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Successful chief experience officers (CXOs) understand that great customer experiences don’t come from silos. Fast, effective support depends not only on the CX team, but also on the product teams providing technical information, the creatives packaging it into customer-friendly forms, the engineering teams building the delivery channels, and the IT ops groups who keep the whole system running. The same principle applies to AI transformation, but many CX leaders forget to apply it. In the rush to put agentic and generative AI to work, they’re overlooking the organizational factors essential for delivering measurable impact at scale. The companies achieving breakthrough results with AI aren’t necessarily those with the most sophisticated algorithms or the biggest data science teams. They’re the ones who solved the alignment problem first. THE ALIGNMENT IMPERATIVE Customer support is recognized as one of the highest-value applications for AI transformation. Generative and agentic AI can provide instant responses for customer inquiries, intelligently route complex issues to the right experts, predict customer needs before they become problems, and surface insights to improve products and processes. Advanced agentic AI systems can now take autonomous actions across multiple platforms, handling complex customer scenarios from start to finish. Subscribe to the Daily newsletter.Fast Company's trending stories delivered to you every day Privacy Policy | Fast Company Newsletters Stakeholders are generally eager to voice support for these initiatives, but there’s more to alignment than nodding along in meetings. Genuine buy-in means participating actively, even by adapting your own priorities and workflows. The importance of alignment is clear when you consider what happens without it. The customer support team identifies an opportunity to automate ticket routing and reduce response times. But as they implement, complications arise. Engineering has a massive backlog. Security flags data access risks. The product team worries about conflicting roadmap priorities. For legal, the compliance implications demand scrutiny. Days turn to weeks, then months, as momentum and C-suite mindshare fade. These are all reasonable concerns, and they’re all surmountable. But without a shared framework for evaluating these trade-offs, the AI project gets caught in a web of competing perspectives. The problem is that organizations are treating AI adoption narrowly as a technical challenge. To make real progress, you also have to address the human systems. Shared ownership models can help break silos and foster alignment. For example the models can measure success holistically, using unified metrics that reflect complete customer journeys or resolution experiences, not just departmental scorecards. STRATEGY AND IT LEADERS NEED A SEAT AT THE TABLE Getting enterprise-wide stakeholders on the same page provides a context for alignment, but it doesn’t guarantee they’ll agree about the best way forward. That’s especially true given the way AI initiatives span business and technical domains. When should engineering resources be diverted from product development to support AI integration? How do you balance data accessibility with security requirements? Which metrics should define success when AI impacts multiple business units simultaneously? Questions like these involve both business judgment about organizational priorities and technical expertise about system architectures, performance implications, and implementation complexity. The support team can’t answer the technical questions. IT can’t make the business priority decisions. Neither has overarching decision-making authority, so the implementation stalls in an endless cycle of meetings, revisions, and compromises that satisfy no one. Even within IT, technical dependencies can introduce bottlenecks. Systems integration touches multiple platforms managed by different technical teams. API access requires coordination between application owners, database administrators, and security groups. Data pipeline modifications impact everything from backup procedures to compliance auditing processes. Without a clear roadmap and top-down mandate, development work can easily bog down. High-level leadership is essential in times like these. Wielding decision-making authority beyond individual departments’, strategic leaders can reallocate resources and mandate cross-departmental collaboration. IT leaders can establish a clear technical roadmap to support and scale the AI initiative without creating security vulnerabilities, performance bottlenecks, or integration nightmares. Together, they can establish shared success metrics to account for both business outcomes and technical performance requirements. REFRAME SUPPORT AS STRATEGIC As CXOs work to build organizational alignment for their AI initiatives, they’ll also need to achieve a fundamental shift in the perception of their department’s value. Customer-facing teams aren’t just cost centers to be optimized and automated; they’re strategic assets that can differentiate your brand in an increasingly commoditized market. advertisement When customers have endless alternatives and minimal switching costs, support quality plays a big part in long-term loyalty and advocacy. Acting as the primary interface between companies and their customers, support teams possess unique knowledge about customer behavior patterns, pain points, and the nuances that determine interaction outcomes. Yet many enterprises still don’t give these teams the level of weigh-in they deserve in shaping AI strategy. They’re consulted late in the process, asked to adapt to systems designed without their input, and measured on efficiency metrics that don’t capture their full impact on customer relationships. This positioning severely limits the ability of support teams to drive AI transformation. Instead, support should be positioned as a strategic partner in the transformation process. When customer-facing teams help shape AI strategy rather than simply execute it, they identify automation opportunities technical teams would miss. They design escalation workflows that preserve relationship quality while improving efficiency, and ensure that AI enhances human judgment when needed rather than replacing it inappropriately. Realizing this potential requires treating support as a strategic capability that touches every aspect of the customer journey. THE PATH FORWARD To move beyond experimentation and drive value at scale, C-suite leaders need to treat AI transformation in CX with the rigor of any other strategic initiative, rather than as a tactical exercise in cost-saving automation. Resource allocation decisions should prioritize customer outcomes over departmental optimization. Solutions should enable enterprise scalability and customer journey integration, not just be bolted on to existing architectures. Customer-facing leaders should have authority over how AI tools are deployed in customer interactions and ensure that they’re measured based on relationship outcomes. Companies that get this right and ensure the needed organizational alignment will unlock the full potential of AI to create sustainable competitive advantage through superior customer experience. Al Martin is VP of customer experience at Forethought.
