Agentic AI Strategy - Where to Start — Building the Business Case for AI Agent Use Cases
- Beyond Team
- 3 days ago
- 3 min read
In almost every leadership workshop we run at Beyond: Putting Data to Work, there’s a moment when someone asks:“Okay - I understand the potential. But where do we actually start?”
It’s a fair question. AI agents sound transformational, but no CEO wants to greenlight a dozen pilots that never scale. What executives need is a clear business case: where ai agent use cases can add value, how to prove it quickly, and how to build from there.
Why a Business Case Matters
Too many organisations fall into one of two traps:
Paralysis — waiting for the “perfect” enterprise-wide strategy before doing anything.
Experimentation overload — spinning up lots of pilots without ever proving sustained value.
The right path sits in between. A strong business case aligns AI agents to measurable business outcomes while managing risk and building confidence for scale.
Step 1: Identify the Right Starting Point
In our experience, the best early use cases share three characteristics:
High repetition: Tasks that occur frequently (e.g., invoice reconciliation, helpdesk triage).
Cross-functional pain: Processes that cut across silos, where manual hand-offs create friction (e.g., customer onboarding, order fulfilment).
Manageable risk: Areas where mistakes won’t cause regulatory or reputational damage (e.g., internal IT operations vs customer financial advice).
Some examples we’re seeing executives prioritise:
IT agents that monitor systems and automatically fix simple errors.
Supply chain agents that track inventory and trigger replenishment.
Marketing agents that execute routine campaign actions once strategy is set.
Step 2: Define What Value Looks Like
A CFO I spoke with put it bluntly: “I don’t care how clever it is - show me the numbers.”
That means defining metrics up front:
Cost savings (reduced manual hours).
Speed (time to resolution, time to market).
Customer satisfaction (fewer complaints, faster responses).
Risk reduction (fewer errors, improved compliance reporting).
Without these, it’s impossible to prove whether agents are creating real value.
Step 3: Build Guardrails
No CEO wants to read about their company in the papers because an agent acted without control. That’s why the business case must also include:
Governance frameworks: when agents can act, when they must escalate.
Monitoring and reporting: dashboards to track what agents are doing in real time.
Accountability: clear ownership — who is responsible if an agent misfires.
These controls aren’t “nice to have.” They’re part of the case for adoption.
Step 4: Think Portfolio, Not One-Off
One of the most common mistakes we see? Leaders betting too heavily on a single use case.
At Beyond, we encourage organisations to think in portfolios:
Start with one or two quick wins to prove value.
Add a second tier of higher-value, higher-risk pilots once trust is built.
Create a roadmap for scaling agents into core processes once the foundations are proven.
This approach keeps momentum while ensuring lessons are learned along the way.
Real Challenges We’re Seeing
Even with a good business case, leaders often stumble at the same points:
Underestimating integration: agents need access to multiple systems — which is rarely plug-and-play.
Ignoring data quality: if the data is inconsistent, the agent’s actions will be too.
Failing to plan for people: employees need clarity — are agents replacing, augmenting, or supporting their work?
These challenges don’t mean you shouldn’t start. They mean you should start with eyes open, and with a plan that tackles both the opportunities and the obstacles.
Final Thoughts on AI Agent Use Cases
The business case for AI agents isn’t about hype. It’s about showing measurable outcomes in targeted areas, then scaling responsibly.
At Beyond: Putting Data to Work, we’ve seen that the most successful leaders follow the same pattern: start small, define value clearly, put guardrails in place, and expand through a portfolio approach.
That’s what an Agentic AI Strategy really means: not just deploying agents, but building the case — step by step — for agents that deliver sustainable, enterprise-wide value.
