Many companies want to use AI and AI agents to make their lead-to-cash processes more efficient. They experiment with generative AI, test initial use cases, and develop prototypes. Yet moving from a successful proof of concept to production remains a challenge.
The key question is not just what AI can do. Companies also need to understand where it can deliver measurable business value and whether their SAP landscape, data, and processes are ready to support implementation. This decision-making foundation is particularly important for complex B2B and industrial organizations. Prioritizing AI use cases without a thorough assessment risks wasted investment, costly rework, and solutions that fail to gain traction in day-to-day operations.
Why Lead-to-Cash Is a Relevant Area for AI
The lead-to-cash process covers the entire journey from the first customer interaction through marketing, sales, and order management to invoicing and service. In many companies, SAP is a central component of the underlying system landscape. This creates numerous opportunities to apply AI across lead-to-cash, including lead qualification, sales proposal generation, customer inquiry handling, and support for order management.
At the same time, these processes are closely interconnected. A decision in sales can affect order fulfillment, product availability, billing, and customer service. Looking at an individual use case in isolation often fails to capture these dependencies. As companies move from traditional generative AI applications toward AI agents, these interdependencies become even more important. An AI assistant might summarize information or support employees with a specific task. AI agents, by contrast, can potentially execute multi-step tasks and trigger process activities.
This raises the requirements for data quality, interfaces, process definitions, and governance. The key question is therefore not simply whether a use case works technically, but whether it can be reliably integrated into the business process.
Three Questions to Answer Before Selecting an AI Use Case
Before investing in implementation, companies should systematically assess three dimensions.
Business Value: What measurable value will the use case deliver?
Not every technically feasible AI application makes economic sense. What matters is how a use case contributes to specific business objectives, for example by reducing manual effort, shortening cycle times, or improving the quality of customer interactions. This requires reliable baseline data, appropriate KPIs, and a realistic assessment of the expected benefits.
Feasibility: Can the use case be implemented within the existing SAP landscape?
Data availability, system integration, process variations, and technical dependencies largely determine the complexity of implementation. A promising use case can run into difficulties because of inconsistent master data, missing interfaces, or legacy system structures. Technical feasibility and process maturity should therefore be assessed early, before an initial idea develops into a larger implementation project.
Adoption: Will the solution actually be used in day-to-day operations?
Even a solution that is economically attractive and technically feasible delivers little value if it does not fit existing workflows. Roles, responsibilities, trust in AI-generated results, and the involvement of affected employees all influence whether an application will be adopted over the long term.
Only by considering business value, feasibility, and adoption together can companies establish a sound basis for prioritization.
Why AI Agents Do Not Automatically Fix Existing Weaknesses
A common mistake is to introduce AI agents before clarifying whether the necessary foundations are in place. Existing process and system issues can easily be overlooked.
Consider an agent designed to support sales proposal generation. It needs reliable customer and product information, access to relevant pricing data, and clear rules for discounts and approvals. If this information is distributed across multiple systems, inconsistent, or difficult to access, automation becomes unnecessarily complex. The problem does not necessarily lie in the AI model itself. It lies in the conditions under which the solution is expected to operate.
Typical challenges in complex SAP environments include:
Fragmented data: Relevant information is spread across different systems or is not sufficiently consistent.
Process gaps: Handoffs between departments and systems lead to manual steps or breaks in process continuity.
Technical legacy issues: Historically evolved integrations and custom extensions make implementation more complex.
Unclear governance: System ownership, access rights, and decision-making rules are not sufficiently defined.
Low adoption: Employees are involved too late, or the solution does not fit their actual workflows.
These factors do not necessarily rule out the use of AI. Instead, they highlight the prerequisites that need to be met and the measures that may be required before or alongside implementation.
From Potential to Decisions: What a Robust Assessment Needs to Deliver
A sound basis for decision-making starts with an end-to-end understanding of the lead-to-cash process. Rather than examining individual tasks in isolation, companies need to identify dependencies, relationships, and potential AI opportunities across the entire process chain.
The next step is to assess the relevant prerequisites:
Which data and systems are required?
Where do technical or organizational constraints exist?
What process changes would be necessary?
How would implementation affect the teams involved?
This analysis provides the basis for prioritizing suitable use cases. Both short-term opportunities and longer-term transformation potential should be considered. An ROI assessment of the highest-priority use case adds further transparency. It compares the expected benefits with the required investment and ongoing costs, supporting informed internal investment decisions.
The result is more than a list of AI ideas. It is a structured basis for determining which use cases are worth pursuing, which prerequisites are still missing, and what steps should come next.
Which Companies Benefit Most from This Approach?
A structured assessment is particularly relevant for industrial and B2B companies with complex lead-to-cash processes and SAP as a central platform.
Typical scenarios include:
Initial AI proofs of concept exist, but a clear scaling strategy is missing.
Multiple business units propose use cases, but establishing a transparent prioritization is difficult.
An SAP or SAP S/4HANA transformation creates new opportunities while existing dependencies still need to be addressed.
AI, IT, and business leaders need a shared business case.
The company wants to deploy AI agents but is unsure whether its data, processes, and architecture are sufficiently prepared.
The AI Lead-to-Cash Audit: Assess Systematically and Prioritize Effectively
With its AI Lead-to-Cash Audit in the SAP Environment, diva-e offers a structured approach to jointly assess AI opportunities and implementation readiness across the lead-to-cash process.
The audit follows five steps:
Understand: Establish a clear understanding of the relevant process, the current situation, and business objectives.
Discover: Identify potential AI use cases across the lead-to-cash process.
Assess: Evaluate business value, technical feasibility, and the prerequisites for adoption.
Prioritize: Rank use cases using transparent assessment criteria.
Activate: Develop recommendations and define the next steps toward implementation.
Deliverables include a prioritized AI use case map, an AI readiness and gap assessment, a roadmap with actionable recommendations, and an ROI assessment for the highest-priority use case. The scope is agreed in advance for the relevant countries and/or business units. The audit typically takes two to three weeks and includes two to three workshops, interviews with relevant stakeholders, and access to the required process, system, and data information.
This gives companies a structured foundation for planning their next steps and making more targeted investment decisions.
Conclusion: Understand the Prerequisites Before Investing
Do you already know which AI use cases in your lead-to-cash process offer the greatest business value—and whether your SAP landscape, data, and processes are ready to support implementation?
If the answer is not yet clear, the first step is to establish a sound basis for decision-making. Successful AI adoption in SAP lead-to-cash depends on more than technology. It requires identifying the right use cases, realistically assessing their business value, and understanding the prerequisites for sustainable implementation.
By evaluating business value, feasibility, and adoption early, companies can make better-informed investment decisions and establish a stronger foundation for scaling AI and AI agents. The next step is not necessarily another proof of concept. It is making an informed decision about where AI can deliver real value across your lead-to-cash process.
Ready to identify your highest-value AI opportunities?
The AI Lead-to-Cash Audit in the SAP Environment helps you systematically identify relevant use cases, prioritize them based on business value, feasibility, and adoption, and derive actionable recommendations for implementation.
Within two to three weeks, you gain a structured basis for decision-making, including a prioritized AI use case map, an AI readiness assessment, a roadmap, and an ROI assessment for the highest-priority use case.
