Sprinklr Research: 92% of Organizations Claim AI Delivered Improved Customer Experience, Only 27% Can Prove It

Sprinklr, the definitive, AI-native platform for Unified Customer Experience Management (Unified-CXM), announced the launch of “Show Your Work,” a new Metric Sherpa research report produced in partnership with Sprinklr examining whether organizations can connect customer-facing AI investments to measurable customer business outcomes. Based on a survey of 807 U.S. marketing, service, and customer experience practitioners involved in customer-facing AI, the report finds a wide gap between the confidence organizations express in AI results and the evidence many can produce.

Among respondents whose organizations use AI in regular live operation, 92% said it met or exceeded expectations. Only 27% said they could quantify a result precisely using records or reports. The research also found that 85% reported gains in at least one metric category their organization did not identify as tracked before launch.

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The report examines how organizations define success, establish accountability, record baselines, isolate AI’s contribution, and use evidence to decide whether to scale, change, or stop an application. It also provides an eight-question scorecard and a practical 90-day plan to help teams strengthen the way they evaluate customer-facing AI.

“For the past several years, the question was whether organizations could deploy AI. Today, the more important question is whether they can prove it is creating value,” said Sarah Stansberry, Chief Marketing Officer at Sprinklr. “As AI capabilities become more available, simply deploying AI won’t be a differentiator. The advantage will come from understanding customers more deeply, turning that understanding into action, and proving what changed for the customer and the business.”

The AI Proof Gap

The findings show that the measurement gap often begins before AI goes live and can widen as results move up the organization:

• Only 17% of respondents said their organization assigns a named owner for AI business outcomes. Respondents with a named owner reported precise quantification nearly twice as often as those without one, 46% compared with 23%.
• Nearly one-quarter of respondents said their organizations started deploying an AI tool without first defining what they wanted it to achieve, yet 85% reported the tool met or exceeded their expectations.
• While nearly 38% said their organization reports AI outcomes with senior leadership, only 1 in 3 are doing the foundational work needed to accurately measure success, such as defining clear goals and success metrics before deployment (34%) or setting baselines (29%).
• C-suite respondents were more likely than those in managerial positions to say that AI was responsible for positive outcomes being reported. At the same time, fewer C-suite respondents said their organization records a baseline before deploying an AI tool.
• Deployments live for at least two years reported precise quantification more often than newer deployments, 43% compared with 25%. The study does not establish that time caused the difference.

The report also identifies differences by function. Marketing respondents said they could quantify results precisely more often than service respondents; 38% compared with 23%. Respondents using AI for their organization’s social media content reported widespread deployment but comparatively low use of pre-launch cost tracking and financial reporting.

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“The question we wanted to answer wasn’t whether organizations believe AI is working. Most already do,” said Justin Robbins, Founder and Principal Analyst at Metric Sherpa. “The more important question is whether they can show the evidence behind those claims. What we found is a significant gap between confidence and proof. Organizations are reporting success, but many struggle to identify the baseline, the owner, the comparison, or the records that explain how they reached that conclusion. As AI becomes more central to customer experience, leaders need a more disciplined way to separate perception from evidence.”

Five Disciplines for Showing the Work

“Show Your Work” translates the research into five measurement disciplines leaders can apply to customer-facing AI programs:

• Outcome: Define what should change for the customer and the business, beyond task speed or hours saved.
• Owner: Assign one named person who is accountable for the business result.
• Baseline: Record what performance looked like before AI and set an expected range.
• Comparison: Use the strongest feasible method to separate AI’s contribution from other changes.
• Review: Establish a regular cadence and agreed criteria to scale, change, pause, or stop.

The report recommends that organizations begin with one meaningful AI application, document what is known and missing, build a reproducible measurement brief, and conduct a 90-day review that separates observed improvement from the amount reasonably attributable to AI.

The research reflects a growing challenge for those teams: as AI becomes embedded across customer-facing operations, organizations need stronger evidence of what is actually improving and why. Sprinklr works with enterprises across marketing, service and customer experience to connect customer signals and enterprise context with the actions organizations take. The focus is on creating a continuous cycle of action, outcomes, and learning that improves the next decision.