Why Your Digital Investments Aren’t Creating Value

Companies are investing heavily in digital analytics hubs with AI and gen AI capabilities, enterprise CRM systems, and marketing technology platforms. Nowhere is this more concentrated than in commercial functions, where growth and customer impact live. Yet, a growing unease permeates the C-suite. In a 2025 PwC survey, 56% of CEOs said their companies were not yet seeing financial returns from AI investments. We hear similar concerns from the executives we work with. While confidence in digital’s potential remains high, commercial teams struggle to translate that promise into tangible business results. In a 2026 ZS survey of U.S. pharmaceutical chief digital and information officers (CDIOs), fewer than half reported measurable value from commercial AI efforts, citing fragmented data, unclear ownership, and limited change management as persistent barriers. Further, leaders on ZS’s CIO Advisory Council say they struggle to tie substantial CRM transformation investments to revenue gains. Repeatedly, executives tell us that digital initiatives fall short of expectations: improving execution but not strategy, increasing efficiency but without freeing capacity for higher-value work, showing strong adoption but limited business impact, and creating disconnected solutions that don’t fit with existing workflows. Despite deploying the latest systems, companies are realizing only a fraction of the benefit they hoped for. Companies that successfully convert digital into value treat it as a commercial operating model transformation, not just a technology upgrade. They focus on four interconnected shifts that rewire how insights are generated, decisions are made, actions are coordinated, and analytics are organized. From human to asset-based intelligence. Insight moves from project-based analyses and judgement to reusable analytical models embedded in decision-making. From intermittent to continuous decision-making. Many decisions move from episodic cycles (for instance, quarterly planning) to real-time sensing and action, while less frequent decisions are made faster. From siloed to coordinated execution. Execution shifts from independent functional control to coordinated action across sales, marketing, and service. From fragmented to centralized analytics. Function-owned solutions and teams give way to shared platforms, tools, and talent that cut across the enterprise and geographies. Together, these shifts underpin digital’s enormous potential to enable personalization for customer impact and sales growth. Shift #1: From Human to Asset-based Intelligence Traditionally, when leaders needed insight—such as why a campaign worked, why a customer stopped buying, or where to focus next—they turned to specialists or commissioned studies that delivered answers. Insights resided in people and projects. With digital, insights increasingly reside in analytical assets, such as data, models, and AI tools embedded in commercial processes. At LinkedIn, salespeople responsible for renewals and growth in hiring and learning products once relied on fragmented dashboards and personal experience to prioritize accounts. Today, a machine-learning-based account prioritizer continuously ranks accounts based on upsell/cross-sell opportunities and churn signals. The intelligence resides in a reusable model that is automatically refreshed and applied consistently across sellers. As a result, salespeople spend less time crunching data and more time selling, driving an increase in renewal bookings. At Schneider Electric, technicians once had to physically inspect thousands of machines at some customer sites to identify those at risk of failure. Today, sensors embedded in the equipment stream real-time status and performance data that, together with asset age and maintenance history, feed a model that continuously identifies at-risk machines and directs customers, and Schneider sales and service personnel, toward preventive maintenance or upgrades. The result is improved uptime and safety for customers, and stronger sales growth and customer trust for Schneider. In both examples, analytical assets either support human judgment or operate autonomously within defined guardrails, shifting responsibility from experts to analysts and algorithms. The result is better and faster decisions, driving impact and growth. Shift #2: From Intermittent to Continuous Decision-making Organizations were built for a certain cadence: plans set annually or quarterly, execution in between, progress reviewed at defined gates. Digital assets change this model by enabling faster insight generation or embedding guidance into execution. In some decisions, the cadence remains episodic, but the cycle time shrinks. At a biopharmaceutical company, determining the size and structure of a large sales organization once took six months of project-based analysis and another six months to implement. Today, a leaner team completes the same work in three months, with most effort focused on implementation rather than analysis. At technology services companies, GenAI tools cut proposal generation time in half, boosting efficiency and customer responsiveness. In other cases, episodic reviews are replaced by continuous decision-making. At Takeda Oncology, salespeople once operated on static, monthly call plans. Today, a system continuously senses changes in individual patient journeys, allowing salespeople to deliver contextually relevant messages to oncologists exactly when a treatment decision is being made. Global shipping and logistics company Maersk applies the same logic in customer service. By layering AI capabilities onto integrated operational and customer data, the company enables near-real-time sensing and response to the thousands of delivery-related queries it receives daily. GenAI-generated draft responses allow operators to review and respond with a click. Digital collapses the time between insight, action, and impact. Shift #3: From Siloed to Coordinated Execution For decades, companies optimized commercial execution vertically. Marketing owned brand and demand, sales owned relationships and revenue, and customer success focused on value realization and growth. Even within marketing, brand, demand generation, media, and analytics teams often operated with distinct budgets, metrics, and priorities. Coordination was encouraged, but not structurally essential. Digital changes that formula. Customers now move fluidly across channels and functions. A physician interacts with a pharma company through reps, portals, scientific information, and patient programs. A cloud buyer moves from online research to technical sales to onboarding and support. Informed customers expect a connected journey. Companies are responding by using digital assets and embedded guidance to coordinate execution around customers rather than functional boundaries. At Microsoft (with whom we’ve consulted), commercial execution is increasingly aligned through shared workflows and customer signals embedded across CRM, collaboration, and support systems. These signals guide the actions of sales, marketing, and service teams, creating a more cohesive and effective customer experience. A similar shift is underway at U.S. Bank. By using customer data that is integrated across online and mobile banking, branches, direct mail, and other touchpoints, the bank has replaced fragmented outreach with more personalized, cross-channel coordination. A large credit card purchase may trigger a tailored lending offer, while retirement-related signals launch a coordinated journey that connects affluent customers with a wealth management advisor. In both cases, execution shifts from functionally optimized actions to synchronized responses across channels, driving growth. Shift #4: From Fragmented to Centralized Analytics Customer analytics have long been scattered across functions and geographies. Marketing managed campaign analytics, sales owned account planning and sales analytics, and countries used their own systems and vendors. At one company, more than 100 country organizations operated separate analytics stacks. (The company is now on a path to centralization.) Digital makes decentralization untenable. Analytics and AI demand scarce talent, strong governance, and sustained investment. As a result, organizations are moving toward centralized analytics hubs supported by shared data platforms, AI capabilities, and expert teams. These hubs may be in-house or outsourced, and located onshore or offshore. Pfizer (with whom we’ve consulted) has evolved from country-specific analytics functions to shared commercial analytics platforms, and tools. In a hub-and-spoke structure, central hubs in the U.S. and India provide core infrastructure and reusable models, while embedded teams remain in the business units to ensure local relevance. Maersk mirrors this shift. It brought numerous fragmented legacy technologies together in a unified digital platform, integrating logistics data across its network. The platform enables enterprise-wide visibility, scalable solutions, and an integrated experience for customers. For every company mentioned in this article, centralized analytics were part of the solution, enabling asset-based intelligence, faster decisions, and coordinated execution. The Road Ahead: Reframing Digital from Modernization to Impact For many leaders, the growing cost of AI and advanced analytics capabilities can feel like an unavoidable digital tax. That perception often emerges when digital investment is framed as modernization: upgrading platforms, standardizing tools, improving efficiency. Digital is treated as infrastructure, and success is measured accordingly: systems deployed, users trained, dashboards accessed. In most organizations, digital underperformance is not a failure of change management, but a mismatch between new ways of working and old organizational designs. Anchoring on the four shifts changes the narrative. Digital is not just about keeping the lights on and implementing disconnected point solutions; it’s even more about making better commercial choices throughout the entire customer journey: deciding which customers to prioritize, how to engage them, and where to deploy scarce resources. When viewed this way, success shows up not in activity metrics but in outcomes, including win rates, decision speed, and sales growth. As one financial services COO put it, the real question is: What does it do for revenue? Digital is more than a technology shift. It redefines how commercial organizations operate and decide, automating some tasks while supporting others that require human judgement and connection, especially in strategic accounts. It moves insight from answers to assets, shifting effort from producing analyses to designing, governing, and improving reusable decision tools. It compresses decision cycles, pushing choices closer to the moment of action and recasting expertise from episodic individual judgment to embedded, reusable institutional intelligence. It replaces siloed execution with coordination, driving tighter alignment across sales, marketing, service, and analytics. And it drives centralization, creating the need for shared capabilities that concentrate scarce talent, provide strong governance, and support sustained investment. When companies make these shifts successfully, digital stops feeling like a tax and starts delivering the dividend leaders expect and need.

Why Your Digital Investments Aren’t Creating Value

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