Technology Enablement: The Third Lever

This is the third deep dive in our Enterprise Value Model series. If you missed the introduction from Brian Blaha, start with Five Levers That Separate Firms Building Enterprise Value from Firms Talking About It. The first lever, Top-Line Growth, is here. The second, Margin Expansion, is here.

Every firm is buying technology. Adoption is nearly universal, with roughly 95% of firms using some form of automation and 73% having adopted AI in some capacity. Yet if technology created enterprise value on its own, the profession would be worth far more than it was three years ago. For most firms, that has not happened. The tools have arrived, but the value has been slower to follow.

After more than twenty years running technology inside accounting and law firms, I've reached a clear conclusion: enterprise value comes from a firm's capability to put technology to work, and that capability is far more organizational than technical. The firms that generate real value from technology align their data, processes, and organization to absorb what they buy.

Start With the Right Goal: Become AI-Infused

There is significant pressure to become an "AI-native" firm. AI-native firms are built around AI from the ground up, and most are startups without legacy systems to carry. Established firms are playing a different game. For them, the more practical goal is to become AI-infused by systematically weaving AI and automation into their evolving operating model.

The value emerges when strategy comes first and technology second. AI is a tool that supports an integrated strategy and is one component of the firm's broader technology layer. Firms struggle when they treat AI as the target itself. Start with where the firm is trying to go, then select the technology that can help it get there. Validate it in short order, and do not be afraid to shift.

AI and Automation: The Technology Is the Easy Part

AI is effective at routine work, which is where most firms have started: data entry, reconciliations, and standard returns. The results are real. Firms using automation well are processing invoices faster, providing clients with information more seamlessly, and shortening the time required to close their books.

Automating a task frees capacity, but what that capacity becomes and whether the team adopts the new way of working are separate decisions. In my experience, technology projects succeed or fail on change management: the organizational structure, workflows, and people surrounding the tool. A firm that automates the work, then validates and adapts, captures the value. A firm that buys the tool and waits for behavior to change on its own keeps waiting.

Delivery Accelerators: Redesign the Work Around the Tool

The second place technology creates value is in delivery: the platforms and workflows that compress cycle times. Standardized processes, workflow automation, and right-shored delivery can dramatically reduce the time required for routine engagements, with some firms seeing 50% to 70% reductions on standard returns. AI did not create these gains; it accelerated them.

A fast tool bolted onto a slow process produces a slightly faster slow process. Firms also scale in steps rather than smoothly. Roughly every 200 to 300 people, the operating model must be rebuilt, and delivery technology that ignores the organizational structure beneath it will stall at those inflection points. Capturing delivery gains requires redesigning both the work and the organization around the tool.

Data and Analytics: The Prerequisite Everyone Skips

The third and most frequently overlooked source of value is data and analytics, the foundation on which the other two depend. You cannot infuse AI into a firm that cannot trust its own data. Before analytics or AI can deliver meaningful results, the data must be clean, consistent, and governed. Firms sit on years of valuable information, including client profitability, realization, pipeline, and cross-sell patterns. When that data is messy, every tool built on it inherits the mess.

Data quality is the unglamorous work that determines whether everything built on it functions. Get it right, and data becomes the connective tissue of the entire model: top-line growth becomes more disciplined when you can see where growth truly comes from, and margins improve when you can see which work is actually profitable. Get it wrong, and the AI layer inherits the problem.

Technology Enablement Is an Organizational Decision

The right strategy depends on the firm's size, operating model, resources, and appetite for change. A smaller firm needs tactical wins. A larger firm needs governance and structure. The advantage lies not in the tools themselves, but in the organizational disciplines surrounding them: clean data, aligned workflows, effective change management, and governance appropriate to the firm's size. Communication up, down, and across is equally important. Real technology change happens inside the firm, not inside IT, and it requires continuous messaging and feedback loops.

A firm that treats technology as a purchase ends up with a larger software bill and the same business. A firm that treats technology as an organizational capability builds something a buyer sees as structurally more valuable. Becoming AI-infused ultimately requires becoming the kind of firm that can absorb and apply the technology.

Next in this series: Talent Model Evolution, the fourth lever, examining why the way a firm builds and deploys its people may be the hardest capability to copy and the most valuable to build.

Winding River Consulting works with professional services firms to build the capability required to turn technology into enterprise value. Schedule a conversation with Jerry at jjustice@windingriverconsulting.com to explore your firm's largest opportunities.