Sesh Tirumala is Chief Information Officer for WD.
For years, technology organizations have been built around projects. Define the scope, set the timeline, deliver on time and on budget, then move to the next one. That model served IT well, but technology’s role is expanding.
In an environment shaped by AI, data and changing customer expectations, the CIO mandate is evolving. Technology organizations should be measured by the business value they create—and how quickly they create it.
That makes business velocity an important measure of technology impact. The question is becoming: How quickly can we move from signal to decision, decision to action and action to measurable business value?
Move From Projects To Outcomes
Projects have a beginning and an end. Business value continues to evolve.
That distinction is why we have been shifting our technology organization at WD toward end-to-end value streams. Instead of organizing work around individual systems or projects, we are aligning teams to the processes that create value across the business—from turning customer opportunities into revenue to planning demand, fulfilling orders and helping engineering teams design and build faster. The bigger change is accountability.
In a traditional project model, success can mean delivering what was requested within an agreed-upon timeline and budget. In a value-stream model, the questions become broader: Did we improve the outcome? Did we remove friction? Did we make the process faster or better?
It also changes how teams prioritize technology investments. Teams must understand how the work connects to a measurable business outcome and where technology can create the greatest impact.
For CIOs, the principle is simple: Own the outcome, not just the output.
Make Velocity A Business Metric
Speed is often discussed as a benefit of digital transformation. I believe it needs to be treated as a measure of business performance.
Unnecessary handoffs, unclear ownership, disconnected systems, technology debt and time spent reconciling information all slow the business down. Technology leaders are well positioned to address those barriers.
Think about the operating loop of a business: signal, decision, action, outcome, learn, improve. The faster an organization can move through that loop, the faster it can respond to customers, address issues and capitalize on opportunities.
CIOs should look beyond traditional delivery metrics and consider measures such as cycle time, decision speed and time-to-value. Build velocity on trusted data, context and AI.
An organization can move quickly with confidence when it has trusted data. Poor data quality creates friction. When teams spend time debating definitions, reconciling numbers or validating information, decision-making slows down. Strong data governance, quality and ownership are prerequisites for speed.
Trusted Data Also Needs Context
A number becomes more valuable when we understand what it represents, how it relates to other information and what it means to the business. A supply number, for example, takes on different meaning depending on the product, customer commitment, factory constraint, inventory position and time horizon.
This is where common business definitions, context, ontology and semantic layers become important. They create a common language across the enterprise and allow data from different systems to be understood.
This foundation becomes important as AI moves from answering questions to supporting decisions and taking action. AI needs access to data and an understanding of the relationships among customers, products, suppliers, factories, processes and financial outcomes.
We’re shifting how we think about AI. AI is a capability we apply to business problems and opportunities to unlock value. The question becomes: Where can AI fundamentally improve the way work gets done?
At WD, we are embedding AI into operational value streams across quality, manufacturing, supply chain and engineering. Our AI Center of Excellence provides a structured path from use-case intake through delivery and scale, with business outcomes considered from the beginning. This also means AI transformation spans technology, workforce and work itself.
We need to rethink workflows, decision rights, roles, handoffs and the division of work between people, systems and AI. Some tasks will be automated. Some decisions will be augmented. New forms of human-machine collaboration will emerge. The opportunity is to redesign the end-to-end process so that the organization can operate with greater speed, intelligence and scale.
As AI becomes embedded in business processes, security and governance become equally important. The enterprise needs to know what data AI can access, how it can be used, which decisions require human approval and how an AI-generated recommendation can be traced back to trusted sources.
This makes data, AI and security the three critical pivots of the modern technology organization.
Data provides the facts. Context gives those facts meaning. AI turns that knowledge into intelligence and action. Security and governance create the trust to operate at enterprise scale.
The CIO As Chief Impact Officer
Ultimately, shifting to value streams is as much a leadership change as an operating-model change.
Technology organizations have historically been viewed as service providers: The business defines what it needs, and IT delivers it. That relationship is evolving into a true partnership.
If technology leaders enter the conversation after strategy has been set, technology becomes an enabler of decisions already made. CIOs need to be at the table earlier—helping identify opportunities and connecting technology, data and AI investments to growth, productivity, resilience and innovation.
I have long believed the CIO should think like a Chief Impact Officer. The role now spans technology, data, AI, digital experiences and business transformation. What connects those responsibilities is impact.
The emerging operating model is straightforward: Value streams define where the enterprise creates value. Trusted data makes that value visible. Context and common business meaning make it understandable. AI accelerates decisions and execution. Security and governance create the trust to scale it. Outcome ownership creates accountability.
The biggest opportunity is to go one step further: rethink how the work itself should be done. Together, these shifts create an organization that can sense change, understand what it means, make informed decisions, act quickly, continuously learn and improve—and create more value with every cycle.
The standard for technology leadership should be: Did we improve the business—and how quickly did we get there?
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