Agentic AI & Machine Customers: From helpful assistants to skilled collaborators and autonomous workers

Agentic AI systems operate autonomously, they learn, collaborate, adapt, and make real-time decisions in complex, nuanced and emergent environments. Contrary to traditional AI that is reliant on human intervention, programming, training, and is limited by models, algorithms, and predefined rules; this new generation of artificial intelligence is designed to realize goals.

Agentic AI systems augment advanced data capabilities, memory management, historic reasoning and decisioning with learning and action to achieve complex goals. They integrate across complex business and environmental ecosystems, iterate on goals to solve non-deterministic and non-probabilistic real-world problems. Capable of independently making decisions, they eliminate the need for human intervention, offsetting nuanced activities once done by humans to machines.

Let’s imagine an Agentic AI system tasked with reducing supply chain inefficiencies and disruptions. Agentic AI goes beyond the realm of traditional AI which would flag issues for human intervention — It analyses impediments, shortfall, real time data, traverses historic decisions, live ecosystem data, to take corrective action without human input, intervention and approval.

Let’s take a moment to reaffirm our understanding. Picture Agentic AI livelihood enhancement use cases with;

• Robotic surgeons performing autonomous surgeries.
• Smart grids that optimize energy generation and distribution based on real-time demand and supply dynamics.
• Autonomous transport and logistics (e.g., cars, mass transit, drones, ships) to enhance safety, reduce congestion, optimize logistics and save lives.
• Traffic management systems that adapt real-time conditions to eliminate congestion.
• Autonomous self-heal systems that monitor and repair infrastructure to reduce outages.
• Personalized patient care and treatment based on real-time patient data to saves lives.
• Autonomous drones for seeding, irrigation and pest control for crop management.
• Robo-advisors that provide real-time financial advice and personalized investment plans.

Machine Customers are an application of Agentic AI, they play transactional roles, predominantly as buyers and sellers. Machine Customers and their operating ecosystems are rapidly evolving to represent transactions for individuals, households, business teams, functions and entire organizations. Their applications range from connected devices— smart refrigerators, and printers reordering supplies when low; to complex systems, like procurement that negotiate contracts, service level agreements (SLAs), and bill of materials (BoMs).

Now, let’s imagine Machine Customer application and use cases,

• Healthcare devices like smartwatches or glucose monitors that autonomously order refills, book appointments, and alert local caregivers based on patient vitals.
• Smart assistants that reorder household/office supplies when inventories run low.
• Autonomous EVs purchasing electricity from a charging station.
• Smart factories ordering spares based on utility, wear and tear for operational continuity.
• Autonomous routers that adjust bandwidth or switch between plans/providers based on cost and performance.
• Autonomous energy grids that trade surplus energy in decentralized markets or manage peak hour consumption and pricing.
• Smart irrigation systems that monitor soil, moisture and weather to order water and adjust irrigation schedules for optimal crop yield.
• Connected cars that order oil changes, tyre replacements, and software updates when required.

For brevity, the tabular summary illustrates Agentic AI and Machine Customer applications.

Aspect	Agentic AI	Machine Customer
Primary Role	Decision-maker, optimizer, executor	Autonomous consumer
Focus	Broader applications across industries	Specific to transactions and consumption
Key Capabilities	Autonomy in decisions and actions	Autonomy in purchasing and interactions
Use Cases	AI managing complex logistics networks	Smart fridge reordering groceries
Scope	Strategic, adaptive and operational	Transactional and economic

Let’s Understand the Impact of Agentic AI and Machine Customers’ on the Enterprise:

• Enterprise strategies (Procurement, Engineering, Marketing, Sales etc) must extend and integrate interfaces and APIs across content, product information to service Agentic AI and Machine Customers.
• Commerce platforms, catalogs and payment systems must evolve to serve these new-age customers.
• Ethical challenges must be addressed to ensure explainability, transparency and trust, to avoid machine decision exploitation.
• With Service-as-a-Software, guardrails must be established to ensure human-machine overrides, conflict and bias mitigation, in real-time.

Further, mainstream innovations must integrate sustainability, optimised resource utilisation, carbon-footprint and circularity i.e. autonomous devices turning themselves in for refurbishment or recycling.

This new-era goes beyond automation to achieve goals and transactions autonomously. Agentic AI and Machine Customers represent a new demand paradigm that needs enablement across every facet of business operations.

Envision a world increasingly inhabited by Agentic AI and Machine Customers, human centric business strategies must evolve to target algorithms and autonomous systems. Novel enterprise strategies (spanning Marketing, Sales, Procurement and more…) must surpass traditional human persuasion, emotional and trust levers to captivate and enchant this new consumer category. They must solve for data compatibility across product and service catalogs, content, APIs and beyond to onboard and integrate novel business-to-machine (B2M) interaction channels.

Beyond people, experiences need to be optimized for machine decision-making to realize transactions and subscriptions. To win over these autonomous, highly fastidious, buyers, it’s paramount that enterprises rewire value propositions, prioritise ethics, trust, explainability and transparency across this novel value chain.

For instance, businesses must level up media spend on highly detailed, structured product information that allows machine customers to independently evaluate and compare product and service offerings for purchase. Challenges lie in intricately balancing these new demands while continuing to appeal to the human stakeholders who set the baseline buying parameters for these new category of buyers.

The transformation of Agentic AI and Machine Customers from helpful assistants to skilled collaborators and autonomous workers represents the next wave of evolution to realize effectiveness and productivity across people, businesses, and machines in a purely digitally-native realm.

With Every Innovation Comes A Whole New Set of Challenges and Opportunities

As this evolution plays out, it gives rise to a new set of challenges and opportunities for businesses to solve as they level up the maturity curve.

Operational Governance: Autonomous systems must operate within robust guardrails spanning data, transparency and explainability; they must ensure accuracy whilst safeguarding privacy and security. Fail-safes, overrides and continuous audits for autonomous AI systems making critical decisions in healthcare, transportation, or defence must be established.

Ethics and Accountability: Determining responsibility is complex when an autonomous systems make errors or unethical decisions. Clear regulation and governance for ethical AI, traceability, accountability, and liability must be established from get-go, whilst rules safeguards and overrides continuously evolve through the business maturity journey.

Interoperability: Seamless cross-border interoperability standards, and best practices for enablement ecosystems must be established for AI systems integration into mainstream adoption. This includes IoT devices, APIs, blockchain networks, edge compute, Web3 and the metaverse.

Sales and Marketing to Algorithms: In a world where autonomous systems make purchasing decisions, traditional human-centric Sales and Marketing strategies must extend experience consumption to AI systems and algorithms. Brands must ensure product and service experiences are machine-readable, they must provide API access for evaluation, and activate real-time refreshes to appeal to autonomous consumers.

Brand Influence: Emotional branding and loyalty established for humans must be extended to  target autonomous systems that make decisions based on predefined and evolving parameters to leapfrog brand influence to this novel consumer category.

Evolving Consumer Trust: Targeting human customers who baseline buying parameters for autonomous systems, will remain a big opportunity for brand differentiation. While machines iterate and optimize parameters, human decisions will shift upstream to high-strategic value. This implies that respected and trusted brands will have the opportunity to secure strategic accounts.

Dynamic Competition: With AI systems able to instantly compare products, services, and pricing, markets will face hyper-real-time competition. Businesses require agility and responsiveness, they must refine offerings in real time to remain competitive in this new era.

Socioeconomic Restructuring: Business models need reinvention for an autonomous world, they must reimagine and redefine future job and workforce structures. They must establish new governance models that enable harmony and co-existence across autonomous and human-decisioning with measures to address potential displacement and inequality.

Addressing these challenges are key, this might very well be the most exciting new era of business development since the internet.

Where Should Businesses Start?

Foster AI Literacy Across Teams: Establish training, skilling and upskilling plans across the enterprise and launch primers and pilot programs to target employee personas. Ensure employees understand the AI purpose, how they work and co-exist with these AI systems and technologies. Extend specialized enablement for programming, oversight and monitoring AI systems effectively. AI is an enabler, not a replacement for human ingenuity.

Define Early Journeys: Study, model ROI and effectiveness levers for candidate business journeys. List out potential use cases for Agentic AI and Machine Customer enablement. Fuse disruptive trends with identified use cases for prioritization across the simple to complex horizons to establish confidence with Agentic AI and Machine Customer systems. For e.g. Chatbots and virtual assistants for FAQs, Agentic AI Helpdesk enablement, Inventory replenishment, Contract reviews, amendment and renewal lifecycles.  

Establish Guardrails: Define clear operational and ethical guardrails for autonomous systems, including decision limits and escalation points. Setup governance for continuous guardrail optimization as the enterprise navigates this maturity curve.

Collaborate with Trusted Partners: Establish a trustedpartner vendor ecosystem to accelerate adoption maturity, speed, scale and mitigate risks.

Track ROI and Iterate: Monitor autonomous systems impact with metrics like cost savings, efficiency, effectiveness, CSAT and business uptick. Use these insights to constantly iterate on goals and outcomes.

Agentic AI and Machine Customers are more than emerging trends—they are a significant step forward in every digital business transformation journey. As they gain traction, enterprises have a unique opportunity to leverage these technologies to drive growth, efficiency, and innovation.

The Resulting Novel World Order

We are living through an era of AI-driven disruption transcending from ‘Helpful Assistants’, to ‘Skilled Collaborators’ to ‘Autonomous Workers’. Autonomous AI systems will soon manifest themselves into mainstream daily life to enhance livelihood.

These systems initially designed to serve humans will evolve to play the role of consumers and decision-makers. They will reshape industries and lifestyles to become the largest consumer category for enterprises and brands to target.

Retail will cater to algorithms, virtual storefronts will market directly to Machine Customers. Interactive entertainment, gaming and media will be AI-driven, enabled with dynamic pathways personalized to consumer preferences in real time. Agentic Healthcare will monitor patient vitals, book appointments, and order medications, ensuring proactive care and increased life expectancy.

Smart Manufacturing facilities will autonomously review, amend, negotiate and execute contracts, source materials, spares and peripherals. They will elevate uptime with self-heal capabilities and maximize production output. Energy markets and smart grids will autonomously trade surplus electricity. Smart Homes will self-heal appliances, replenish supplies, pay utilities, renew subscriptions and more.

Autonomous Agriculture will optimize harvest for a growing population to eliminate hunger and starvation. Autonomous robotic surgeries will save lives, drones will deliver medicaid and daily essentials to inaccessible areas. AI-driven personalized education will cater to individual learning paths.

Challenges such as algorithmic biases, security and privacy concerns, and maintaining human oversight will define future societal discourse. An intricate balance between autonomy and accountability, crucial in this autonomous era will continuously evolve.

‘The resulting novel world order will free humans from mundane decisions and activities resulting in ultra convenient lifestyles.’