Introducing AI/Run:
Your End-to-End Infrastructure for Enterprise AI Adoption

Why are so many enterprises running copilots, agents, and pilots, but still failing to scale AI across workflows? Most aren’t stuck at the tech layer but at the execution layer. AI/Run was built to break this pattern with unified governance, integration scaffolding, cultural activation, and performance visibility needed to build enterprise-wide AI-native operations.

The old idea of “first you experiment, then you enable, then you scale” no longer reflects what’s happening on the ground. Most enterprises are in all three stages of AI adoption at once. Most have reached the AI-engaged stage with copilots, scoped agents, and PoCs built into delivery workflows. While a smaller set has entered the AI-enabled stage, wiring role-based agents into sprint rituals to reduce effort and improve velocity.

Yet very few can make the jump to the AI-native stage, where AI is orchestrated across the SDLC with governance, telemetry, and reliability.

Read more: The Three Stages Every Enterprise Must Pass Through In Their AI Adoption Journey 

In the last two years, we’ve seen this stall repeat across enterprises. Pilots stay trapped in tool silos, experiments happen without coordination, and vendor platforms operate without shared governance, integration strategies, or visibility. Independent research now echoes this. MIT, after studying 350 employees and reviewing 300 enterprise AI deployments, reports that 95% don’t produce measurable returns. Not because the technology disappoints, but because the organization has no way to carry early pilots into governed, scaled adoption.

AI/Run was built to break that stall point. Developed from 100+ enterprise programs, it’s a production-grade execution and scaffolding layer that lets enterprises: 

• Move beyond isolated experiments into integrated delivery workflows
• Align engineering, platform, and security teams under one AI operating model
• Modernize legacy and high-debt systems with AI safely and incrementally
• Track adoption, performance, and ROI across roles, teams, and locations
• Accelerate AI maturity without creating cultural, security, or technical debt. 

This infrastructure is already powering measurable gains for enterprise clients. EBSCO, the global information services company used AI/Run to pilot GenAI workflows across 10 teams and then scale to 900+ developers. The framework enabled role-based adoption, automated performance metrics, and a centralized knowledge repository. As a result, engineering velocity increased by 10%, pull requests doubled, and teams were able to save ~50 minutes per day.

We put it to the test continuously and within weeks, saw smoother cross-functional handoffs, consistent adoption patterns across teams, and real-time visibility into what was working and where it was stalling. Here’s how it has changed AI adoption journey:

Reducing people resistance with bottom-up cultural transformation

AI maturity is not achieved through central policy or bulk tooling rollout. Still, many organizations assume that licenses and mandates from the top C-suite will drive adoption. The real adoption engine sits with developers, testers, analysts, and product teams, and that layer is fractured.

Some teams lack role-specific fluency with copilots, agents, and workflows, so usage stalls early. Others start strong but lose momentum because there are no feedback rituals or reinforcement structures. Over time, culture adds its own drag where AI feels like a boardroom directive rather than a capability teams own. The result is resistance, silent drop-offs, and no clear path beyond basic copilots.

AI/Run tackles these human bottlenecks across three dimensions:

• Skills that actually translate into adoption: Instead of broad enablement sessions, AI/Run delivers role-based curricula, Day-1/Day-30 playbooks, prompt catalogs, and embedded IDE micro-labs. Developers, QAs, BAs, architects, and PMs receive task-specific guidance tied to their daily workflows. Each geography and function has AI Champions who mentor teams and localize adoption. 

• Behaviors that survive the “week two drop-off”: Most pilots lose momentum when novelty drops and uncertainty kicks in. AI/Run builds reinforcement into delivery cycles using sprint nudges, reliability signals, manager toolkits, and checkpoint scorecards. Local champions inside squads normalize daily use and prevent quiet abandonment.
• Adoption momentum that leadership can see and scale: From the start, AI/Run instruments the rollout with dashboards that show retention by role and team, abandonment signals, and sprint-level usage patterns. It helps leadership decide which teams to double down on, where to reallocate licenses, and which use cases are ready for scale. 

This is exactly how a global enterprise like Wolters Kluwer enabled skill readiness and cultural adoption at scale, AI/Run was used to enable 6,000+ employees across regions. Over 800 senior leaders participated in targeted strategy workshops, 4-hour team learning series established bottom-up alignment, and AI champions were trained to drive peer-level adoption. Within weeks, 98% of participants said the training applied directly to their work, and confidence in using AI jumped to 84% –- a clear shift from experimentation to embedded practice.

Improving processes integrity by standardizing AI across organization

AI only creates value when the delivery pipeline is built to support orchestration, compliance, and reuse. Dropping copilots and agents into legacy workflows without shared standards can only lead to tool sprawl, weak alignment with CI/CD, version control, and testing. Worse, every new agent restarts security and legal reviews because there are no vetted blueprints or approved vendors to lean on. 

The real damage, however, appears downstream. Without telemetry, evaluation harnesses, or lifecycle governance, most prototypes stay disposable, break on upgrades or vanish after one deployment. When this plays out across dozens of teams, the problem stops being technical and becomes financial. 

AI/Run prevents that collapse by wiring AI into delivery through three integrated engines: 

• Unified development ecosystem as the governed foundation: AI/Run provides reference architectures, platform selection matrices, pre-vetted vendor shortlists and black-box evaluation harnesses. Sandboxes with masked or synthetic data lower integration risk, while environment templates plug directly into GitHub/GitLab, Jira, Slack, Confluence, and CI/CD tools. Because the blueprints are pre-approved, compliance reviews and vendor onboarding move faster.
• Integrated delivery workflows that eliminate SDLC breakpoints: AI/Run wires AI into the delivery motion instead of leaving it on the edges. BA → Dev → QA → Ops handoffs improve with AI/Run vetted issue-to-PR templates, agent-driven test generation, and automated regression suites sit inside the flow. Multi-agent orchestration coordinates work across functions, while CI triggers and policy-as-code ensure every action maps back to tickets, commits, and approvals.

• Centralized knowledge management for context intelligence: AI/Run turns scattered engineering context into a shared intelligence layer. Documentation, delivery assets, code history, and architectural patterns sit in one governed system, improving how LLMs interpret intent and reducing rework and hallucinations. Teams can use this knowledge portal to deploy agents across Jira, Slack, GitHub, Confluence, and CI/CD through secure connectors. It also ensures every win becomes reusable across teams and regions instead of staying local.

PostNL, a national postal and eCommerce provider, applied this model to unify AI across development stages. AI/Run helped deploy 20+ agents for user stories, test creation, code review, and documentation automation across the SDLC. Within months, teams cut manual test case creation time by 80%, reduced user story generation time by 75% and decreased manual documentation work by up to 90%. The impact showed up in faster releases, cleaner handoffs, and greater production confidence.

Driving performance by turning adoption into ROI for execs 

Getting engineers to use copilots and build agents is no longer the hard part. Proving the value of those investments to the C-suite is. CFOs and CEOs aren’t impressed by clever copilots or isolated productivity anecdotes when budgets are tightening and customer demands are rising. 

They want to see whether licenses, pilots, and agent platforms are producing measurable business returns in terms of reduced churn, high cycle time, and faster releases. But with licenses underutilized and pilots scattered across teams, there is no clear way to attribute gains to spend. That is one of many reasons why investments get cut even when adoption looks promising. 

AI/Run closes this execution gap by connecting day-to-day adoption with delivery outcomes and financial impact: 

• Making adoption visible and defensible: Instead of tracking licenses or login counts, AI/Run dashboards show retention by role, team, and geography. Leadership can see which groups are driving real impact through improvements in the number of agents created or copilots used. 

• Converting usage into measurable performance: AI/Run links tool and agent usage to delivery velocity, time-to-market gains, and task completion rates. Teams can see where copilots and agents are accelerating execution, and leaders can compare uplift against historical baselines across squads and functions.
• Creating one version of value leadership can fund: Instead of anecdotal updates, AI/Run provides attribution models and executive dashboards that quantify outcomes. This gives decision-makers a single-pane view of what to scale, what to fix, and what to retire without politics, guesswork, or conflicting reports.

One of our financial and analytics enterprise clients used AI/Run to scale GenAI adoption across 80 teams and 440 engineers, but more importantly, they could prove its return through data-driven dashboards with 360-degree visibility into AI adoption, extent of automation and productivity gains. Within a year, AI-assisted delivery drove $1.7M in net benefits with 30% faster development cycles. 

Building AI-native enterprises with AI/Run

This is the now-or-never moment for enterprise AI. With so many initiatives being quietly withdrawn, the line between scaling and stalling lies in whether organizations can unify adoption into one operating fabric. AI/Run delivers that foundation, drawn from EPAM’s proven adoption patterns across industries.

For organizations unsure how to break past pilot purgatory, the first move is clarity. An AI/Run readiness walkthrough surfaces where you stand today and shows how quickly your teams can progress toward AI-native ways of working.