By Tony Rost
Global Lead, Managed Services and Data
Logic, Part of Accenture
Why retailers of all sizes need more than maintenance from their managed services
Around the world, retail IT teams are being asked to do much more with much less. They’ve been tasked with optimizing & maintaining existing systems while simultaneously building new functionality, all without expanding budgets or teams.
As a result, the industry is rethinking its expectations for managed IT services. Keeping core systems running efficiently still matters, of course, but mere maintenance is no longer enough. Retailers need their run investments to actively create capacity for change.
This shift reflects a broader operating-model evolution often referred to as “Run to New.” The idea is that the purpose of the Run state in enterprise architecture is not just to maintain systems, but to continuously enable transformation. This isn’t some theoretical future concept. Right now, our team is helping retail IT teams tap new AI technologies to modernize their stacks and lower operational costs, freeing up more resources for innovation. In other words: yes, you can improve stability and free up capacity at the same time. In this blog, we’ll explain how.
→ Get The New Retail IT Operating Model ebook
Why retail IT struggles (and how to fix it)
Retailers can easily spend 70-80% of their overall IT budgets* simply “keeping the lights on.” This unending survival mode is made all the more difficult thanks to some unique contours of the retail IT landscape, like:
- Comparatively small IT budgets compared to other industries
- A tech-stack blend of legacy on-premise systems alongside newer cloud or SaaS modules (think of merchandising on-prem with cloud native POS)
- High operational volatility driven by seasonal peaks, promotions, and supply chain disruptions.
- Small, cross-functional IT teams wearing multiple hats—from break/fix support to project delivery.
With merely 10-20% of budgets going to change and growth, retailers have found themselves in a perpetual game of catch-up with the always-evolving marketplace. This imbalance breeds accumulating technical debt, extended release cycles, and mounting frustration among IT leaders desperate for greater agility and fewer operational headaches. While traditional managed services can deliver occasional victories in the “Run” state, the fundamental philosophy of “your mess for less” falls short of enabling change in the face of today’s retail environment’s demands.
Run to New for retail: 5 building blocks that turn run into reinvention
At Logic, Part of Accenture, we’ve operationalized and refined a playbook for retailers of all sizes into five practical, outcome-driven building blocks. Together, they define a modern retail IT operating model where Run work funds reinvention.
01 Reimagined labor mix
Historically, retail IT teams were structured like internal development shops, built to maintain custom systems and hand-coded integrations. Modern SaaS platforms, composable architectures, and API‑first ecosystems have changed that equation.
Leading retailers are shifting internal IT away from dev-heavy execution and toward business partnership and orchestration. Internal teams focus on translating business needs into roadmaps, coordinating change across merchandising, supply chain, stores, and digital, and governing platforms rather than building everything themselves from the ground up.
When deep technical expertise is required, retailers increasingly rely on partner-delivered capacity that scales with demand. AI plays an enabling role by accelerating delivery and reducing friction, not by replacing people.
We help retailers redesign their operating model so internal teams focus on business alignment and orchestration, while flexible, partner-led delivery provides technical scale when needed. Logic, Part of Accenture brings retail-specialized platform expertise on demand and embed AI-accelerated tooling into delivery to improve speed and efficiency without increasing fixed headcount.
For a footwear brand facing a skills gap in Oracle RMS and a new digital platform, we embedded two platform specialists and ran capability‑build sprints with store‑ops IT staff. Within six months, the internal team was resolving tickets independently, and the external contractor spend dropped substantially.
02 Agentic AI for legacy applications
Legacy systems remain mission-critical across retail, but they no longer have to block modernization. Agentic AI now enables teams to interact with, stabilize, and modernize legacy environments incrementally and safely.
AI agents can navigate user interfaces, execute workflows, remediate defects, and automate portions of refactoring work, allowing progress without risky rip‑and‑replace projects.
We apply agentic AI selectively to reduce legacy drag without disrupting critical retail operations. Using a mix of AI-assisted analysis, remediation, and workflow automation, we help retailers modernize existing systems (no rip-and-replace), enabling progress through controlled, incremental improvements rather than large-scale rewrites.
Rather than replacing a long‑standing legacy application, one retailer used agentic AI to stabilize and incrementally modernize it. Work that once required months of manual effort became a controlled, step‑by‑step process that reduced risk while accelerating improvement.
03 Autonomous IT operations
Retail IT teams lose enormous capacity to repetitive operational work: incident triage, regression testing, batch‑job monitoring, and manual remediation. Autonomous IT applies GenAI, RPA, and self‑healing automation to remove this work from human queues.
Incidents resolve automatically. Known issues do not recur. Systems stabilize faster, freeing engineering capacity for higher‑value initiatives.
We focus on automating the operational run layer to deliver an immediate, measurable impact. By combining intelligent monitoring, event correlation, and self-healing workflows, we help retailers reduce operational noise, improve stability, and reallocate engineering resources to modernization and growth initiatives.
A clothing retailer deployed intelligent automation to classify operational issues using transaction velocity, system telemetry, and network data. Mean time to resolution dropped significantly, and engineers were able to refocus on modernization work.
04 Composable architecture
Modern retail depends on modularity. Service‑oriented, API‑first architectures allow core platforms (Point of Sale, Order Management, Customer Relationship, etc.) to function as composable components rather than tightly coupled monoliths.
Composable architecture simplifies integration, improves upgradeability, and enables systems to evolve independently without full replatforming.
Logic helps retailers evolve toward composable architecture incrementally, prioritizing stability and business impact over large transformation programs. We modernize selectively through containerization, API enablement, and CI/CD improvements, allowing systems to change independently while maintaining operational continuity.
For a home décor retailer, we containerized core retail modules, implemented CI/CD pipelines, migrated to OCI, and retired most on‑prem servers. Release cycles improved from quarterly to monthly, and over 80% of technical debt was retired without unplanned outages
05 Retail observability
Retail success depends on workflows, not servers. Observability provides end‑to‑end visibility into the processes that drive revenue: inventory movement, promotions, order orchestration, and fulfillment.
By instrumenting these workflows, retailers can detect issues before customers are impacted, reduce manual reconciliation, and tie IT performance directly to business outcomes.
Logic implements retail-specific observability focused on business flows rather than isolated systems. We design dashboards, alerts, and traces that surface issues across the full transaction lifecycle, enabling proactive intervention, faster root-cause analysis, and better alignment between IT performance and retail outcomes.
For a clothing retailer, we deployed Elastic‑based traces across POS → OMS → WMS. Inventory mistransfers that once took hours to diagnose now surface in real time. Manual reconciliation fell by 70%, and online out‑of‑stock incidents dropped by half.
Lessons learned and best practices
At Logic, we know retailers. We know how to help build lean IT organizations that can adapt to rapid market shifts. Here are some of our key lessons from our Run To New projects:
- Start small, scale fast: Begin with a single building block (e.g., observability or AI automation) as a pilot. Demonstrate ROI in 3–6 months, then expand to adjacent areas.
- Align on outcomes: Early alignment workshops with both IT and business stakeholders ensure that run and change targets map to real revenue or customer experience metrics.
- Invest in culture: Technical automation alone won’t succeed without a culture that values data-driven decision-making and continuous learning. Leadership buy-in is critical.
- Tailor to retail rhythms: Incorporate high-season planning (e.g., back-to-school, holiday) into your change calendar. Automate rollback and failover scenarios to minimize risk during peak traffic.
Measure twice, automate once: Comprehensive service experience KPIs provide guardrails—preventing “automation for its own sake” and ensuring every improvement drives business value.
Getting started
By right-sizing foundations, automating the grind out of operations, modernizing architecture incrementally, and instrumenting retail workflows, managed services become a force for reinvention rather than delay.
With Accenture’s scale and Logic’s retail DNA, retailers of all sizes no longer have to choose between stability and change. They can achieve both on a timeline that matches their seasonal rhythms and growth goals.
Want to learn more? Download our new ebook: The New Retail IT Operating Model: How CIOs Free Budget for AI-Driven Innovation. Or, if you’re ready to learn how this new approach can be applied at your org, let’s talk.
→ Get The New Retail IT Operating Model ebook
* Metrics observed across Logic, part of Accenture engagements and are provided for illustrative purposes only.