From AI Experimentation to Enterprise Adoption: The Information Challenge Financial Services Must Solve
By Justin Cruz, Strategic Solutions Manager, Financial Services and Manufacturing at Laserfiche
AI is quickly becoming part of the day-to-day operations of financial services organizations. Banks, credit unions, insurers and investment firms are using it to support underwriting, claims processing, fraud detection, customer service, and other processes that rely heavily on information and documentation.
As these use cases move from pilots into the broader business, business leaders must become honest with themselves to determine if their organizations have the right information infrastructure in place to set them up for success.
Financial institutions have accumulated years of documents, records and data across core systems, departmental applications, cloud environments, and shared repositories. Much of that information remains unstructured or disconnected from the processes that depend on it. When AI is introduced into those processes, the quality and accessibility of that information can have a significant impact on the results.
AI Is Only as Useful as the Information Behind It
Consider an AI system supporting a lender with a loan application. To evaluate an application, it may need to work across income documentation, application forms, correspondence, credit information, and internal lending policies. If those documents are scattered across systems, inconsistently classified or difficult to access, the AI system has less context to work with.
This is where financial institutions should take a closer look at the information already moving through their critical business processes. Where does it live? How is it organized? Can the right people and systems access it when they need it?
AI-driven enterprise content management software, like Laserfiche, can help organizations classify and organize unstructured information, extract relevant data, and connect documents to the workflows where they are needed. This can be particularly useful in areas such as onboarding, lending, claims, and compliance, where employees may spend significant time searching for information, reviewing documents, or moving data between systems.
The goal is to make sure the right information is available in the right context, with appropriate controls around how it is accessed and used. For example, Members Trust Company reduced account servicing steps by 85% by replacing email-based account service requests with a centralized portal that captured required information, routed requests to the appropriate department, and automatically filed documents with searchable metadata.
This reduced account servicing from 15 steps to two and average handling time from 45 minutes to about 15 minutes per request.
The Workflow Matters as Much as the AI
A lot of attention has gone toward what AI can do. Financial institutions should also be looking at where AI fits into the work employees are already doing.
Think about the steps involved in processing a claim, opening an account, or reviewing a loan. Employees may still be collecting documents, validating information, requesting approvals, and transferring data between systems. Even when all the necessary information exists, these manual steps can slow the process down.
AI can help with individual tasks, but its value increases when it is connected to the workflow around them. A document could be classified when it is received, relevant data extracted, the next step initiated, and the task routed to an employee when human review is needed.
That creates a more defined role for AI. Instead of asking employees to work around another standalone application, organizations can incorporate AI into processes they already understand.
Governance Should Be Built Into the Process
As AI becomes part of important business workflows, organizations also need to know how information is being accessed and how AI is influencing the process.
That matters particularly in financial services, where a single workflow can involve sensitive customer information, major financial transactions and business decisions that must adhere to strict regulatory requirements. Organizations need visibility into where AI is being used, what information it relies on, and where human review remains necessary.
Governance works best when it is considered part of the workflow from the beginning. Access controls, audit trails, approval points and consistent information-management practices can help organizations establish that visibility without creating a separate process for every AI application.
There is also a practical reason to think about governance early. As organizations add more
AI use cases, managing them individually can quickly become difficult. Establishing common principles for how information is accessed, processed and acted upon gives teams a framework they can apply as adoption grows.
Moving Beyond AI Experiments
The next phase of AI adoption in financial services will be shaped by what happens after
the pilot. Organizations will need to connect AI to the information and processes that actually run the business. That means understanding where information enters the organization, how it moves between systems and people, where decisions are made, and where automation can remove unnecessary manual work.
For banks, credit unions, insurers and investment firms, this also creates an opportunity to revisit processes that have been digitized but never fully redesigned.
The technology will continue to evolve, but the underlying questions are more consistent: Is the information categorized correctly? Can it be accessed in the right context? Is the workflow designed to support both automation and human oversight? And can the
organization see what is happening along the way?
Answering those questions can help financial institutions move AI from experimentation into everyday business processes, where its impact can be measured in faster decisions, less manual work, and better experiences for employees and customers.
Click here to go to our resource: Shaping Tomorrow's Enterprise: The Power of Intelligent Workflow Automation
