Why AI Product Launches Demand a New Measurement Stack

Most AI product launches fail, but Jyoti Yadav's new playbook offers a solution. Learn how Loom (Atlassian) achieved success by prioritizing user trust, rigorous experimentation, and outcome-based pricing. This guide reveals why traditional metrics fall short and how to ensure your AI initiatives deliver real business value.

Worldwide AI spending is on track to reach $2.52 trillion in 2026, a 44% jump from the prior year, with generative AI alone racing past $644 billion in annual spend. Yet beneath those numbers sits a harder statistic: roughly 95% of enterprise generative AI pilots fail to deliver measurable P&L impact, and about 80% of AI projects overall fall short of their intended business value. The gap between how much companies are pouring into AI and how much value they are capturing has become the central question for product leaders shipping AI features at scale. Accuracy benchmarks and model scores, which defined the first wave of AI development, are no longer enough to predict whether a product will actually earn a place in a customer’s workflow.

Jyoti Yadav, Senior Data Science Manager at Loom (Atlassian), is one of the product leaders working to close that gap. In March 2026 she published Beyond Accuracy: The New Playbook for Launching and Measuring AI Products, a practitioner’s guide drawn from more than a decade of experience leading data science teams at Meta, Coinbase, Mastercard, and Atlassian. The book argues that AI products require a fundamentally different measurement stack, one that treats model outputs as probabilistic, user trust as a primary metric, and experimentation as the instrument for telling product signals apart from noise. Her work inside Loom’s transition from a standalone startup to part of Atlassian’s $975 million acquisition has become a case study in what that new playbook looks like when applied to a shipping product.

Where the Old Product Metrics Stop Working

The failure pattern at the center of most AI initiatives is no longer about the model itself. Roughly one in three AI projects is abandoned before reaching production, and another 28% are completed but unable to deliver measurable business value. Gartner projects that 60% of AI projects unsupported by AI-ready data will be abandoned through 2026, and the average organization now scraps 46% of its AI proofs-of-concept before a single customer ever sees them. The underlying issue in most of these failures is organizational and measurement-related: teams define success too narrowly, evaluate features on offline benchmarks that do not reflect real behavior, and discover too late that their metrics were never the right ones.

That pattern is familiar territory for Yadav. At Loom, her team led the analytical groundwork behind the AI Suite launch that later became the anchor of the company’s post-acquisition pricing strategy. Rather than scoring the AI features on classical accuracy alone, her team instrumented how users actually behaved around auto-generated titles, summaries, and chapter markers. They tracked how often users kept the AI-generated metadata without editing it, how that adoption translated into faster video creation, and how the savings changed the economics of a recording session. Sixty-seven percent of users adopted auto-generated titles and summaries without manual edits, and users produced and shared videos 60% faster, numbers that held up because they were designed to reflect live user behavior rather than model output quality in isolation.

"Accuracy tells you how well a model performs on a test set, not whether a product will earn a place in someone’s workflow," Yadav says. "Once you accept that AI products are probabilistic, you have to measure the probability that a real user will accept the output, keep it, and come back for more. That’s a different discipline."

Experimentation Becomes the Backbone of an AI Launch

The A/B testing and experimentation software market reached around $904 million in 2026 and is projected to grow at roughly 11.5% annually through the middle of the next decade. Adoption among large enterprises now sits near 72%, with more than half of organizations running over ten experiments a month. The shift reflects a deeper change in how product teams think about AI. When outputs vary from one user to the next, the only reliable way to evaluate a feature is to compare it against itself under controlled conditions and measure how real user cohorts respond.

Inside Loom, Yadav led a team of six data scientists through an end-to-end pricing and packaging overhaul that hinged on exactly this kind of experimental rigor. Each AI feature in the new premium tier was evaluated through a sequence of tests, from early qualitative signal to full-traffic randomized rollouts, before it was priced into the product. The launch drove a $2.85 million increase in annual recurring revenue and shifted a meaningful share of Business-tier users into the new AI tier, but the commercial result rested on a long chain of experiments that proved each feature was changing user behavior rather than decorating the interface. Her team also defined the metrics that governed whether a feature shipped at all, closing the loop between model performance, user adoption, and the dollars the tier was expected to carry.

"Experimentation has to run as the spine of an AI product launch," Yadav explains. "Every decision about what to build next, what to deprecate, and what to charge for has to tie back to a test where the answer could have gone the other way. Otherwise you’re shipping on taste and calling it strategy."

Pricing an AI Product Means Measuring the Outcome

The economics of software have started to look different. Seat-based subscriptions, which carried SaaS for two decades, are giving way to hybrid and consumption-based models as AI-driven features carry real marginal compute costs. Salesforce’s Agentforce business reached nearly $800 million in annual recurring revenue in its first full year, growing 169% over the prior period, while most AI-native companies now build their pricing around tokens consumed, workflows executed, or outcomes delivered. For product leaders, the question is no longer just how to price a tier but how to quantify the value each AI interaction actually creates for the customer.

Yadav’s work on Loom’s pricing strategy sits directly in that transition. Her team partnered with finance and product to redesign the way the company’s AI features were packaged, landing on an AI-plus tier that bundled automated titles, summaries, and chapter markers into a premium offering. The tier went live inside Atlassian’s enterprise billing systems, which opened Loom up to the company’s 200,000+ customer base under standardized roles and compliance controls. Her recognition as a Senior Member of the Institute of Electrical and Electronics Engineers reflects the technical depth behind that work, a designation awarded to professionals with a substantial record of significant contribution in their field.

"Pricing an AI feature ends up being a measurement exercise before it’s a packaging one," Yadav notes. "You have to know what the feature is worth to a user before you know what a customer will pay for it, and that only becomes clear once you can quantify the outcome the feature is driving. Everything else is guesswork dressed up in a price sheet."

Trust Becomes the Final Checkpoint Before Scale

The International AI Safety Report 2026, authored by more than 100 global experts, found that advanced AI systems remain "jagged" in their performance, excelling at complex benchmarks while still failing unpredictably in routine interactions. Enterprises have begun treating those inconsistencies as a product risk rather than a research problem. A recent BCG survey found that 60% of companies generate no material value from their AI investments, and a majority of customer-facing teams now track hallucination rates, groundedness scores, and workflow-level reliability alongside traditional engagement metrics. Observability vendors have spent the past year acquiring AI-specific evaluation startups, a signal that reliability has become a procurement criterion rather than an internal science question.

That shift is the connective tissue of Yadav’s book. Beyond Accuracy argues that trust is not a soft outcome to be measured after launch but a design parameter to be engineered into one, through instrumented feedback loops, continuous evaluation, and measurement frameworks that treat model reliability, user adoption, and business impact as co-equal pillars. The work she led at Loom maps directly onto that thesis. Auto-generated summaries were evaluated not just for linguistic quality but for how frequently users kept them, shared them, and relied on them under real-world conditions. The same framing now informs how she coaches teams outside Atlassian, including through her guest lectures on launching and measuring success for AI products.

"The real work on an AI product starts once the model ships," Yadav says. "Earning the right to keep using it is what separates the features that compound from the ones that quietly get turned off. Trust is the last mile of the stack, and it’s the only one where the returns keep adding up."

The Data Science Role Is Being Rebuilt Around AI Readiness

Employment forecasts for AI product and data science roles are outpacing most of the broader tech economy, with AI product management positions projected to grow at more than a 20% compound annual rate through the rest of the decade. The pattern is visible in compensation too: AI-focused product managers now earn roughly 35% more than their traditional peers, and demand for professionals who can translate model behavior into product strategy has become one of the tightest talent markets in enterprise software. Even so, 75% of employers still report struggling to find qualified candidates for those roles.

That talent gap is part of why Yadav’s work has drawn attention outside the walls of Atlassian. In March 2026 she served as a Session Chair at the 2nd International Conference on Information Technology and Artificial Intelligence (ITAI 2026), hosted at Lasell University in Massachusetts, where conference proceedings are published in the Scopus-indexed Springer series Lecture Notes in Networks and Systems. Her session drew academic researchers and industry practitioners working on the same problems her book addresses: how to evaluate AI systems, how to measure their deployment readiness, and how to build governance structures around model behavior that is, by design, never fully deterministic. The conference role is one of several 2026 judging and program-committee appointments she has taken on as her work has reached a broader audience.

"The data scientist of five years ago wrote models. The data scientist of today has to define what good looks like before a model even gets built," Yadav reflects. "That means owning the evaluation framework, the experimentation design, and the business case at the same table. The role has grown, and the people who can do it well are rarer than any model."