In the TCS Global Retail Outlook 2026, a survey of more than 800 senior retail executives across 18 countries, AI ranked among the top enablers for the business. Yet 85% said they had not begun implementing, or even planning for, multi-agent AI systems, and nearly half (48%) had no plans to. Expectations keep climbing anyway: in a recent BCG survey, 68% of retailers said they expect agentic commerce to have a significant or transformational effect on their business within two years.
That leaves most retailers somewhere between wanting to change how their operations run and being ready to. Over the past several weeks, this series has looked at why operations become the ceiling on assortment growth, why retailers end up trading growth against cost control, how in-house and outsourced models compare, and how to get more from your partners. This post turns those ideas into a self-assessment you can run with your own team.
How to use this assessment
Readiness has two sides. The first is whether your operation needs to change: whether the pressure on your team is already showing up in day-to-day work. The second is whether your organization is able to change: whether the data, leadership, and ways of working are in place to support it. Most transformation efforts stall because the second side was never checked.
As you read, check off each statement that sounds like your organization. Then count your checks for each part separately.
Part one: signs you need to change
The earlier posts in this series cover these pressures in depth, so this part is quick. Mentally check off each one as it applies to your team.
- Supplier growth is outpacing your team: Each new wave of suppliers adds backlog or headcount, and onboarding queues grow faster than your team can clear them.
- Exceptions go to whoever is available: Late shipments, cancellations, and data errors get handled case by case, so the same problems repeat across suppliers.
- Leadership wants growth and cost control at the same time: Expansion plans and efficiency targets arrive in the same planning cycle, often with the same team expected to deliver both.
- Your team’s hours go to monitoring rather than improving: A large share of the week goes to watching dashboards, checking feeds, and chasing routine follow-ups.
If you checked 3 or 4, your operation needs to change. If you checked 2 or fewer, the pressure is still building.
Part two: signs you’re able to change
This part is where most organizations discover their gaps. Each statement reflects a factor that research consistently ties to whether operational change succeeds or stalls. Do the same as before, and check off each one as it applies.
Your data is connected and trustworthy.
Supplier, order, and inventory data live in connected systems that your team, and your partners, trust enough to act on. This is the most common point of failure. Gartner predicts that through 2026, organizations will abandon 60% of AI projects that aren’t supported by AI-ready data, and 63% of organizations either lack or aren’t sure they have the right data management practices for AI. In retail specifically, BCG’s recent survey of 150 retailers and brands found that fragmented legacy systems, poorly structured metadata, and missing integration layers make it hard for AI agents to act on marketplace information.
A senior leader owns the outcome.
Someone at the leadership level is accountable for how the operation changes, beyond approving the budget for it. McKinsey’s 2026 State of AI survey found that the small group of organizations seeing significant financial value from AI back their deployments with leadership commitment, and one retail executive told BCG that senior sponsorship and business ownership of AI benefits are what create room to invest.
You’re willing to redesign the work.
Your organization is prepared to rethink how a workflow runs from end to end, rather than adding a new tool to the existing process. This is the clearest dividing line in the research. In McKinsey’s 2026 survey, nearly three-quarters of AI high performers had fundamentally redesigned workflows around AI, compared with about a quarter of other organizations. Across all respondents, only 37% said AI had contributed to EBIT at all.
You invest in people and process, not only technology.
Training, role changes, and process design get real time and budget alongside the systems. BCG’s guidance for AI transformation is to put 10% of the effort on algorithms, 20% on technology and data, and 70% on people and processes, a reminder that most of the work of changing an operation happens outside the software.
Your partners can carry more of the work.
Your commerce partners have the access, data, and accountability to take on operational work, not just provide software. If your team still does most of the work inside a partner’s platform, this is worth revisiting.
If you checked 3 or more, your organization is ready to change. If you checked 2 or fewer, the foundations need work first.
What your results mean
Wherever you land, our eBook, Autonomous Commerce: The Operator’s Guide to Moving from Manual Coordination to Intelligent Execution, lays out a five-level maturity curve you can use to plan the next step. Or, call our team and we can help you get started.
Where Logicbroker fits
Logicbroker works with enterprise retailers on both sides of this assessment. Drop ship and marketplace programs run on one platform connected to a network of suppliers, which gives retailers connected supplier, order, and inventory data from the start, with supplier onboarding and product onboarding built in. If you’d like a second opinion on your results, our team can walk through them with you and help map where to start.