It starts the same way in almost every organization. Someone says, “We need to be doing more with AI.” Heads nod, ideas fly — and then silence — because the real question is, “Where do we even start?”
That tension between ambition and execution was at the heart of a recent Lemongrass Live webinar, “Making AI Real – How to Build the Business Case for AI in Your Organization.” Hosted by Doyle Tipler, Solution Architect at Lemongrass, with experts Jake Echanove and Joanne Cordon, the session explored how enterprises can move from experimentation to measurable business value, particularly within SAP and cloud environments.
From AI Curiosity to Enterprise Strategy
AI adoption today mirrors cloud adoption a decade ago — full of enthusiasm but lacking direction. The panel agreed that organizations often experiment with AI tools without connecting them to tangible business outcomes. The key to moving from hype to impact lies in structure: clear purpose, strong data foundations, and executive alignment. Without these, AI remains a collection of disconnected pilots that never scale.
Defining the Right Use Cases
Many organizations start with technology rather than the business problem. The panel stressed that the question shouldn’t be “How can we use AI?” but “Where will AI add value?” — for example, improving forecasting, speeding up procurement, or reducing operational costs.
They recommended a value-driven approach:
- Start with a business problem, not a tool.
- Quantify potential outcomes in measurable terms.
- Engage stakeholders from operations, IT, and finance to uncover genuine opportunities.
Historically, the most successful initiatives start when business and technology leaders align on a shared goal and a common definition of success.
Turning Ideas into Business Cases
Once the right use cases are identified, the next challenge is securing leadership confidence and investment. The “4Cs of AI Enablement” was recommended as a practical framework:
- Clarity – Define the business outcome and target metrics.
- Context – Understand where AI fits into the process and whether it solves the right problem.
- Connection – Link AI outputs to tangible business KPIs such as revenue, cost, or efficiency.
- Commitment – Secure leadership sponsorship and resources.
A fifth “C” — Criteria — was added to emphasize the importance of measurable results. Typically, leaders want both a compelling story and the numbers to prove impact. People will typically buy into a vision but they will commit when they see results. This balance between logic and narrative critical.
Building the Foundations: Data, Architecture, and Clean Core
AI success starts with data. Poor data quality, fragmented architecture, and layers of technical debt can derail even the most promising initiatives. Modern data architectures that unify SAP and non-SAP sources are key and applying clean core principles, e.g., staying current, reducing custom code, and standardizing processes, are enablers for AI adoption at scale.
AI in Action: Lemongrass’s Clean Core AI Solution
Lemongrass’s own Clean Core AI capability within the Lemongrass Cloud Platform (LCP) illustrates these principles in practice. The tool uses Artificial Intelligence to analyse customers’ SAP codebases, map dependencies, and identify what can be retired, reverted to standard, or modernized. In many cases, up to 60% of custom code can be safely retired.
This not only simplifies the SAP environment but also accelerates readiness for S/4HANA and AI integration, reducing cost and increasing agility.
Intelligent Use Cases: From Ops to Business Value
The conversation also covered the growing number of AI use cases emerging across industries — from predictive maintenance and AI Ops to intelligent planning. One example discussed was the idea of a supply chain control tower that predicts disruptions, models alternatives, and calculates the cost and carbon impact of rerouting in real time.
Each example underscored that the most successful AI deployments blend trusted data, clean architecture, and human oversight, proving AI isn’t about replacing human judgment — it’s about amplifying it.
The Risk of Inaction
While enthusiasm for AI is high, the panel warned that waiting too long carries risk. Competitors embedding AI into their operations today will gain strategic advantage tomorrow. The advice: start small, prove value with a pilot, build credibility, and scale from there. Transformation is iterative — AI success isn’t about doing everything at once but rather doing the right things with the right foundations.
Final Thoughts
To make AI real, business should start with expected business outcomes and work backward focusing on these three core principles:
- Outcome clarity – Define what success looks like and how it will be measured.
- Technical readiness – Build clean data, architecture, and governance foundations.
- Leadership alignment – Tell a story that connects strategic ambition with measurable business value.
If the value is there, AI will make the case for itself.
Watch now: Making AI Real: Building the Business Case for AI in the Enterprise
