This week we start a 4 weeks special issue speculating on big trends that will, in my opinion, shape the AI discourse in 2025 and beyond. Today, we look at Agentic AI and Machine Customers. While the names might sound futuristic, their practical applications are very tangible, with big implications for enterprises across sectors. Let’s look at both in more detail.
Focus On: What are Agentic AI and Machine Customers?
Agentic AI refers to a new generation of artificial intelligence systems that are goal-driven, autonomous, and adaptive. Unlike traditional AI, which requires explicit inputs (aka promots) and is limited by predefined rules, Agentic AI combines advanced capabilities such as memory, reasoning, and real-time learning. It can independently make decisions, iterate on goals, and take action with minimal human intervention.
For example, imagine an AI-powered system tasked with reducing supply chain inefficiencies. Agentic AI would not just flag issues—it analyses data in real time, identifying optimal solutions, and taking corrective actions without waiting for human approval.
Machine Customers can be seen as an application of Agentic AI: autonomous, AI-enabled applications that can act as buyers, evaluators, and decision-makers on behalf of individuals or organisations. These could range from connected devices—like refrigerators reordering groceries when supplies run low—to complex systems, such as an enterprise procurement bot autonomously negotiating contracts with suppliers.
The Rise of B2M Marketing?
The fusion of Agentic AI with Machine Customers represents a big shift in business operations, moving beyond mere automation to full autonomy. It also carries huge ripercussions in B2B marketing, potentially even introducing a completely new category: Business To Machine (B2M) marketing.
Imagine a world increasingly populated by machine customers, traditional marketing approaches focused on human persuasion must evolve to target algorithms and autonomous systems. Marketing to machine customers involves optimising for decision-making logic rather than emotions—ensuring that products and services are designed to meet the criteria machine customers prioritise, such as efficiency, cost-effectiveness, and data compatibility.
This shift pushes even further the ongoing integration of marketing and technology, as businesses need to rewire their value propositions to align with machine-readable formats, like API integrations or machine-optimised content, and focus on creating seamless transactional experiences.
For instance, instead of media ads, businesses may need to provide highly detailed, structured product information that allows machine customers to independently evaluate offerings. Unsurprisingly, trust becomes key again as brands must prioritise transparent data practices and certifications that signal safety, compliance, and ethical standards to win over these autonomous, highly fastidious, buyers. The challenge lies in balancing these new demands while still appealing to the human stakeholders who often set the buying parameters for their machines in the first place.
A Whole New Set of Challenges
If this prediction turns out to be true, corporates will face a wide array of novel challenges, including:
Data Governance: Autonomous systems must operate within robust data frameworks to ensure accuracy and security. Poor-quality or biased data could lead to flawed decisions, misaligned purchases, or reputational risks if machine customers act unpredictably.
Ethics and Accountability: Connected to the above, determining responsibility is complex when an autonomous machine customer makes an error or unethical decision. Clear guidelines on liability, transparency, and accountability are essential, requiring collaboration between legal, technical, and governance teams to define rules and safeguards.
Interoperability: Seamless integration with existing systems, including IoT devices, APIs, and blockchain networks, is vital for enabling machine customers to function effectively. Fragmented infrastructures or proprietary silos will limit the efficiency and scalability of these autonomous systems.
Marketing to Algorithms: In a world where machine customers make purchasing decisions, traditional human-centred marketing strategies will not work. Businesses need to optimise their offerings for algorithms, which may require rethinking how they communicate value propositions. Ensuring product data is machine-readable, providing API access for evaluation, and maintaining real-time updates will be essential to appeal to these autonomous buyers.
Loss of Brand Influence: Machine customers make decisions based on predefined parameters, which will marginalise the role of emotional branding or loyalty campaigns. Companies may struggle to differentiate themselves in markets where decisions are made by “cold hearted“ machines.
Evolving Consumer Trust: Human customers, who will set the buying parameters for machine customers, will remain the big opportunity for brand differentiation. Human decisions will shift upstream to focus on high-value ticket items, meaning highly respected and trusted brands will have the opportunity to secure strategic accounts.
Dynamic Competition: With machine customers able to instantly compare products, services, and pricing, markets will face hyper, real time competition. Businesses will need to refine their offerings on the fly to remain competive in this environment, requiring agility and real-time responsiveness.
Addressing these challenges will be key and not an easy task, although this might very well be one of the most exciting development in marketing since the internet.
Where To Start?
Identify Low-Risk Starting Points: Start by automating repetitive, low-risk processes—like inventory replenishment or basic contract review—to build confidence in Agentic AI systems.
Foster AI Literacy Across Teams: Ensure employees understand how these technologies work and how to oversee them effectively. AI is a tool, not a replacement for human judgment and ingenuity.
Collaborate with Trusted Partners: Partner with technology providers experienced in Agentic AI and machine-customer systems to accelerate deployment and mitigate risks.
Set Guardrails: Define clear operational and ethical guidelines for autonomous systems, including decision limits and escalation points.
Track ROI and Iterate: Monitor the impact of these systems using metrics like cost savings, efficiency improvements, and customer satisfaction. Use these insights to constantly iterate.
Agentic AI and Machine Customers are more than emerging trends—they are a significant step forward in business transformation. As they gain traction, enterprises have a unique opportunity to leverage these technologies to drive growth, efficiency, and innovation.
Stay tuned for Part 2 of our 2025 Predictions Special Issue!
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Disclaimer: The views and opinions expressed in Chronicles of Change and on my social media accounts are my own and do not necessarily reflect the official policy or position of S&P Global.