The CX Leader’s Guide to 2027 Budget Planning: Invest to Scale Your AI Foundation

Under pressure from leaders who demand clearer proof that customer experience teams maximize their business impact, CX leaders enter the budget planning cycle for 2027 determined to scale artificial intelligence. Winning CX leaders will strive to build AI-powered CX systems that turn signals into consistent action; CX leaders who focus on short-term interventions without fixing underlying processes, meanwhile, will struggle.

Most CX decision makers expect to increase CX investment in the next 12 months, with gains typically in the 5 percent to 10 percent range, and about 1 in 10 are expecting increases by more than 10 percent, according to Forrester’s 2026 global Budget Planning Survey. CX leaders plan to increase spending on technology, initiatives, and data, but they risk underfunding the resources required to turn those investments into impact, with budget cuts steepest in the personnel and services categories.

Optimize Spending to Build AI-Powered CX Systems

Even amid growing budgets, organizations are at risk of scaling fragmented data, inconsistent processes, and weak CX foundations. To succeed, CX leaders should fund core capabilities such as connected data; cut spending on outdated and low-value activities; and experiment with AI-driven ways to enhance decision making.

Here are selected recommendations from Forrester’s 2027 Budget Planning Guide for CX leaders.

Invest in Systemic CX Execution

AI increases the speed at which organizations can scale both value and dysfunction. Teams can build consistency and prevent rework by investing in these areas:

Cross-functional CX governance and alignment. Establishing disciplined planning and prioritization cadences that bring the right decision makers together is more important than ever. Even companies with highly customer-focused employees flounder when teams define customers, priorities, and success differently, a problem that AI can magnify.

Customer insight capabilities that turn signals into action. Build teams that combine data analytics skill and CX expertise. Focus investment on helping these teams connect fragmented data from operational, behavioral, conversational, and other unsolicited signals.

Decrease Spending on Band-Aids and Dependency Loops

As AI accelerates execution, inefficiencies scale faster and become harder to unwind. Eliminate initiatives like the ones below, which create activity without improving outcomes:

Data centralization pipe dreams that require heavy integration before payoff. Stop chasing the familiar promise that “Once we centralize all customer data, we’ll finally fix CX.” Cut programs that need multiyear integration to deliver value. Integrate only the data required to support high-value decisions.

CX dashboards that fail to drive decisions. Stop funding dashboards featuring “always-on” generic surveys. These programs cater to score-obsessed organizations but lack the quantified root-cause and financial-impact insights required for action. Shift investment toward analytics capabilities that generate actionable insights and the skills to communicate them.

Experiment With Emerging Tech and Methods to Build Future CX Capabilities

AI is expanding what CX teams can build, but it’s not clear what will create value. Experiments like these can reduce risk, prove impact, and earn the right to scale investment:

Using synthetic data as hypothesis generators. Fund experiments to test how and when synthetic data can reliably inform decisions. For example, use synthetic personas alongside real customers for concept testing to assess output quality.

Measuring the impact of AI-powered interactions on customer and business outcomes. Partner with teams deploying customer-facing AI to shift the focus from stand-alone AI-driven interactions to the customer journeys and goals they support. Define up front which metrics to capture in the AI-assisted journey and how to do so. Move beyond customer satisfaction score and the Net Promoter Score (NPS) to a fuller measurement set that captures AI’s value across the journey.

Avoid Spending That Increases Fragility

Many organizations still fund projects that seem like progress but actually increase risk when they forgo foundations. Avoid funding anything that scales before the underlying system can support it:

“Turning on AI” as a CX strategy. Don’t deploy AI before your foundations are ready. For example, AI-driven insight generation and personalization efforts often fail when customer understanding relies on siloed, survey-led signals instead of holistically connected datasets.

Ditching packaged CX analytics for LLMs prematurely. Despite advances in machine learning and generative AI, achieving consistent accuracy with general-purpose large language models (LLMs) in text mining remains a challenge. For large enterprises that require explainability and risk mitigation, purpose-built analytics solutions remain the better option.

Senem Guler Biyikli is an analyst at Forrester Research. Biyikli leads user experience research, advising companies on research practices and tools, scaling research teams, and user experience best practices.