Moving deliberately rather than fast: How brands and retailers are achieving AI success

Ahead of this year’s ShopTalk event, Modern Retail sat down with Ronen Vengosh, Chief Strategy Officer at Celigo, the intelligent automation platform that unifies the predictable and the fully agentic, to talk about what cross-functional brand and retail success looks like in the era of AI, how brand and retail organizations are successfully scaling the technology and the kind of framework that sets brands and retailers up for integrating AI.

As commerce evolves in the era of AI, what does it look like to safely apply the technology where it matters most for brands and retailers?

Ronen Vengosh: The brands and retailers getting the most value from AI are not the ones moving fastest — they are the ones moving most deliberately. The right starting point is identifying processes that carry a high manual workload and a clear, measurable outcome, then committing to automating that entire process end-to-end rather than just sprinkling AI on top of individual tasks. Critically, this is also an opportunity to rethink the process itself, not simply replicate what already exists at higher speed. Once you have defined the new process, it is worth asking how much of it can actually be handled by deterministic automation — rules-based logic, workflow orchestration, system integrations — and reserving AI for the steps that genuinely require it. That “least agency” mindset reduces error surface and makes the system auditable. Finally, keep humans in the loop, especially early on and in any area where errors are costly. That is not a sign of distrust in the technology, it is how you build the confidence to scale it responsibly.

Why is it key to move AI out of IT and into every department for brands and retailers to truly achieve cross-functional success in the era of AI?

Ronen Vengosh: AI will ultimately reshape every function in a brand or retail organization — merchandising, supply chain, finance, marketing, customer service — and treating it as an IT project is one of the most reliable ways to slow that down. The teams closest to the business problems have the context to identify where AI will actually move the needle, which is a very different skill from knowing how to deploy infrastructure. That said, democratizing AI deployment across departments does not mean abandoning governance — the risk of fragmented, ungoverned AI proliferating across a brand or retail enterprise is real. The better model is one where IT brings the AI tools and technical knowledge, the “Art of the Possible,” while establishing the guardrails, integration standards and data pipelines, which allows business units to drive the use cases and own the outcomes. That balance — decentralized initiative with centralized control — is what allows AI adoption to scale without creating a compliance and security problem in the process.

Can you talk about what AI for process automation looks like in real workflows? How does this apply to the idea of a connectivity-first approach to AI?

Ronen Vengosh: A useful example is order exception management — a process that in most brand and retail operations involves people manually triaging failed orders, cross-referencing inventory systems, checking with suppliers and updating the customer. No company’s change management is perfect and everyone is trying to move faster in today’s world. An AI-augmented version of order exception management connects the relevant systems first, so the AI has reliable, real-time data to work with, then uses that data to triage automatically, propose resolutions and escalate only the cases that require human judgment. The sequence matters: AI sitting on top of disconnected, siloed data does not produce reliable outcomes; it produces confident-sounding errors. A connectivity-first approach means building the integration layer before the intelligence layer — establishing clean, orchestrated data flows between ERP, order management systems, warehouse management systems, CRM and commerce platforms — and then deploying AI into a workflow where it can act on information it can actually trust. That foundation is what separates AI that performs in a demo from AI that performs in production and drives a direct increase in customer experience as well as operational efficiency.

What is the difference between brands and retailers that are able to successfully operationalize AI and those that are unable to do so?

Ronen Vengosh: Most brands and retailers are currently using AI as a productivity tool — someone in marketing uses it to draft copy faster, someone in finance uses it to summarize reports — and that creates real but modest value. Operationalizing AI is a different challenge entirely. It means deploying AI throughout entire business processes, including ones that span multiple departments and systems, and having those processes run reliably at scale without a human reinserting themselves at every step. The gap is roughly analogous to the difference between a skilled craftsman producing excellent individual pieces and a factory that can produce those same pieces consistently at volume — the output looks similar, the underlying capability is completely different. Brands and retailers that operationalize successfully tend to share a few traits: They invest in the integration and data infrastructure first, they pick processes where the ROI is unambiguous and they resist the temptation to run 20 pilots simultaneously and instead drive a small number of use cases all the way to production.

What are some of the biggest mistakes related to AI that brands and retailers are making today?

Ronen Vengosh: The most common mistake is running too many pilots at once without driving any of them to full production. A brand or retailer with 15 AI experiments underway often has less operational AI than one with two use cases that have been fully deployed and are running reliably in the background. Fragmented experimentation feels like progress but rarely produces the compounding benefits that come from end-to-end automation. A second significant mistake is deploying AI into processes before the underlying data and system connectivity are in order, which predictably results in AI that behaves inconsistently and erodes trust in the technology faster than it builds it. Third, many brands and retailers are making AI adoption the responsibility of a single central team, usually within IT, and then wondering why uptake is slow. The business units are not engaged, do not feel ownership and default to working around the tools rather than into them. None of these mistakes are fatal, but organizations that do not correct them early tend to find the cost of fixing them grows significantly over time.

If you could suggest a framework that would successfully set brands and retailers up for integrating AI, what would that look like?

Ronen Vengosh: We think about the AI maturity journey in four stages. Aspirants are just getting started — the priority here is identifying a single high-value process with a clear ROI case and running a focused pilot rather than spreading energy across too many initiatives. Experimenters have a pilot behind them and are ready to build AI into a complete end-to-end workflow, which requires more investment in data connectivity and cross-functional alignment than most organizations anticipate. Practitioners are running AI in production and need to shift their focus to scalable data pipelines, the ability to integrate new systems quickly and governance structures that can handle complexity without slowing things down. Orchestrators are the most mature cohort — they are deploying agentic workflows at scale, and are focusing on observability, scalable governance and optimizing costs and results. At each stage, the right priorities and the right mistakes to avoid are different, and one of the most common errors is trying to skip stages — investing in orchestration before the foundational integration work is solid enough to support it. For those that haven’t started the journey yet, focus on starting small and the success will build from there.

Sponsored by Celigo