We recently caught up with Emre Kok, SVP of Technology and Software at invent.ai, to discuss the biggest values driving retail transformation and the ways agentic AI is fundamentally changing how decisions are made. The insights were clear: AI isn’t just supporting retail growth strategy anymore. AI is becoming part of it. The shift is central to any AI transformation effort, and together, retailers are indeed unlocking a new level of value through enterprise-grade scalability.

AI has long played a role in shaping retail decision-making, but what most know as AI and the storylines of AI today can be confusing. The stringent and rule-based systems in the past were the apex of improving customer experiences through better decision-making. Alas, that narrative has been replaced with LLMs and a sense that anyone can build an agent that’ll work and provide the real answers you need. That’s simply untrue, and trying to wing AI implementation will lead to major issues, horrible customer experiences and who knows what other issues. 

But if we were to take a step back and consider a top-side view, we could see that AI integrates with more systems, processes and operations than ever. Retail AI is not just a simple automated process but a profit-optimized, high-impact-use-case tool that truly unlocks a new degree of value. An advanced, agentic AI must be built around giving tools the ability (through rigorous testing and ongoing monitoring) to think and learn on their own in a sense. 

From demand forecasting to product assortment, traditional optimization tools have helped teams manage complexity. But something fundamental is shifting. With the rise of agentic AI, those static models are evolving into dynamic systems that interact directly with products, users and data to drive continuous adaptation. This is the foundation of true digital transformations.

As the scale of retail grows, so do the challenges. Tens of thousands of SKUs and billions of transaction points overwhelm legacy systems. Even advanced dashboards and alerts fall short. There’s simply too much data, too many decisions and not enough time. That’s where agentic AI steps in, enabling real time alignment between action and insight. Such expansion is precisely how retailers begin to quantify agentic AI ROI and measure value beyond surface metrics. Unlike older models, AI technologies built with machine learning and AI models allow systems to adapt automatically.

From automation to interaction: The shift agentic AI enables

Traditional AI workflows were built around rule execution and basic optimization. But that’s not where the market is going. With generative AI and large language models increasingly embedded in retail systems, the future is no longer about automation in workflows. A cohesive and strategic AI-Decisioning Platform can see beyond the typical and recognize that not every tool is needed in every case. 

Invent.ai is leading that shift by embedding agentic AI into environments that not only process data but actively guide retailers through it. This means surfacing action recommendations, adapting to new data in real time and unlocking previously siloed operating models. As systems evolve, integrating AI becomes essential across departments.

As Emre explained, "We have analysts looking at results to identify if [agentic AI output and] results were expected. Where the agents are coming into place is figuring out the reasoning and navigating the user through the experience."

That’s where agentic AI in retail problem solving is already showing up. Not as a distant future, but as a daily driver of retail outcomes. This evolution represents meaningful AI adoption.

Building autonomy at scale with guardrails, not guesswork

Most AI platforms still depend on human intervention for course correction. Invent.ai removes that dependency. Our AI-Decisioning Platform and expert team trains agents not just to analyze, but to act, monitor and self-correct based on business logic and ethical boundaries.

Earlier attempts at process automation involved layers of controls–a quasi-precursor to the recommended-for-action language that now fills the deepest reaches of the internet. Today, those controls are embedded inside the system, inside invent.ai. “We’re employing the guardrails and controls to ensure autonomous frameworks are doing what they’re supposed to do," said Emre. The shift is no longer from manual to automatic. It’s from reactive to intentional.

In this model, oversight doesn’t vanish. It evolves. Invent.ai makes it possible to run agentic AI systems with confidence by design. That’s the foundation of ethical guardrails for agentic AI.

Partnership isn’t post-sale. It’s part of the product.

A major reason AI initiatives fail is simple: vendors don’t typically stick around once a solution is live. Invent.ai changes that. Our client success model isn’t reactive; it’s embedded. We’re embedded. We’re in it together, we evolve together and we’re not going to leave you stuck and alone. 

"The second differentiation is our partnership model with clients that helps them after going live," Emre said. 

It’s this partnership that allows retail teams to adjust and scale AI solutions as real conditions shift. New data sets, inventory management rules and merchandising strategies are supported without heavy lift and advanced coding know-how.

For teams wondering what happens after launch, this kind of partnership ensures the system continues to evolve, helping improve customer outcomes, streamline supply chain decisions and elevate customer experiences. Selecting an agentic AI vendor means you have to think about long-term fit, not short-term hype. 

When scale breaks archaic systems, collaboration rebuilds it

Retailers often run into the same wall: systems that once fit can’t keep up with the volume and volatility of today’s markets. Agentic AI isn’t a patch but a rebuild of how technology is making changes to everyday capabilities. 

With invent.ai, multiple agents operate in collaboration across datasets, breaking down silos and accelerating response. Unlike platforms with one or two embedded AI tools, invent.ai’s model integrates multiple roles: some agents interact with users, others govern logic and some continuously retrain. This allows the system to operate as a truly AI-powered, AI-driven decisioning network.

Rather than drowning in disconnected alerts, teams get what they need. 

• Fewer hand-offs. 
• More action. 
• Stronger MFP results. 

That’s the architecture behind strategic decisioning shifts. Working with a technology provider, hyper-focused on retail profit optimization, i.e., invent.ai, further enables rapid deployment at scale. Rather than simply trying to build technology, retailers get the value of nearly 25 years expertise in retail AI, ensuring their productivity goals are always in the foreground of all decisions. Retailers then have the confidence to scale and apply insight across all teams without delay and without added costs.

Be part of the retail AI transformation with invent.ai

Ready to see what agentic AI can do inside your organization? Let’s talk about how invent.ai helps teams think faster, scale strategically and build retail systems that never stand still.