Agentic AI represents the next frontier in artificial intelligence: systems equipped with autonomous decision-making, goal-oriented planning and continuous learning – capable of executing multi-step workflows with minimal human input. Unlike generative AI, which reacts to prompts, Agentic AI proactively sets objectives, executes them across tools, adapts, learns and closes the loop.
Does generative AI solve the problems that we hoped for?
It feels like yesterday that the world was blown away by the generative abilities of ChatGPT. In fast succession, LLM models were everywhere, and competitors and niche players alike were entering the market, which sparked both excitement and criticism across academic, regulatory and business environments. While ChatGPT for many is the AI, competing models have presented themselves like pearls on a string. We now have a wide selection of substitutes available, each claiming to be more efficient, accurate, or faster than the other.
Two years later, the prospects of AI-enabled productivity augmentation, which promised to revolutionise the way we work, still feel like a distant dream. Many CEOs report having made substantial investments in GenAI capabilities, but with limited bottom-line impact (coined the term ‘the gen AI paradox’). Use cases typically only skim the surface of what modern business operations require. While business functions such as customer service, HR, finance and BI all have demonstrated strong use cases for generative AI, its impact in the commercial realm has been limited. The problem lies not in the capabilities of the technology but instead in how generative AI inherently lacks adaptive abilities and the agency to go beyond the static, stimulus-response dynamics to which it typically responds. In other words, while GenAI can generate, it struggles to do.
Agentic AI: Our pathway to the next frontier in automation?
Different from generative AI, Agentic AI does not need a human counterpart. It doesn’t just generate outputs based on delimited prompts and inputs, but instead makes decisions and takes action. It promises autonomy, stripped of the need for human feedback or interaction. Agentic AI does not attempt to create something new; it works towards broader, multi-step goals, dynamically adjusting to new information to provide adaptive and context-specific decision-making.
Why does this matter? The emergence of Agentic AI has the potential to usher in the next stage of business automation. By supporting goal-based task execution, we can leverage the technology to adapt and act independently based on real-time signals and reinforce learning in closed feedback loops, which improve over time.
What role does it play in commercial excellence?
Simply put – because the commercial business environment is highly dynamic. In this interconnected time, where information is more available than ever before, the competitive edge lies among those who can quickly and accurately respond to market fluctuations. While GenAI provides horizontal value, the high-impact vertical impact is lacking, as we struggle to apply and operationalise the technology outside organisational barriers.
In our previous articles on dynamic pricing (find the latest article here: (4) Dynamic Pricing Done Right – and Wrong: Disney vs Uber | LinkedIn), we describe the transformative potential of Agentic AI. 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 entire chain, turning pricing from a static function into a strategic advantage.
Similarly, 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 to trigger relevant cross-selling or upselling offers. Shopping data can be actively leveraged to improve forecasts and optimise revenue opportunities, simplifying stock and order management processes. Additionally, agents can be used to provide data insights, saving pricing analysts time and effort that would otherwise be spent generating and building visualisations for reporting purposes.
Another interesting use case is the opportunity to automate rebating processes, automatically calculate thresholds and structure incentives to strategically leverage and 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 alike – particularly when leveraged in combination with generative AI.
Augment, not replace
A common misconception in AI discourse is that advanced systems like Agentic AI are poised to replace human workers. That view misses the point. The true power of Agentic AI lies not in substitution, but in augmentation – freeing commercial teams from repetitive tasks, surfacing actionable insights faster and enabling more informed, timely decision-making. 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. With Agentic AI embedded into the commercial engine, the focus shifts from firefighting and admin to value creation and strategic growth. The goal is not to displace the commercial workforce – it’s to empower it.
So, what does it take?
Realising the value of Agentic AI in commercial functions requires more than technical access – it demands strategic integration. 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 autonomous execution: identifying where agents can act, when to escalate and how to measure impact. 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 yet another technology investment, with limited bottom-line impact.
To succeed with Agentic AI, it must be integrated with strategic intent, not merely as an add-on to an already constipated technological environment. If implemented correctly, it can enable organisational foresight and adaptability, transforming your organisation’s commercial capabilities to the next level.
Reach out
At Valcon, we are ready to drive your business forward with our sales excellence services. Read out to our experts:
Danilo Zatta, PhD
[email protected]
+49 172 6937532
Anders Worsøe Gantzhorn
[email protected]
+45 2022 5337
Sources:
