Experimentation has become an essential part of how brands optimise customer experiences. Teams today are eager to experiment more and optimise the full customer journey — from acquisition to retention to loyalty. But for many brands, succeeding at and scaling experimentation has proven difficult, bogged down by manual processes with limited resources, siloed insights and unclear returns.

In addition to technical challenges, many teams at organisations that run A/B tests across channels and surfaces find it difficult to know if similar tests have been executed before, why their highest performing tests are winning or how to prioritise their testing strategies. They are eager for a solution that can bring all their data and testing insights together to align their teams and improve the speed and scale of learning, sharing and optimising across their experimentation programmes.

Adobe’s next generation of experimentation.

We are excited to announce the launch of Adobe Journey Optimizer Experimentation Accelerator, an AI-first application that enables growth marketers, product managers and experimentation teams to work together. In one centralised workspace, they’ll be able to prioritise experimentation strategies, increase predicted lift (revenue) and conversion and scale experimentation learnings.

Powered by the Adobe Experience Platform Experimentation Agent, this new application automates experimentation analysis while reducing manual effort, so teams can:

• Discover what works and why: Quickly design better experiments by revealing valuable content, audience and behavioural insights that explain why tests succeed or fail.
• Generate high-impact test ideas: Recommend new test opportunities ranked by predicted performance to drive greater lift and conversion.
• Improve active experiments: Quickly iterate testing for active experiments and optimisation cycles to help accelerate business growth and KPI attainment.
• Centralise global experiment trends: Monitor and share learnings across teams with governance and stakeholder summary reports.

Journey Optimizer Experimentation Accelerator integrates seamlessly with Adobe Target and Adobe Journey Optimizer experiments to significantly improve the impact of existing testing and journey orchestration workflows. This integration acts as a force multiplier for teams to optimise the entire customer journey.

Prioritise experimentation strategies.

While teams can often tell whether a test has won, few have the time and expertise to uncover why it worked. Closing that gap requires data analysts, product managers and optimisation teams to perform time-intensive, manual analysis, which slows down decision-making and shared learnings. In most cases, insights remain buried in spreadsheets or scattered across slide decks, making it hard to prioritise strategies or replicate success.

Experimentation Accelerator changes the equation by using AI to analyse experience variants from past and active tests and automatically detect optimisation patterns. These patterns are surfaced as AI Experiment Insights that are correlated with experiment, journey and audience attribute performance. Teams can quickly access and share clear, human-readable insights that explain not only what components of the experiment worked, but why it worked and for whom.

For Adobe teams running experiments on Adobe.com, this approach proved transformative.

With more contextual insights and a clearer rationale for performance, teams gain a better sense of where to start when designing their next test. This results in smarter, faster decision-making — and understanding of overall experiment impact to core business goals and KPIs.

Identify high-impact opportunities for growth.

Not knowing what to test next or what experiment changes to make next can significantly stall progress. All too often, teams rely on instinct or ad hoc brainstorming, which leads to missed opportunities like optimising onboarding flows, personalising content based on member benefits or other key interactions.

With Journey Optimizer Experimentation Accelerator, teams can identify growth drivers using AI Experiment Opportunities to connect experiment insights data and strategic business goals. These AI-generated opportunities are new test ideas, which are ranked by their predicted lift and conversion values. They are dynamically created by continuously learning from a brand’s entire ecosystem of experiments.

For example, a financial services and insurance brand might see suggested test ideas that highlight specific content with member benefits to show how social proofing can affect call-to-actions (CTAs) or provide multiple engagement pathways (such as online quotes, phone calls and scheduled consultations) for overall marketing strategies to drive higher probability of lift and conversion. The opportunity evaluation view in the product provides quick guidance on potential learning impact by comparing new test ideas against past performance — while rationale and evidence-based examples explain why a suggested test matters.

Growth marketers, product experts and experimentation teams can leverage these new agentic AI workflows to gain data-driven clarity on what to test next and how to improve the overall win rate of experiments — enabling faster, more impactful business growth.

Accelerate optimisation on active experiments.

Scaling traditional A/B testing methods often require larger sample sizes and lengthy wait times to reach statistical significance. This can significantly limit how many experiments teams can run and how quickly they can learn. As customer experience orchestration evolves to dynamically personalise experiences for each individual at every touchpoint, teams need a way to activate new generative AI content and quickly iterate for continuous optimisation.

Journey Optimizer Experimentation Accelerator introduces AI Adaptive Experiments, a new approach to running smarter tests across campaigns and customer journeys. AI models enable teams to activate AI-suggested test ideas within active experiments on the fly, using human-in-the-loop workflows. These workflows continuously adjust and validate test variants or treatments in Adobe Target or Journey Optimizer — improving incremental lift without sacrificing statistical accuracy. AI Adaptive Experiments give teams the power to take advantage of generative AI and new test ideas with reduced sample sizes needed to validate results. By building this into existing experimentation workflows, this rapid iteration method allows practitioners to test more, learn faster and focus on what drives results.

Centralise experimentation management.

As experimentation expands across an organisation, so do the challenges of managing it. Results are often siloed and stored in disconnected systems, making it difficult for new teams to learn from past work, track which tests are active or demonstrate measurable contribution to business KPIs.

Journey Optimizer Experimentation Accelerator provides teams with centralised management and features to encourage shared learnings — acting as an experimentation hub where teams can manage, organise and scale their testing programmes. A centralised catalogue of experiments across both Adobe Target and Journey Optimizer provides smart filtering and customised tagging. This catalogue offers self-service visibility into what was tested, how it performed and what was learnt to reduce redundancy and encourage reuse of proven strategies. Teams no longer need to spend hours manually pulling together personalised reports or results for stakeholders. They can now easily export experiment results into presentation-ready views tailored for cross-functional learnings and stakeholder updates.

A new era of experimentation and AI.

As data and AI continue to advance, experimentation has become one of the strongest differentiators for brands. This era of experimentation and AI is unlocking new methods of scaling insights, accelerating growth and embedding intelligence into every step of the customer journey. With Adobe Journey Optimizer Experimentation Accelerator, brands can finally unlock the full potential of their data across the organisation — turning each experiment into a driver of growth.

Learn more about Adobe Journey Optimizer Experimentation Accelerator, and how agentic and generative AI are redefining experimentation and optimisation.

As Senior Product Marketing Manager at Adobe, Brent brings data-driven solutions to market, shaping the next generation of agentic AI products for customers and partners. Having 12+ years of experience in CXM and MarTech industries, Brent is passionate about solving complex optimisation challenges and champions experimentation as a driver of growth—empowering engineering, product and marketing teams to innovate faster, compete smarter and reimagine customer experience orchestration.

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