How agentic AI can take pricing to the next level
In a world where pricing decisions are becoming increasingly complex and dynamic, businesses need intelligent data – and this is where agentic AI comes into play. In terms of opportunities and impact, agentic AI can go beyond traditional AI or even generative AI, write Danilo Zatta and Anders Worsøe Gantzhorn from Valcon.
In the context of pricing, agentic AI holds the potential to be a gamechanger. It can monitor market signals in real-time, identify margin improvement opportunities, simulate pricing scenarios, adjust pricing models and ensure compliance with pricing governance – in a nutshell, it can do more than previous incarnations of AI, with less human intervention – and as a result it can increase scale and reduce operational costs.
Why does agentic AI bring that gen AI doesn’t?
It feels like yesterday the world was blown away by ChatGPT. In fast succession, LLM models were everywhere, and competitors and niche players were entering the market, sparking excitement and criticism across academic, regulatory and business environments. Agentic AI doesn’t replace generative AI – it enhances it, because agentic AI needs to leverage the power of LLMs. It’s an extra, proactive layer.
So what can agentic AI do for pricing strategies that generative AI can’t?
An example would be market scanning and competitor monitoring. Whereas generative AI acts like a very smart analyst, summarising competitor data and pricing patterns and interpreting data to explain trends, agentic AI acts like an autonomous market intelligence officer, constantly scanning data and acting without needing prompts.
Examples include: It would be connecting to APIs and scraping data from competitor websites and integrating that with news feeds. It would detect unusual events, like competitor price drops and alert you to it. And it would be sending you alerts to tell you what it’s discovered.
Agentic AI: pathway to the next frontier in automation?
A well-implemented agentic AI solution can act autonomously and deliver what it thinks you need to make decisions. That is not to say it doesn’t need human intervention or supervision – planes will always need pilots, medical doctors will always be needed to check the results of AI assessments and pricing professionals will be needed to oversee the results of what agentic AI produces.
But the emergence of agentic AI has the potential to usher in the next stage of business automation. By supporting goal-based task execution, organisations can start to leverage the technology to adapt and act independently based on real-time signals and reinforce learning in closed feedback loops, which improve over time. Think of it as a persistent, context-aware colleague that ensures processes run, insights are applied, and opportunities are seized – even when you’re not looking.
Agentic AI is about the augmentation of workforces, not the replacement. The goal is not to displace the commercial workforce – it’s to empower it.
It would be a mistake to think that unlocking the potential of agentic AI is plug-and-play. It needs skilled AI developers, architects and domain experts who can translate business needs into system logic, define operational guardrails and build the technical backbone required to ensure reliability, consistency and long-term value.
Use cases of agentic AI in pricing
In today’s highly dynamic and interconnected commercial business environment, where information is so readily available, competitive edge can only be achieved by those organisations which can quickly and accurately respond to market fluctuations. With the advent of agentic AI, the power of generative AI can finally be harnessed to deliver vertical value in the domain of pricing.
Pricing operations are complex, multi-layered and data-driven: monitoring market trends, tracking competitor prices, adjusting promotional strategies and ensuring margin integrity. Agentic AI can automate and optimise this chain, turning pricing from a static function into a strategic advantage.
Similarly, AI agents can provide top-line growth when embedded into e-commerce environments, proactively analysing user behaviours, browsing activity and contextual factors such as seasonality or purchasing history – this with the purpose of triggering relevant cross-selling or upselling offers. Shopping data can be leveraged to improve forecasts and optimise revenue opportunities and simplify stock and order management processes.
AI agents can also be used to provide data insights, which can save pricing analysts the time and effort they would have spent generating and building visualisations for reporting purposes – this can free them up to work on more strategic tasks.
Another interesting use case is the automation of the rebating processes, which can calculate thresholds and structure incentives to improve existing customer relationships. In other words, AI agents have the potential to optimise commercial efforts both internally and externally, benefiting business owners and customers.
From concept to practice
Realising the value of agentic AI in commercial functions and pricing strategies requires more than technical access – it demands strategic integration and complimentary technical expertise.
First, data foundations must be solid – agents rely on clean, connected and real-time inputs across CRM, pricing and market signals. Second, workflows must be redefined to support near-autonomous execution: identifying where agents can act, when to escalate and how to measure impact, while keeping a human in the loop for safety.
Finally, commercial teams need the mindset and structure to work with AI: not as a tool to manage, but as a partner to delegate to. Without this alignment, the promise of agentic AI risks becoming just another technology investment, with limited bottom-line impact.
To succeed with agentic AI and its role in pricing, it must be integrated with strategic intent, not merely as an add-on to an already extremely complex technological environment. Similar to generative AI, agentic AI is no shortcut to limitless profits – it is an investment requiring deep technical expertise and commercial acumen.
But if the technology is implemented correctly, it can enable organisational foresight and adaptability, taking commercial capabilities and pricing power to the next level.
