This article is based on a recent conversation with Joe David, Vice President of Professional Services at BCforward, about how leaders can identify AI use cases that create real business value.
Watch the full interview below for Joe’s perspective on how enterprise leaders can separate meaningful AI use cases from trend-driven experiments.
AI is everywhere right now. It appears in product descriptions, service offerings, internal roadmaps, vendor pitches, and boardroom conversations. But the presence of AI does not automatically mean business value is being created.
For enterprise leaders, that distinction matters. The organizations that benefit most from AI will not be the ones that launch the most pilots or adopt the newest tools first. They will be the ones that connect AI to real business priorities, measurable outcomes, and the way work actually gets done.
As Joe David, VP of Professional Services at BCforward, explained, one of the biggest risks companies face is starting with the technology instead of the business problem.
“AI success has very little to do with the tool itself,” David said. “It has a lot more to do with how the organization puts it to work.”
That mindset is quickly becoming the difference between AI experimentation and AI impact.
The Risk: Choosing AI Use Cases Because They Sound Innovative
Many companies are under pressure to show progress with AI. That pressure can lead teams to select use cases because they are popular, visible, or exciting rather than because they solve a meaningful business constraint. The result is often a collection of pilots that demonstrate activity but do not clearly connect to business priorities.
David pointed to AI agents as one example. There is significant excitement around autonomous agents and their potential to manage complex workflows. But in practice, the real difficulty often appears once those agents need to operate inside a business environment.
Task-level flows may be manageable. But when a use case involves coordination across ERP systems, CRM platforms, approvals, exception handling, and edge cases, implementation becomes much more complex. If the business objective is not clearly defined first, those challenges may not appear until the organization is already deep into implementation.
For leaders, the lesson is direct: a use case should not be selected because it sounds innovative. It should be selected because it solves something the business can clearly name, measure, and improve.
Beyond the Claim That a Partner “Does AI”
Nearly every company now claims to have AI capabilities. AI is attached to software, services, workflows, platforms, and products across the market. But the claim itself does not tell leaders whether AI will improve an outcome.
According to David, the better question is not whether a partner uses AI. The better question is whether AI helps deliver the outcome in a stronger way. The strongest partners start with the business problem first. Then they determine how AI can improve the service, solution, or operating model around that problem.
Leaders should be asking whether the partner can help create speed, improve analysis, focus employees on higher-value work, connect new workflows to existing operating models, and take accountability for measurable results.
This is where the AI conversation shifts from curiosity to accountability. It is no longer enough to demonstrate that a tool can do something. Enterprise leaders need to know whether it will change performance in a way that matters.
What Makes an AI Use Case Meaningful?
A meaningful AI use case has three characteristics: economic value, data readiness, and actionability.
Economic value means the use case improves something that matters to the business. That may be internal efficiency, customer experience, revenue growth, cost reduction, risk management, or better decision-making.
Data readiness matters because AI depends on the quality, accessibility, and structure of the information behind it. If the organization’s data environment is fragmented or unreliable, AI may amplify those issues rather than solve them. Actionability is the third and often overlooked factor. Even if a model works, it only creates value if its output changes a decision, process, or workflow.
For example, using AI to summarize customer service tickets may be helpful. But the real value shows up when the system can identify the issue type, route the ticket to the right team, suggest the next best action, and help reduce repeat contacts.
At that point, AI is no longer just organizing information. It is improving the flow of work.
How Leaders Should Pressure Test an AI Idea
Before allocating budget, time, and internal resources to an AI initiative, leaders need to move past the technical specifications and ask practical business questions.
The Enterprise AI Litmus Test
- Metric Impact: What specific business KPI will this directly improve?
- Process Change: Exactly which steps in the current workflow will look different tomorrow?
- Ownership: Who owns the ultimate business outcome, not just the software?
- Measurement: How will we calculate ROI if the tool works perfectly?
These questions are simple, but they immediately expose whether an organization has the operational clarity needed to support adoption.
David pointed to the common use case of deploying AI for proposal development. The test should not just be whether AI can write text. A true enterprise test measures whether the tool reduces first-draft timelines, pulls approved historical data faster, improves consistency, and gives the pursuit team more breathing room to focus on pricing, win themes, and strategy.
When scaled this way, AI ceases to be a novelty software experiment. It connects directly to how the business wins revenue.
Warning Signs That an AI Initiative May Become a Wasted Investment
AI initiatives often show early warning signs when they are headed in the wrong direction.
One of the clearest signs is that no one has defined success. Another is when teams focus heavily on model performance but cannot explain the business outcome the model is supposed to improve.
Data issues are another warning sign. If teams are slowed by inconsistent, incomplete, or poorly governed data, the initiative may expose larger operational problems that need to be addressed first. The absence of an integration path is also a major risk. If there is no clear plan for how AI will fit into an operational workflow, the initiative may remain disconnected from the business.
As David put it, “AI amplifies your environment. If your data and process aren’t ready, AI won’t fix that. It’ll just scale the problem.”
That is why accuracy alone is not enough. A forecasting model may be technically accurate, but if sales leaders do not trust it, finance does not use it for planning, and account teams continue working from spreadsheets, the model has not changed the business.
For enterprise teams, the warning signs are not always technical. More often, they show up as gaps in ownership, trust, adoption, workflow integration, and decision-making.
“AI amplifies your environment. If your data and process aren’t ready,
AI won’t fix that. It’ll just scale the problem.”
— Joe David, VP of Professional Services, BCforward
What Leaders Should Measure to Know Whether AI Is Working
To understand whether AI is creating value, leaders need to measure business impact, not just technical performance. Model accuracy may matter, but only if it improves the outcome. The more important measures often include productivity improvements, cost reduction, cycle time, revenue lift, quality improvements, and adoption across the team.
For example, an AI recommendation tool may produce accurate outputs. But leaders still need to ask whether it helps close tickets faster, shortens onboarding time, improves forecast quality, reduces manual rework, or increases adoption.
The real question is not simply, “Did the model work?”
The better question is, “Did performance improve because people actually used it?”
That shift in measurement is critical. AI that sits outside the workflow may look impressive in a pilot but fail to create measurable value in the business.
The Companies That Generate ROI Will Treat AI as a Business Capability
Over time, the difference between companies that generate real ROI from AI and those that only experiment with it will become more obvious. The organizations that succeed will treat AI as a business capability, not a standalone technology initiative. They will invest in data and governance early, focus on a few high-impact use cases, and embed AI into their operating models.
The organizations that struggle will continue launching pilots without scaling them.
For many leaders, the best path forward is to start with one or two high-value areas, such as speeding up intake, improving knowledge retrieval, reducing manual quality checks, or making proposal development more efficient. From there, the organization can build the governance, data, adoption, and ownership model around those use cases.
Once value is proven in a controlled area, the business has a playbook it can repeat more broadly.
That is where AI begins to move from experimentation to transformation. It becomes less about testing what the technology can do and more about changing how work gets done.
For enterprise leaders, that is the real opportunity. AI can create value, but only when it is tied to business outcomes, supported by the right data and workflows, and measured by the impact it delivers. The organizations that understand this will not simply adopt AI. They will operationalize it.
Ready to Move From AI Experimentation to Measurable Value?
AI readiness starts with knowing where your organization has the clearest opportunity to create impact, where your data and workflows may create friction, and which use cases are worth pursuing first.
BCforward helps enterprise leaders assess readiness, pressure test AI opportunities, and build practical paths from strategy to execution.
If your organization is exploring AI but needs a clearer way to prioritize use cases, reduce risk, and connect investment to measurable business outcomes, reach out to learn more about BCforward’s AI Readiness Assessment.
