Build AI value without rebuilding your data

Start small by design

Start by asking which decisions or workflows you can improve to materially change performance today.

For instance, when organizations apply AI to finance operations like forecasting, reconciliations or compliance monitoring, they can tap into their finance data that is already contained, structured and governed effectively. CFOs using AI in these areas can improve accuracy and cycle times without first modernizing every upstream system.

By narrowing the scope of AI solutions, teams can prepare only the data required to solve a specific problem, showing returns on an AI investment without waiting for a larger data readiness initiative. The first step is to find the right targeted AI use case.

Six questions to identify targeted AI use cases

These questions can help leaders identify which AI use cases could deliver early value for their organizations.

1. Does the AI solution address a problem that’s specific and tied to an outcome leaders already track?
   Organizations might have issues that seem important or pervasive but aren’t tied to clear metrics. To show early value, a targeted solution needs to focus on an issue with clear metrics, such as reducing errors, shortening close cycles or improving customer response times.
2. Is the data set bounded and owned by a small group?
   A targeted AI solution can improve reconciliations by aggregating data from a limited number of accounting and custodian systems, rather than the full enterprise landscape.
3. Is the workflow repeatable and high volume?
   In asset management, for example, fund administrators use AI to flag reconciliation exceptions every day. This can turn small efficiency gains into measurable impact.
4. Can we measure success with existing metrics?
   In finance and fund administration, accuracy rates, hours saved and exception clearance rates are metrics that already exist and can make value visible. Even if these metrics are not currently tracked by the leaders who will evaluate the AI solution, they can be captured and evaluated to determine the value of the AI solution.
5. Does the AI output fit directly into existing processes?
   It’s important for targeted AI solutions to surface insights that are readily usable within existing processes, reinforcing adoption while ensuring that people retain decision authority.
6. Does the use case have a natural expansion path?
   It’s important to think about how the solution can be expanded to drive further gains across the enterprise. If the solution and data are so isolated that there’s no opportunity for expansion, then the initial gains will not be aligned to a next-stage initiative. For instance, customer-facing AI solutions in services firms often start with internal insights, then later feed CRM or marketing systems as confidence grows.

When organizations apply these questions to their use-case analysis, they can focus targeted solutions — and data readiness — where they will have the most impact.

Real examples of targeted AI

Many organizations have already implemented AI in targeted use cases where solutions are delivering measurable business value.

• Intelligent document processing for high‑volume workflows:
  When processes rely on manual reviews of invoices, contracts, forms or other structured documents, AI solutions can automate document classification, data extraction and validation using a defined set of document types rather than the full enterprise content library. These solutions can improve speed and accuracy while feeding results directly into existing finance or operations systems. Because the data scope is limited and repeatable, organizations can achieve measurable efficiency gains without redesigning enterprise data models.
• AI‑assisted forecasting and variance analysis:
  Organizations can train AI models on historical financial and operational data, then use the models to identify patterns, trends and variances earlier than traditional approaches. These solutions typically rely on existing finance data sets that are already governed and trusted. Teams can improve forecast accuracy and decision speed while keeping the solution connected to core planning processes, creating a clear path to expand insights across the enterprise.
• Automated exception detection in routine processes:
  Finance, operations, compliance and other teams can spend a lot of time reviewing transactions to find exceptions. Machine learning can scan defined data feeds to flag anomalies for human review. This reduces manual effort and focuses attention on investigating high-priority issues. The data required is narrow and well understood, yet the solution integrates into existing workflows, making it easier to extend the same pattern to other processes over time.
• Accelerate and standardize customer support:
  In customer operations, AI can help organizations deliver faster and more consistent responses. Solutions apply generative AI to a subset of customer interaction data, improving service quality while keeping human oversight in place.

In each of these cases, organizations start by identifying a specific problem, setting a limited data scope and defining clear measures of success. At the same time, these cases are not isolated — they align with trajectories that can achieve greater value with next-stage solutions.

Avoid the cost of isolation

Earn the right to scale

Early AI success reshapes leadership conversations. Leaders support broader investment when they see measurable results tied to strategic priorities.