Agentic AI: From Bold Claims to Real-World Readiness
As the narrative around Agentic AI continues to gain momentum, organizations face a critical question: how do we decide if it’s the right time, place, and model to deploy it? While early experiments and pilots show promise, large-scale adoption must be guided by a clear and structured framework that balances ambition with operational reality.
This final article in our series introduces a practical roadmap for businesses seeking to assess Agentic AI’s feasibility—one rooted in strategic clarity, organizational maturity, and operational value.
1. Is your industry AI-ready?
Some sectors are naturally better positioned to benefit from Agentic AI:
- AI-Ready Sectors: Finance, logistics, e-commerce, telecom, and IT—industries with structured data, digitized workflows, and well-defined operational rules
- Cautious Zones: Healthcare, legal, public sector—domains with high stakes, complex regulation, and a critical need for human oversight
Agentic AI readiness would be based on your data readiness level and existing automation baselines in your sector. This doesn’t mean you can’t start testing and prototyping use cases, yet that requires a clear use case that can be processed in a secluded space in order not to have to rely on external data. The best option to do so is to look for a new business need that is adjacent to your core business. For example starting a new venture that relies on existing customer data, coming from your CRM systems where the data are already mostly structured. This would provide you with a green field where you don’t have any legacy to deal with and where your teams can operate freely to build new a cae for change for the rest of the organisation. Design thinking—or simply revisiting your opportunity folder—is one of the best starting points. It enables a fast learning curve by allowing you to experiment with existing technology components without integrating them into your entire ecosystem. This approach helps you bypass typical approval barriers and accelerate initial prototyping.
2. What level of execution is acceptable?
Agentic AI doesn’t have to be fully autonomous to deliver value. Choose your level of execution:
- Human-in-the-loop – AI suggests, humans approve. Ideal for decision-critical operations
- Human-on-the-loop – AI acts, humans monitor. Suitable for real-time operations with low risk
- Fully autonomous – AI executes end-to-end. Best for back-office tasks with minimal external dependencies
The first two options enable clear escalation paths and fail-safes before granting execution authority to AI agents. Your learning curve will be exponential, without engaging any large budget for testing. You can run this within your walls by replicating existing operations so you can double check scenarios and involve business process experts to confirm the agentic AI behaviors if you want to target business process optimization, as you would be able to do with a RGM topic or customer data quality check for scoring, lead management or CRO (conversion rate optimization). This approach offers even more strategic value, as it builds early traction and can later be seamlessly connected to real-life operations—especially through A/B testing capabilities of your eMarketing and eBusiness teams. Starting with topline initiatives is a strong move, as revenue generation potential reinforces the business case and simplifies ROI demonstration.
3. Which business processes should AI automate first?
I would have initially recommended to start with tasks that are structured, repeatable, and rule-based, like: (a) internal functions (invoice matching, contract review, data validation, system monitoring), knowledge tasks: Report drafting, scheduling, document classification, or even low-risk front-office: FAQ bots, lead qualification, customer support triage. Still, it may impact way more than expected and can’t be called quick wins. So, though they are low hanging fruits, pay attention to those and build a strong case for change before moving forward. Working closely with behemoths on this topic can be highly beneficial—they are more likely to invest alongside you and support your business case. In this instance, I would recommend avoiding exclusive collaboration with startups. Instead, consider running a competitive benchmark between incumbents and startups to evaluate their respective performance and extract valuable insights for broader, scalable projects.
Another way of thinking about this would be to look into high-stakes decisions (e.g. medical recommendations), non-standardized workflows, and customer-facing financial transactions. This would provide heavy guardrails and an easy decision making process. This would need to be set up in an next generation innovation initiative, in order to understand what can be scaped and industrialized. This suggestion may really seem scary and a leap of faith you can’t absorb or receive buy-in from your organization, still this is a great way of redefining a landscape with budgets that are not that expensive knowing you’ll be massively supported by industry leaders in need of testing grounds with leading players. They would invest a lot with you and put you under the limelight. It may be a good option if you’re in an innovation-friendly setup.
4. Does agentic AI provide measurable ROI?
This one is for sure one of the key dimension, if not the one and only thing to keep in mind. AI adoption should not be a technology showcase, it must deliver business value. As usual, you need to focus on time saved or revenue generated. Efficiency gains, revenue impact, TCO, TTV, TTM, and GTM. Standard KPIs to track as they would help you for your financials. This is self explainatory so we won’t spend more time on it.
5. How should you pilot before scaling?
Successful agentic AI implementation hinges on thoughtful experimentation:
- Design a Low-Stakes Pilot: Target a non-critical process with measurable outcomes
- Build Cross-Functional Teams: Include legal, security, ops, and business units
- Capture Feedback Loops: Monitor performance, anomalies, and user trust
- Iterate Before Scaling: Use pilot insights to refine workflows and governance
These steps are really standard procedure for innovation at scale. You need to be the most hands-on possible in order to build your case for change here, closely monitor development and identify key takeaways that would provide you with a platform for the next stage. And though you may not like it, you would definitely need to onboard the CIO/ CTO/ CDO and update them early on to receive feedback on what is to be include/ excluded from your scope. Data consumption is a largely overlooked area from other functions, as was Internet before it became what we live in our everyday life. It’s the same sequence with AI, GenAI and now Agentic AI. Just as it was once essential to understand the fundamentals of eCommerce and eMarketing, it’s now critical to develop at least a baseline proficiency in data management and governance. Without this, scaling AI initiatives—particularly those involving Agentic AI—will prove nearly impossible. Tough luck, I understand, but this is the new non-negotiable skill set in a world where OpenAI and others have flung the floodgates wide open.
Final thoughts
Don’t get fooled by the common sense where all papers highlight amazing potentials by connecting Agentic AI to workflows in order to turn them into “intelligence”. The intelligence lies in your ability to make your organization AI ready. This is the only way you would be able to reach the full potential of this new data driven ecosystem that uses AI related technologies to make business process decisions. Which also means you need to heavily invest in (re)defining and updating your BPM, as the many agents you would set up will only make decisions out of your detailed and precise BPM, or hallucinate when confronted with non scripted requests.
Then you would be able to reach examples like dynamic pricing agents adjusting promotions in real time including components like brand values, thresholds of sanity (also known as loyalty and fairness), hyper personalization without nonsensical clustering that turns campaign management an horror show for operations teams (cascading to catastrophic results), and so many scary examples of AI generated operations you can find vastly documented on Reddit.
The road to success is all about data management and process management. As you will dive into prototyping and real life testing, try to make use of simple go/ no got tests so you won’t find yourself with a blind spot when industrializing. If you can’t validate outcomes easily, forget it for now. Build strong foundations you would be able to step onto first. Starting with strategic challenges to consider:
- Data Fragmentation – Disparate systems and unstructured data reduce agent effectiveness
- Lack of Internal AI Literacy – Execution depends on cross-team alignment and process clarity
- Governance – Organizations must define ethical boundaries, accountability, and intervention protocols
Agentic AI is not science fiction—it’s already delivering real results. But its power lies not in dramatic autonomy, but in targeted, controllable execution that enhances business performance. The organizations that will win the Agentic AI race are not those who adopt first, but those who adopt smart, as would say any consultant. I would go with the startup advice: keep it simple stupid: a legitimate use case that is well documented, with co-validated KPIs, and a strong case for change that highlight you can make a lot of baby steps quickly, with frequent go/ no go gates. You’ll prove you’re agile and consistent with your organization needs to innovate, but at scale. With all the constraints, limitations and prerequisites. Because you are now part of the innovators community. Welcome home.
This concludes our series on Agentic AI. Let’s meet and talk about your projects!
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