BY Emre Yenier, Principal Consultant, VFP, and Kristin Hubbard, Managing Consultant at VFP
Summary
You have done the hard work. Your governance is robust, your processes are standardized, and your teams are aligned. Yet, even with a solid foundation in place, starting the journey into Artificial Intelligence can feel overwhelming. Without a logical framework, AI success will feel like a random stroke of luck rather than a strategic victory. To capture the full potential of the AI boom, leaders must apply a deliberate lens to identify exactly where AI can add the most value to their established systems
Where to Begin?
In our previous discussion, we explored why you must fix your foundation, addressing technology, processes, and people before adopting AI. If you followed that roadmap, you have:
- Selected a suitable software solution and implemented it with minimal customizations.
- Standardize your processes and align them with industry best practices.
- Ensured your team is onboarded, excited, and supported by clear change management policies.
Now that your systems are live, the goal shifts: how do you get more value out of those investments? The answer lies in looking for specific friction points across your existing “Big Three”: People, Process, and Technology.
The Strategic Lens: People, Process, and Technology
To move from “functional” to “intelligent,” evaluate your operations through these three categories to find the “sweet spot” for AI integration.
People: Enhancing Human Input
Look for areas where human effort is currently a bottleneck due to repetitive or high-volume tasks. AI excels at combining well-defined tasks with rule-based reasoning, allowing your team to focus on higher-value work.
The Opportunity: Identify tasks with high cognitive load, such as reviewing large volumes of contracts, tickets, or financial data.
Example: Automating the checking of expense reports against corporate policy and receipts. AI can verify accuracy and compliance instantly, flagging only the exceptions for human review.
Process: High Volume meets Pattern Recognition
AI requires historical data to learn effectively, making volume a key prerequisite. Focus on processes that involve both structured and unstructured data (such as emails or PDFs), where 70–80% of cases follow a predictable pattern.
The Opportunity: Identify high-volume workflows, such as invoice validation or support ticket routing, that suffer from long cycle times.
Example: Predictive Attrition Analysis. By analyzing the tone in communications and the frequency of support cases, AI can highlight accounts at risk of churning before a human agent even notices a trend.
Technology: Turning Data into Intelligence
Many systems generate vast amounts of data but fail to leverage it. If your team is still exporting data to Excel to perform manual summaries or analysis, your technology is not delivering its full potential.
The Opportunity: Look for “underutilized data”, large datasets that lack predictive analytics or search functionality that doesn’t “understand” content.
Example: Risk Flagging. AI can be trained to analyze all data points for a project (milestones, budgets, and actuals) to automatically flag high-risk projects, providing a summary that would otherwise take a project manager hours to compile.
Takeaways for the AI-Ready Leader
As you move forward, keep these two guiding principles in mind:
- AI amplifies process maturity; it doesn’t fix chaos. If you try to apply AI to a chaotic or broken process, you will simply accelerate your inefficiency.
- Think Big, Start Focused: Focus on achievable goals with measurable value before launching a large-scale, enterprise-wide solution.
The Executive Quick Test:
Before approving an AI project, ask: Is the task repetitive at scale? Is there clean historical data? If the answer is yes, you are no longer just “chasing the buzz,” you are building a competitive advantage.
