AI’s integration into digital customer experience relies on business objectives and technical infrastructure
At the core, integrating AI into digital customer experience isn’t just about slapping a layer of new tech on top of what you already do. It’s about aligning your business goals, revenue growth, customer retention, market capture, with the actual systems and stacks that drive your operations. That means marketing, sales, IT, customer service, all of it, working in one direction, off the same foundation. Otherwise, you’re just accelerating in the wrong direction.
In most companies, leadership comes in from different angles. Revenue officers, CMOs, and business heads typically ask: “How do we capture more value? Retain more customers? Build loyalty that spreads?” And those questions are valid. At the same time, CIOs and digital teams are focused on how to lay down the infrastructure that supports these goals. They want to stay modern, agile, and resilient, because today’s tech advantage becomes tomorrow’s minimum requirement pretty fast. Calvin Cheng, Director at West Monroe, highlights this convergence well. The message from various leaders is clear: create a modern customer experience that grows the bottom line. But to get there, business horsepower and technical capability need to be locked in tight.
To unwrap this for the C-suite: if your teams are talking past each other, strategy on one side, technical execution on the other, you’re stuck. You won’t scale AI use cases properly, and you’ll just burn cycles integrating systems that don’t play nicely. Alignment must start at the top. Revenue and infrastructure are two sides of the same coin here.
The upside? Getting it right opens up real competitive advantage. When systems across customer experience are unified, marketing automation, CRM, transactional systems, customer support, you create a feedback loop of insight and opportunity. Data flows. Decisions get sharper. Customer impact gets measurable. That’s when AI actually adds value.
Siloed data and disparate applications are a major barrier
Most organizations are sitting on top of data trapped across platforms, email marketing systems, CRMs, ERPs, customer service databases, all operating on different rules, different access, and no real connection. So when leaders wonder why customer churn is rising or why retention efforts aren’t paying off, the problem is the lack of a clear lens into customer behavior.
Each department builds its own stack. That’s common. Marketing collects engagement data. Sales stores deal histories. Customer service tracks case outcomes. Finance logs transactions. But the problem is, none of these systems talk to each other. This was reflected in one specific case mentioned by Calvin Cheng, Director at West Monroe. He described a large pharma client whose data was spread across systems, making it nearly impossible to deliver a consistent experience regardless of whether the customer was touching sales, service, or marketing. With no unified customer profile, your AI doesn’t have a complete picture to work with.
For C-suite leadership, this means decisions are being made with partial information. The CFO sees declining lifetime value but can’t trace the root cause. The CMO sees high churn but doesn’t have full data on customer sentiment or service touchpoints. The sales team struggles to nurture leads they don’t completely understand. When data is siloed, you’re limiting the business’s ability to act intelligently and at scale.
Fixing this depends on organizing data, governing it, and connecting it across systems. When your organizational structure dictates your technology foundation, you end up building more complexity. Break the loop. Siloed applications lead directly to siloed insights. And if AI is going to work for your customers, giving them the seamless, personalized experience they expect, you need data that flows laterally across the business.
The investment here should focus on integration, governance, and common data architecture. Clear ownership of data quality and structure is what allows AI to move from promise to precision. Without that, you’re plugging AI into fragmented systems with incomplete inputs, which gets you scripted automation, not intelligence.
A phased approach is key for successful AI deployment
Too many companies rush into AI expecting immediate impact. That leads to wasted budget, disconnected pilots, and tech that doesn’t scale. A smarter strategy is incremental: start with clear, high-value use cases that solve real business problems or improve specific customer experiences. Then build from there. This isn’t just about testing ideas, it’s about building internal momentum based on proof.
Calvin Cheng, Director at West Monroe, described this as a “crawl-walk-run” path. It begins with identifying the right problem to solve, something that delivers value to the organization or the customer, and then tracing what data and workflows must be aligned to execute it end-to-end. AI effectiveness hinges on proper workflow integration, and that doesn’t get resolved with one-off deployments or a single platform. Testing small, learning fast, and scaling deliberately is the most reliable path forward.
This strategy lets leadership see real use case validation without needing to overhaul everything at once. It’s pragmatic. It also helps teams understand what parts of their existing tech stack support or block progress. Once you’ve got one success, expanding AI to other functions or customer touchpoints becomes far less speculative. You’ll know what it takes, in data quality, integration, orchestration, and change management, because you’ve already done it once.
And that’s key: success in AI means having the right starting point and the operational rigor to scale it. Use cases need to be chosen carefully, not based on what vendors advertise, but based on what creates forward business movement. Whether it’s automating a customer support experience, personalizing recommendations, or speeding up insights to frontline staff, the focus should be on measurable outcomes.
For the C-suite, this means shifting the conversation from “What AI should we buy?” to “Where can AI add value right now, with what we already have?” That requires alignment between technical feasibility and commercial impact. Without both, even the best tools stall out. Start precise. Amplify later.
The push for AI adoption is often fueled by FOMO rather than a strategic plan
A lot of organizations are jumping into AI because they feel they have to, not because they’re prepared. That’s the reality. There’s pressure from boards, media, investors, competitors. Everyone wants to show they’re “in the game.” But implementing AI without a plan sets you up for misalignment, overspending, and solutions that create more complexity than they solve.
Calvin Cheng, Director at West Monroe, highlights this well. He points out that top executives often reach for generative AI because it’s seen as the next big lever. The interest is real. The expectations are high. But the underlying work many companies haven’t done, data assessment, system compatibility, training models on accurate internal datasets, gets skipped. Cheng’s team counters this gap by leading with a product mindset: run diagnostics on what data is available, prototype simple use cases, and then demonstrate value as quickly as possible.
That prototype isn’t a polished solution. It doesn’t need to be. The point is to ground AI conversations in real output. Can this tool help marketing deliver faster segmentation? Can it help sales respond with better targeting? Can customer service resolve tickets automatically and accurately? Those are the early wins that prove capability, reduce risk, and build executive trust in the process.
The nuance for C-suite leadership is this: FOMO can be used as a positive force if it accelerates strategic clarity instead of noise. But it demands discipline. Letting hype drive adoption means you’ll apply AI where it’s most visible, not where it’s most valuable. That distinction matters.
True progress starts with hard questions: What data do we control and how clean is it? What outcome are we trying to improve, and what are the blockers? And who owns success? Until those answers are clear, no AI system—no matter how advanced—is going to transform the way your business delivers value. You don’t win by being first. You win by being deliberate and scaling what works.
Agentic AI is evolving to provide greater autonomy in creative processes and workflow execution
We’re entering the phase where AI systems are generating content, reasoning, making decisions, and triggering actions within workflows. Agentic AI, powered by large language models (LLMs) and advanced algorithms, is moving beyond content production to become a tool for handling tasks that require logic, sequence, and context. That’s a shift from outputs to outcomes.
Calvin Cheng, Director at West Monroe, points out that the real potential lies in the reasoning engine behind these systems. It’s about what the system can do with those ideas, analyzing internal data, making recommendations, and initiating processes without requiring constant human input. This kind of AI can unlock significant acceleration across knowledge work, customer experience flows, and even certain operational tasks. But it’s not plug-and-play.
The key threshold is trust. Businesses will increasingly need to ask: when can AI act without a human in the loop? If a workflow is repetitive, predictable, and low impact, giving AI more responsibility is an easy decision. But if the stakes are higher, legal implications, brand risks, or sensitive customer experience touchpoints, oversight becomes non-negotiable. The risk tolerance of the process should guide the level of automation, not the capabilities of the model itself.
For decision-makers, the play here is measured deployment. Identify processes where high-frequency decisions are taking up valuable time, and test how AI can take over without degrading quality. Then expand. It’s about using AI to remove friction from the tasks that don’t require human judgment. And when reasoning engines get good enough, they can become another layer of intelligence across the business, always working, always optimizing. That’s where the real compounding value starts to build.
Most organizations are lagging behind the latest AI innovations, leading to a competitive gap
Most enterprises are behind AI leaders in terms of deployment, infrastructure readiness, and capability. While technology vendors keep pushing the next generation of agentic AI tools and promising real-time automation, personalized customer journeys, and adaptive reasoning engines, the majority of organizations are still trying to centralize their data and define basic use cases. There’s a clear gap between what’s being marketed and what’s actually possible within most companies’ current environments.
Calvin Cheng, Director at West Monroe, observes an average lag of about two years between vendor developments and enterprise adoption cycles. The technology is advancing faster than organizations can restructure teams, replatform systems, and modernize data governance. And many leaders are still focused on retrofitting AI into existing processes rather than redesigning for it. The ambition is high, but organizations aren’t always asking the tough questions needed to get practical.
Executives need to understand: AI implementation won’t be repeatable across businesses. There’s no blueprint to copy. Your organization has its own tech debt, data maturity level, operational complexity, and customer expectations. Large platforms might offer out-of-the-box tools, but real competitive value comes from customizing those capabilities to your business model. Leaders need to commit to internal assessments that go beyond surface-level AI workshops and dive into real constraints—data readiness, internal ownership, workflow fit, and ongoing maintenance.
There’s also a temptation to chase every demo and new feature roll-out, but that creates distraction. The core issue is catching up with your own potential. Most organizations already have data, platforms, and systems that aren’t being fully utilized. Instead of waiting for perfect readiness, C-suites should prioritize initiatives that unlock value quickly and build repeatable patterns for AI success.
Moving forward, the winners won’t just be the fastest adopters, they’ll be the ones that execute deliberately, with clear alignment between their data foundation, operational needs, and AI ambition. The path isn’t straight. But that’s the gap worth closing.
Key executive takeaways
- Align business and tech from the start: Executives must ensure business and technology leaders are working toward shared customer outcomes to unlock ROI from AI investments across digital experience channels.
- Break down data silos before deploying AI: Disconnected platforms and fragmented data block meaningful AI usage. Leaders should invest in data integration to build a complete customer view that enables intelligent decision-making.
- Start small with high-impact AI use cases: Launch AI through targeted, measurable pilots tied to clear business problems. This phased approach reduces risk and builds confidence while surfacing infrastructure gaps early.
- Don’t let FOMO drive your AI strategy: AI efforts should begin with prioritized diagnostics and prototypes focused on business value—not hype. Ensure initiatives are guided by operational readiness and strategic impact.
- Automate where risk is low and repeatability is high: Deploy agentic AI in well-understood workflows where error tolerance is acceptable. Gradually increase automation as trust in the AI model and its reasoning improves.
- Close the gap between ambition and infrastructure: Most organizations trail AI innovation by years. Executives should focus on internal alignment, actionable use cases, and leveraging current assets before chasing next-gen features.
