Unlocking business transformation through agentic AI

For development, they use Azure’s machine learning services to train the AI on vast amounts of imaging data and medical literature. The resulting solution can analyze medical images and patient data to identify patterns and suggest possible diagnoses, serving as a second opinion for doctors. By using Azure’s cloud, the hospital guarantees data security and compliance with health regulations during this AI analysis.

Phase 4: Implementation and adoption

• Step 7: Implement the agentic solution in phased rollouts. Instead of a big bang deployment, start with a pilot program or a controlled rollout in one department or location. This allows the team to validate the solution in a real-world setting, measure results and work out any issues on a small scale before broader implementation. Monitor the pilot’s performance against the success criteria defined in the roadmap (Phase 2).
• Step 8: Drive user adoption through change management. Train employees and end-users on the new AI tool – not just how to use it, but how it benefits them. Communicate success stories and efficiency gains to build buy-in. It’s important to address concerns or resistance: some staff might fear AI will replace their jobs, so clarify that the AI is there to assist and elevate their roles. Executive champions should continuously reinforce the transformation vision. If needed, adjust workflows to best integrate the AI into daily operations.

Example: A large retail company rolling out an AI-powered inventory management system might first pilot it in a single flagship store. In this pilot, store managers and inventory clerks use the new system to forecast demand and automate re-ordering. Early results show reduced stockouts and waste, confirming the solution’s value. The company then gradually expands the implementation to more stores, region by region. Throughout this process, it holds training sessions for store staff on the new system and highlights that the AI helps ensure popular products are always in stock (improving sales and easing employees’ workload). By phasing the adoption, the retailer also fine-tunes the system’s algorithms with data from each new store rollout and it addresses employee feedback, ensuring high adoption rates and minimal disruption to operations.

Phase 5: Monitoring and optimization

• Step 9: Continuously monitor the performance of the agentic solution. Define key metrics (KPIs) that align with the project’s goals – e.g., processing time reduction, error rate, customer satisfaction scores, cost savings – and track them in real time if possible. Use analytics dashboards to observe how the AI is performing and where there might be bottlenecks or drifts in accuracy. This phase often benefits from setting up an AI Operations (AIOps) or monitoring team.
• Step 10: Optimize and evolve the solution based on data and feedback. Treat the agentic system as a living solution that requires periodic tuning. Update the AI models with new training data as more information is gathered, adapt to changing business conditions (like new regulations or market trends) and incorporate new features or improvements identified post-launch. Also, establish a feedback loop with users to capture their experiences — perhaps the AI could be making decisions faster, or needs to handle a new scenario. Version upgrades and integration of emerging technologies should be planned as part of a continuous improvement roadmap.

Example: A bank that has deployed AI-driven customer service agents and fraud detection systems keeps a close eye on these tools. The bank’s analytics show how quickly the AI chatbot resolves inquiries and tracks a reduction in call center volume. It also monitors the fraud detection AI in real-time, verifying how many fraudulent activities it catches and ensuring false positives are minimal. Using these insights, the bank makes adjustments: for instance, if the chatbot struggles with a certain category of questions, the AI team refines its natural language understanding. If new types of fraud emerge, data scientists feed those patterns into the fraud model to improve its accuracy. This ongoing optimization cycle helps the bank continuously improve user experience and service efficiency over time. By staying responsive to data, the bank ensures its agentic AI solutions remain effective and deliver sustained value.