Navigating Enterprise Transformation: Design and Data for Competitive Advantage

“Data is the lifeblood of a company. It’s more important today than it ever was because of AI.” Ed Lovely

Reinvention as an Operating Habit

IBM has been around for more than a century, and Seth framed that longevity around a single consistent thread: reinvention. The point was not nostalgia. It was proof that the company has repeatedly rebuilt what it offers and how it brings those offerings to market. Seth pointed to IBM’s recent shift as evidence. Seven years ago, less than half of IBM’s revenue was software-centric. Now it is more than 80%. That is not a minor portfolio adjustment. It is a new business model, which demands a new internal operating system to support it.

Client Zero and the Growth Loop

A core idea behind IBM’s approach is being client zero. Before IBM sells a product to customers, it uses it internally first. Seth described the logic as a loop: internal adoption tests and refines the offering, stronger offerings support revenue growth, revenue growth drives productivity, and productivity enables reinvestment. It sounds neat on a slide, but it only works if the organisation has a foundation strong enough to support daily decision-making without friction, confusion, or mistrust. For IBM, that foundation is enterprise data.

The Silo Trap That Slows Everyone Down

Seth began the data story where many enterprises still live. Working with data is often accepted as painful. You have to know where to find it, navigate access, export it, manipulate it in spreadsheets, then paste screenshots into decks and hope nobody asks the wrong question at the wrong time. The deeper issue is not inconvenience. It is structured. End-to-end workflows do not happen in silos. They cross boundaries between marketing, sales, finance, billing, and support. When data cannot flow across those boundaries, the organisation slows, manual workarounds multiply, and insight arrives late, if it arrives at all.

This is also where AI hits a wall. Without connected, governed data, AI becomes a set of isolated proofs of concept that struggle to show meaningful return. You end up optimising a corner of the business while the workflow that matters still breaks across the seams.

EPM and a Single Operational Truth

IBM’s answer is what they call EPM, Enterprise Performance Management. Seth described it as two things working together: an integrated enterprise data architecture and a front-end experience that makes governance and insight usable for IBMers. When data from end-to-end workflows comes together in an integrated model, it becomes exponentially more useful. A client journey might start at a marketing event, move into a sales opportunity, flow through billing and payment, and then land in customer support. Connecting those stages in one place changes what people can see, what they can trust, and how quickly they can act.

Seth positioned this as a “single source of operational truth” for the company, integrated across the enterprise and governed holistically, so teams can move faster with fewer arguments about what is correct.

Outcomes That Made the Room Pay Attention

Seth shared results that anchored the story in reality. Seventy-five per cent of IBM’s enterprise workflows are now optimised with integrated insights. AI use cases can be deployed in weeks rather than months. Reporting has been simplified by sunsetting more than 20 legacy platforms, with more planned. Then he landed the detail that felt almost unbelievable to many enterprise veterans: IBM’s C suite prepares and runs operating reviews with live dashboards, with no spreadsheets and no slide decks.

The C Suite Runs It Live

Ed explained what changed behind that headline. IBM’s operating team meeting happens every two weeks for two hours and includes the CEO, CFO, and senior leaders. In the old world, preparing for it cost the company 1,100 hours each cycle. Teams churned to assemble decks, reconcile numbers, and anticipate questions. Then the meeting would often stall when two leaders arrived with slightly different figures. Ed described how quickly a meeting disintegrates when trust collapses. Once the room doubts the data, every decision slows.

EPM changed the medium of the meeting. Leaders now run it “on the glass, meaning on live dashboards on their own devices. They can drill down from consolidated numbers to invoice, client, or product level in real time. That shift does two things at once. It removes the dead time created by parking lot questions and follow-up meetings, and it strengthens trust because everyone is looking at the same underlying model, just at different levels of detail.

Ed also shared a telling constraint. No helpers are allowed in the room. If the platform cannot be used directly by senior leaders, it fails the point of the exercise. IBM trained its most senior people to run the meeting themselves, which became a practical test of whether the experience was genuinely simple.

Why a Data Office Needs Designers

Seth asked the question that brought the talk back to the audience’s world. Why does the Chief Data Office have a design team? Ed answered from lived experience. He grew up in finance and accounting roles where working with data was arduous and simply accepted as “part of the job. For him, that resignation was unacceptable. If IBM wanted a single operational truth, the experience had to be simple enough that users would choose to engage with it, rather than dread it.

Ed and Seth also made a deliberate design choice: they designed for a hard persona, a brand-new IBMer with no internal context. If that person can find and use the right business data quickly, the system has a chance to scale across 300,000 plus people and 170 countries.

Preventing Misinterpretation at Scale

One audience member asked the question every large enterprise must answer: how do you know people are pulling the correct data and not misinterpreting it across the organisation? Ed described IBM’s data catalogue as the stabiliser. If someone is unsure what a data element means, they can go to the catalogue and see definitions, metadata, and context. It is a direct response to the classic enterprise failure mode, where two teams present two numbers that look similar but are derived differently.

Seth added that the catalogue is intentionally designed for discovery. People arrive with a specific need, then realise what else exists and how it connects. This is what “democratising data” looks like when done responsibly: access paired with understanding, and speed paired with appropriate governance.

The Catalyst and the Starting Point

When asked how IBM actually spearheaded centralisation, Ed named the catalyst: IBM’s CFO, Jim Kavanaugh. Jim’s view was that companies investing in data standards, privacy, and governance would have a distinct competitive advantage in the AI era. IBM started where correctness is non-negotiable: finance. Finance data has to be right, because it powers external reporting and tax obligations. Ed said it took roughly 12 to 15 months to reach a tipping point in finance, and once that happened, demand spread quickly across the company.

AI Fear, Reframed as Advantage

A question about job loss surfaced an emotion many teams are carrying. Seth’s answer was pragmatic. Businesses care about market outcomes. Design and data matter because they help deliver user outcomes that drive those business outcomes. If you anchor yourself there, AI becomes less of a threat and more of a tool. Practice changes, work shifts, and the opportunity to use AI to automate lower-value tasks and move faster on higher-value problems.

Ed widened the lens. Engineers have the same fears as code assistants improve. His message was consistent: AI is augmentation. The goal is not replacement, but enabling people to increase output and focus on what comes next.

Ed closed with a metric that revealed IBM’s product mindset. When asked how they built confidence in the timeline, he pointed to utilisation. They tracked monthly active users, and when that number began to spike, they knew the platform had crossed from initiative to habit. For IBM, that is the real definition of transformation: when the work changes, not just the narrative.

Want to watch the full talk?

You can find it here on UXDX: https://uxdx.com/session/navigating-enterprise-transformation-design-and-data-for-competitive-advantage/?utm_source=Website&utm_medium=Blog&utm_campaign=Post+Conference+Highlights

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