Agentic AI Cheat Sheet for First-Time CAIOs – Do’s & Don’ts.

Read time: 2.5 minutes

1. Solve a Real Problem:

• Do: Pick one business problem with a measurable impact and focus on the outcome.

• Don’t: Launch AI projects just because everyone else is doing it.

• Example: Auto-prioritize customer support tickets so teams handle the most urgent issues first.

• Why it matters: 30% of generative AI projects will be abandoned due to unclear business value (Technology Magazine, 2024).

2. Audit Your Data Thoroughly:

• Do: lean, standardize, and structure the data you plan to use before deploying AI.

• Don’t: Assume AI can fix messy or incomplete data.

• Example: Standardize customer logs with timestamps, categories, and resolution details so the model learns from reliable patterns.

• Why it matters: Poor data quality costs ~$15M per year (Forbes, 2021).

3. Start Small, Prove Value:

• Do: Begin with a single workflow, measure results, and scale from there.

• Don’t: Attempt to automate your entire business in one shot.

• Example: Start with automating invoice approvals and expand once ROI is clear.

• Why it matters: Lenovo’s engineering teams saw up to a 15% boost in code quality and speed after adopting AI agents (Observer, 2025).

4. Build a Cross-Functional Team:

• Do: Bring together engineers, data scientists, product owners, and business leaders with shared KPIs.

• Don’t: Expect a single AI expert to manage enterprise-level transformation.

• Example: Form a team consisting of an engineer, a data scientist, and an operations lead to run one pilot effectively.

• Why it matters: C-suite-led cross- functional collaboration can raise success rates by ~60% (American Healthcare Leader, 2025).

5. Pick Tools That Actually Work:

• Do: Choose open and flexible AI tools that integrate easily with existing systems.

• Don’t: Buy expensive black box solutions that are difficult to implement.

• Example: Utilize modular APIs to automate workflows, rather than relying on rigid enterprise tools.

• Why it matters: Open-source AI drives higher ROI than proprietary systems (IT Pro, 2025).

6. Measure, Monitor, Adjust:

• Do: Track performance metrics like accuracy, adoption, and time saved, and adjust regularly.

• Don’t: Depend on vanity metrics or assume adoption will happen automatically.

• Example: Monitor escalation rates and resolution accuracy on a weekly basis to fine-tune the system.

• Why it matters: Real-time KPI tracking systems saw a 30% increase in employee productivity. (FieldPie, 2025).

Key Takeaways:

• Start with a single problem and prove value before scaling.

• Data quality is the foundation of every successful AI project.

• Cross-functional teams and open tools drive adoption and ROI.

• Continuous measurement and adjustment keep AI initiatives on track.