians getting hours back to spend with patients. Residents finding answers at 2am. Smart technology, real impact – that’s why I do this work.

Ready to take the first step? AI Design Labs offers a structured starting point to assess your readiness, identify high-value use cases, and build your agentic AI roadmap.