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The Death of ABM?

  • Writer: william wright
    william wright
  • 5 days ago
  • 19 min read

The Death of ABM? Why Agentic AI Will Bulldoze B2B Marketing as We Know It

In the pantheon of B2B marketing sacred cows, Account-Based Marketing (ABM) has long stood unchallenged, celebrated for its precision, praised for its personalisation, and defended for its promise of meaningful engagement with high-value targets. But a seismic shift is underway. Agentic AI, autonomous, decision-capable systems acting on behalf of human buyers, is quietly and rapidly dismantling the very foundations on which ABM was built.

This is a post with a particular point of view illustrating the potential rise of agentic AI in B2B and its impact on ABM approaches, (alternative, essential human points of view, decision-making, choice, control and human-agent collaboration are explored in other posts). This is not a ‘now’ article, although some aspects of agentic aree closer than you might thinki, others are not. This is a near future perspective, a challenge to prepare, a provocation to stimulate leadership thinking in commercial strategy and operations from both buyer and seller perspective.

This post was prompted by a recent article from Bain & Company. The article, Marketing's New Middleman: AI Agents, underscores a transformative shift in B2B marketing. As large language models (LLMs) evolve into autonomous agents, they are increasingly mediating the customer journey, from initial discovery to final decision-making. These AI agents are not merely tools; they are becoming the primary interface between brands and buyers.


The article highlights that these AI agents can independently perform tasks traditionally managed by marketing and sales teams, such as identifying needs, evaluating options, and making purchase decisions. This evolution challenges the effectiveness of traditional marketing strategies, including Account-Based Marketing (ABM), which rely heavily on personalisation, ideal customer profiling, assumed complexity, buyer committes and human interaction.


In this new landscape, the focus shifts from crafting personalised messages to ensuring that product information is structured and accessible for AI consumption. Brands must adapt by optimising their content for machine readability, emphasising data accuracy, and facilitating seamless integration with AI systems.


Ultimately, Bain & Company suggests that to remain competitive, businesses must reorient their marketing strategies to engage not just human buyers but also the AI agents acting on their behalf. This requires a fundamental change in how marketing content is created, structured, and delivered.


The Rise of the Autonomous Buyer: Why Agentic AI Will Rewrite the Rules of Commerce

Agentic AI represents a significant evolution in artificial intelligence, characterised by autonomous systems capable of making decisions and performing tasks without human intervention. These AI agents integrate various technologies, including machine learning, natural language processing, and reinforcement learning, to analyse data, plan actions, and execute tasks independently. Wikipedia


In the context of buyer journey management, agentic AI transforms traditional linear processes into dynamic, recursive systems. Rather than following a set sequence of stages, AI agents continuously monitor and evaluate options, adapting to new information and changing circumstances in real-time. This approach enables more responsive and personalised customer experiences, as AI agents can anticipate needs, recommend products, and facilitate purchases with minimal human input.


Looking ahead, agentic AI is poised to revolutionise commercial strategy and operations across various industries. By automating complex workflows and enabling real-time decision-making, businesses can achieve greater efficiency, reduce costs, and enhance customer satisfaction. For instance, in retail, agentic AI can manage inventory and optimise supply chains. Similarly, in enterprise settings, AI agents can streamline business processes, improve data analysis, and support strategic planning. prioritisation and complex decision-making.


The Buyer Has Left the Chat

The era of sales-led discovery calls and meticulously curated buyer journeys is over. Agentic AI is now assuming the front lines of enterprise procurement and solution evaluation. These digital agents don’t need cold calls, content hubs, or hyper-personalised LinkedIn ads. They need data. And when they have it, they execute, in milliseconds.


Agentic AI is not a chatbot. It's a tireless, logic-driven evaluator that identifies needs, scans the market, builds longlists, evaluates alternatives, shortlists viable options, scores them against priority matrices, and yes, makes or strongly recommends purchasing decisions. It collapses weeks or months of human deliberation into seconds. Dialogue with vendors is no longer required. “Let's jump on a discovery call” is dead air to an AI agent with API access and a mandate to buy.


A Completely Different Kind Of Buyer Journey

Agentic AI collapses the traditional buyer journey, once depicted as a sequential or even complex linear process, into a fluid, always-on, recursive loop of search, evaluation, and decision-making. No longer constrained by human limitations like time, attention, or cognitive overload, AI agents continuously scan the market, update criteria, reprioritise needs, and reassess vendors in real-time.


This perpetual motion transforms buying into a dynamic system of instant recalibration, not a journey with fixed stages. Needs aren’t identified once, they are constantly refined. Options aren’t reviewed sequentially, they’re assessed in parallel. Decisions aren't final, they’re subject to continuous optimisation. The result is not just efficiency, it’s a fundamental rewiring of how demand surfaces and is fulfilled in real-time, marginalizing traditional touchpoints and rendering static marketing assets obsolete.


From Months to Moments: The Collapse of the B2B Buyer Journey

The traditional B2B buyer journey, once stretched over weeks or even months of demos, RFI’s, RFP’s, consensus meetings, and vendor negotiations, is being crushed into minutes, even seconds, by agentic AI. Autonomous agents equipped with real-time data access and algorithmic evaluation capabilities can perform what once took procurement teams multiple cycles: identifying needs, generating solution sets, benchmarking vendors, evaluating risk, and recommending a decision.


These AI-driven processes eliminate inertia, reduce human error, and bypass internal friction, compressing complex purchasing cycles into near-instant transactions. In this new landscape, speed isn’t just a competitive edge, it’s the default. Vendors still relying on drip campaigns and nurture flows are selling into a reality that no longer exists. The question is no longer “Who’s in the buying committee?” but “Has the agent already bought?”


Beyond Human Limits: How Agentic AI Makes the Impossible Buy Possible

Agentic AI obliterates the bottlenecks of traditional B2B purchasing by doing what human buying teams simply can't, processing massive complexity with unrelenting consistency and analytical depth.


Where procurement committees are constrained by time, bandwidth, cognitive bias, and internal politics, AI agents evaluate thousands, even millions, of permutations, trade-offs, and decision points in minutes. They bring mathematical rigour to prioritisation, weighting technical fit, cost efficiency, compliance risk, performance benchmarks, and more with algorithmic precision. This isn’t just acceleration; it’s a qualitative leap in decision-making intelligence. Agentic AI doesn’t simplify complexity, it masters it. And in doing so, it redefines what’s possible in enterprise procurement: faster, smarter, and exponentially more thorough than any team of humans could ever hope to be.


The End of the Committee: Why Code Makes Better Decisions Than People

Agentic AI is poised to eliminate the need for bloated buying committees and their entourage of influencers, approvers, and political passengers by embedding organizational needs, constraints, and strategic goals directly into code. Where human decision-making is riddled with unspoken assumptions, cognitive bias, turf wars, and the paralysing fear of being wrong, agentic AI operates with ruthless objectivity. It evaluates based on verified data, not gut feelings; on weighted criteria, not office politics.


These systems don’t get distracted by the loudest voice in the room or derailed by ambiguity, they translate stakeholder priorities into machine-readable logic and execute flawlessly against it. The result? Decisions that are faster, fairer, and factually superior to anything a cross-functional committee could hope to orchestrate. In the age of intelligent agents, the meeting is finally cancelled, permanently.


Goodbye Guesswork: How Causal AI Will Rewrite the Logic of B2B Buying

Causal AI, AI systems designed to understand not just correlations but why things happen, represents a game-changer in the B2B buyer journey. When embedded into agentic systems, causal AI enables machines to simulate, predict, and explain the downstream consequences of each decision a buyer could make, from initial needs assessment to supplier selection. Unlike today’s probabilistic models, which infer from past data, causal AI can identify root causes and project likely outcomes, transforming needs analysis from reactive intake to proactive strategy.


In B2B contexts, this means agentic systems won’t just match features to requirements; they’ll rationalise needs, test assumptions, evaluate trade-offs, and eliminate options that don’t contribute to the buyer’s core objectives. The result? ABM strategies built on vague personas and content journeys will be rendered obsolete by precise, machine-driven logic that narrows choices with surgical clarity. In this future, ambiguity, bias, and internal noise give way to clean, causal reasoning, accelerating procurement cycles, enhancing stakeholder confidence, and forcing suppliers to meet a far higher bar of verifiable value.


Scrap the ICP: Your Ideal Customer Is Now an Algorithm

In a world where buyer-side agentic AI drives or outright controls the procurement process, the once-sacred Ideal Customer Profile (ICP) of ABM lore becomes a relic of a human-centric past. You’re no longer marketing to personas, job titles, or organisational charts, you’re interfacing with autonomous agents programmed to optimise outcomes, not build relationships.


These agents don’t care about your brand story, your buyer journey mapping, or your finely tuned ICP targeting, they care about data integrity, feature-to-need alignment, cost-efficiency, and provable outcomes. If your go-to-market strategy is still built around who the human buyer is, you’re missing the point. The real "ideal customer" is now a machine running procurement logic, and unless your product metadata, integration specs, and performance evidence are tailored to that machine’s evaluation model, your relevance is algorithmically zero.


And here’s the kicker: by the time your SDR reaches out, the decision is probably already made. Agentic AI doesn’t wait for outreach, engage in top-of-funnel fluff, or respond to persuasion. It conducts its own exhaustive search, scrapes structured and unstructured data, and ranks vendors, often without a single human-to-human interaction. Supplier contact becomes a post-selection formality, not part of the decision-making process. If you’re not already embedded in the datasets and signals these agents are using, you’re not competing, you’re invisible.


No Borders, No Barriers: How Agentic AI Erases Language and Culture from the Buying Equation

Agentic AI is unbound by the linguistic and cultural friction that slows human decision-making in global enterprises. It translates, interprets, and communicates fluently across any language, instantly, eliminating misunderstandings, nuance gaps, and the delays that come from navigating multilingual stakeholder groups. In complex, multi-regional organisations, where procurement often stalls over misaligned expectations and cultural bias, AI agents bring unmatched clarity and speed.


But more than just neutralising barriers, agentic AI can actively evaluate supplier fit against the linguistic and cultural dynamics of the buying organisation itself, ensuring that vendors aren’t just technically aligned, but operationally compatible across geographies. It’s not just faster, it’s smarter, more inclusive, and radically more precise in matching global needs with global solutions.


Memory as Competitive Advantage: How Agentic AI Turns Corporate Intelligence into Buying Power

Agentic AI doesn’t just rely on public data, it thrives on the proprietary insights, institutional memory, and performance benchmarks locked within an enterprise’s own digital walls. By ingesting historical procurement outcomes, supplier performance records, internal KPIs, and strategic priorities, these AI agents build a contextual intelligence unmatched by any human decision-maker. They don’t forget past supplier failures, missed SLAs, or hidden costs. They remember what worked, what didn’t, and why, and they apply that memory instantly, at scale. Add to that the ability to plug into trusted external networks, such as vetted supply chain partners, industry benchmarks, or verified third-party performance scores, and suddenly, reliable data becomes the new currency in procurement. In this model, access to credible, interoperable insight becomes more valuable than brand reputation or persuasive salesmanship. The best pitch is no longer heard in a meeting, it’s proven in the data.


The Deal Isn’t Done at Signature: How Agentic AI Closes the Loop on Procurement Performance

In the agentic era, purchase decisions don’t end with a signed contract, they begin a new cycle of real-time evaluation, optimisation, and continuous learning. Unlike traditional procurement processes that lose visibility after onboarding, agentic AI embeds itself into post-purchase operations, actively monitoring implementation, usage, outcomes, and ROI. It tracks adoption rates, performance metrics, cost efficiency, and user satisfaction across the enterprise, turning every deployment into a data stream for future decisions. This creates a virtuous feedback loop where purchasing intelligence compounds over time. Agentic AI doesn’t just buy smarter, it learns from every transaction to buy even smarter next time. The procurement function is no longer episodic; it’s continuous, reflexive, and relentlessly focused on maximising value at scale.


Personalisation As We Know It Is Now Irrelevant

The cornerstone of ABM, personalisation, is suddenly a solution looking for a problem. AI buyers are immune to storytelling, blind to brand loyalty, and unimpressed by marketing flair. They don’t need “nurturing.” They need specifications, compliance data, price comparisons, integration maps, and risk scores. The very idea of “relevance” is being redefined from emotional resonance to technical compatibility.


In this environment, the age-old ABM playbook, content orchestration, stakeholder mapping, and tiered campaign frameworks, feels almost quaint. These constructs were designed for human buying groups, consensus decision-making, and high-touch engagement. But the AI agent doesn’t suffer from committee groupthink or internal misalignment. It needs inputs, not influence.


Marketing Must Now Serve Machines

The implications are stark. Marketing is no longer crafting messages for humans, it must now design systems that interface directly with autonomous agents. Product data needs to be machine-readable. Value propositions must be codified into logic trees. Commercial models should be API-ready. If your marketing cannot be consumed, parsed, and actioned by AI, it’s functionally invisible.


This isn't just a tech upgrade. It’s a philosophical reset. Marketing and sales must now think in terms of agent fluency, the ability to make a compelling, machine-verifiable case in environments where dialogue is replaced with data exchange. The goal is no longer persuasion. It’s configuration.


When Buyers Are Bots: Why Your Content Must Convince Machines, Not Marketers

In the age of agentic AI, factually rich, data-driven content isn’t just useful, it’s essential. Unlike human buyers who can be swayed by brand narratives, emotional appeal, or persuasive storytelling, AI agents operate with a different mandate: precision, logic, and verifiability. They consume content not as marketing collateral but as structured data sets. That means case studies with measurable outcomes, research with cited sources, proven ROI metrics, and product comparisons with clear benchmarks become the currency of influence. Verified claims trump marketing promises; empirical evidence outweighs anecdotal persuasion. For an agentic buyer, content must be machine-readable, logically structured, and rigorously substantiated to pass the threshold of trust and actionability.


But that’s only half the equation. As these autonomous buyers proliferate, seller-side organisations will need their own agentic counterparts, AI tools that can interpret how buyer-side agents scan, parse, and prioritise content. These 'agent-on-agent' systems will be essential to understand how machines rank relevance, weigh claims, and trigger recommendations. Sellers must move beyond personas and psychographics to architect content ecosystems optimised for algorithmic consumption. In short, it’s not just about creating better content; it’s about engineering conversational content interfaces, autopnomous dialogue for a machine-mediated market, content where agent consumption can be traced and measured, where your next buyer may never blink, but will always benchmark.


From Haggling to Algorithms: How Agentic AI Could Transform Traditional Procurement Negotiations

In an article by Remko Van Hoek and Mary Lacity in the MITSloan Management Review, Procurement in the Age of Automation the authors explore how leading companies like Walmart, Maersk, and Google are using automated negotiation technologies, such as e-auctions and AI chatbots, to transform procurement. These tools drive cost savings, increase speed and efficiency, and improve fairness and transparency for suppliers. By automating negotiations, businesses can handle thousands of supplier interactions simultaneously, shorten sales cycles, and unlock greater supply chain resilience.


Despite the benefits, adoption often faces internal resistance from buyers and business unit leaders, who fear loss of control or quality. To overcome this, successful companies follow six best practices:


  • Mandate consideration, not use, of automation

  • Make success visible

  • Prequalify suppliers rigorously

  • Treat new suppliers fairly

  • Use AI to engage overlooked tail-end suppliers

  • Build strong support structures like centers of excellence (COEs).


The key takeaway is that automation isn’t about replacing people, it’s about enhancing procurement strategy, scaling smart decisions, and enabling human stakeholders to focus on value, not repetitive tasks. The authors conclude that automation in procurement is here to stay, and the faster companies adapt, the more competitive advantage they’ll gain.

However, Agentic AI could potehntially take automated negotiations to the next level by adding true autonomy, intelligence, and decision-making capability to procurement systems.


While current tools like e-auctions and chatbots streamline supplier interactions, agentic AI introduces dynamic, goal-driven agents that can evaluate trade-offs, learn from outcomes, adapt strategies in real time, and execute end-to-end negotiations without human oversight. These agents won’t just automate processes, they’ll continuously optimise them based on organisational goals, market shifts, and supplier behavior. Unlike today’s static automation, agentic AI can negotiate on intent, not just input, making procurement not only faster and cheaper, but also smarter and more aligned with long-term enterprise strategy.


The End of ABM, or Its Rebirth? The Emergence Of Agent-Based Marketing

So, is this the death of ABM? Quite possibly. At least in its current form. ABM was built for complex sales cycles with large buying committees, human ones, an approach devised for the large technology sales of the 1980’s. But when decisions are made by agents that don't need persuasion, personalisation, or relationship-building, ABM becomes performance art without an audience.


Yet there’s a potential rebirth. A radically new ABM, Agent-Based Marketing, could emerge. It would focus not on personas, but on protocols; not on storytelling, but on system interoperability. It would mean marketing teams collaborating with engineering to create product information schemas optimised for autonomous consumption.


Prepare for Obsolescence, or Transformation

If your marketing and sales strategy still assumes that the buyer will read your whitepaper, respond to your campaign, or hop on a call with your SDR, you are not just behind, you are irrelevant. The future is here, and it's making decisions without you.

To survive, B2B leaders must pivot from intent data to interaction protocols, from engagement metrics to machine interfaces. The winners will be those who design for a world where buyers don’t browse, they execute.


Welcome to the post-ABM era. Welcome to Agent-Based Marketing.

Looking ahead…

From Chatbots to Cognition: Why First Principles Thinking Will Rewrite the Future of AI, and Business

In an AIGO article byPeter Voss, "AGI from First Principles," argues that the current path toward Artificial General Intelligence (AGI) is misguided, and that true progress requires a radical shift from scale-driven machine learning to First Principles Thinking (FPT). The piece lays out a roadmap from today's chat-based assistants and agentic AI toward Cognitive AI, and ultimately, AGI: a system with human-like reasoning, adaptability, and autonomy.

FPT demands that we strip intelligence down to its cognitive essence, conceptual learning, contextual reasoning, real-time adaptation, and autonomous metacognition. The article criticises the brute-force scaling of large language models (LLMs) as inherently limited, unable to replicate the layered, interactive, and deeply efficient learning of the human brain. Instead, it champions Cognitive AI, or DARPA’s so-called “Third Wave” of AI, which integrates symbolic reasoning (System 2) with pattern-matching (System 1), enabling continuous, real-time learning and dynamic goal orientation.


Implications for Commercial Strategy, Procurement, and B2B Marketing:

This shift has profound consequences for commercial operations:


  • Procurement will evolve from automated negotiations to fully cognitive agents that don’t just optimise deals, they reason, prioritise, and simulate trade-offs in complex scenarios with minimal oversight. They will understand context, learn from outcomes, and improve continuously, something today’s e-auctions or scripted bots can't do.

  • Account-Based Marketing (ABM) and B2B marketing as a whole will be forced to move beyond persona-based targeting and content orchestration. Cognitive AI on the buyer side will render personalisation obsolete, favouring machine-to-machine clarity, structured value communication, and empirical proof over emotional storytelling.

  • Enterprise Strategy will need to shift from “selling to people” to “proving value to systems.” Commercial success will depend on building cognitive-ready ecosystems: logic-based product representations, machine-readable case studies, quantified outcomes, and interoperable data models. Influence will shift from persuasion to performance, measured, verified, and continuously evaluated by machines.


Agentic AI is just the beginning, it’s still in it's infancy but the time to consider it's impact and other AI developments is now.. However, today Agentic AI doesn't think, it just acts on patterns and instruction, the real transformation will come when businesses build for thinking machines, not just automated workflows. First Principles Thinking reveals that scaling current AI tools won't get us there. But understanding and engineering intelligence, layer by layer, just as humans acquire it will. Companies that grasp this shift early will not just survivel, they'll define the next era of competitive advantage.


Cognitive AI and AGI: Current Development and Future Outlook

Cognitive AI is progressing steadily, aiming to emulate human-like reasoning and adaptability. This approach integrates symbolic reasoning with pattern recognition, enabling AI systems to understand context, learn from experiences, and make decisions in complex environments.


Artificial General Intelligence (AGI), which aspires to perform any intellectual task a human can, remains a long-term goal. Expert predictions about its development and possible application vary:

A cautionary note

An ISG Research article by Robert Kugel, Buyer Beware: The Evolution of Agentic AI in Business Software explores the emerging role of agentic AI, autonomous software agents capable of executing business processes, making decisions, and interacting dynamically with their environment. Unlike traditional AI or bots, agentic systems follow a “sense-analyze-decide-act” model, using data to operate independently across multiple systems and tasks. The article outlines a taxonomy of agents, from simple task-based systems to complex utility- and goal-based agents that require extensive training and data.


While agentic AI holds immense potential to boost productivity and performance, the article warns of premature hype ("agent washing") and highlights that many vendors rebrand basic automation as agentic. The true deployment of agentic AI faces hurdles: data quality, governance, ethical concerns, and organisational readiness. Agents must be tested, trained, and aligned with outcomes through either heuristic approaches or large action models (LAMs), requiring significant enterprise maturity in data operations.


Despite current limitations, ISG predicts agentic AI will gain widespread adoption by 2028, offering transformative potential for digital business. Enterprises are urged to act now—building the data foundations and governance frameworks necessary to support this next wave of intelligent automation, or risk falling behind.

Footnote

While agentic AI is accelerating and automating large portions of the buying process, human oversight remains crucial, at least for now, particularly in high-stakes scenarios. In cases involving novel solutions, unproven technologies, or decisions with significant financial or reputational consequences, human judgment still anchors final approval. Buyers may rely on AI agents to construct the option set, filter noise, and even recommend optimal paths, but they will often retain veto power where strategic risk is high. That said, this collaborative model, AI as consigliere, human as decider, is likely a transitional phase. What we are witnessing today is only the visible tip of the agentic iceberg.


As agents evolve in their ability to simulate reasoning, validate claims autonomously, and weigh abstract variables like trust or brand equity, the locus of decision-making will continue to shift. The future is not man versus machine, but machine as the lead analyst, strategist, and negotiator in a procurement environment built increasingly for speed, certainty, and scale.


In other posts, we explore the commercial strategies and operational innovations suppliers must adopt to stay relevant, and win in a world where agentic AI shapes, drives, and finalises purchase decisions. We explore how the power of structured data, machine-readable content, and intelligent information architecture becomes the new frontline of influence.


It's no longer about messaging just for humans or relatively simple search technologies; it's about building ecosystems that AI agents can access, understand, and act upon. From quantified value propositions and interoperable data sets to persistent visibility across digital channels, suppliers must now think like B2C giants ensuring brand presence and mental availability not just for people, but for machines. It’s about enabling the agentic customer journey with continuous streams of support, performance data, and decision-ready insight, long before a transaction occurs, and long after it concludes. Influence no longer lives in the pitch, it lives in the pipeline of data and information AI can trust throughout all commercial disciplines.

Useful sources and references:

Enterprise AI Today - List of case studies, insights and news Revealing the GTM Strategies of the Top AI Consulting Firms. Enterprise AI Today - Versant - Big Five Consulting: Betting Billions on AI Partnerships - Explore how the Big 5 consulting firms leverage strategic AI partnerships, from McKinsey's 1,000+ partner ecosystem to Bain's exclusive OpenAI alliance, transforming how they deliver enterprise AI value.

Bain & Company - Agentic AI: Believe the Hype - Bain’s Chuck Whitten explains why agentic AI will transform not only corporate operations but many aspects of daily living.

HBR - What Is Agentic AI, and How Will It Change Work? - From the early days of mechanical automatons to more recent conversational bots, scientists and engineers have dreamed of a future where AI systems can work and act intelligently and independently. Recent advances in agentic AI bring that autonomous future...

UC Berkley - The Next “Next Big Thing”: Agentic AI’s Opportunities and Risks - Agentic AI is poised to revolutionise industries, handling complex tasks with human-like decision-making, but with great power comes great risk.

Accenture - Leveraging the hive mind - Harnessing the Power of AI Agents

PwC - Agentic AI – the new frontier in GenAI - An Executive Playbook

Open AI - Practices for Governing Agentic AI Systems - Agentic AI systems, AI systems that can pursue complex goals with limited direct supervision, are likely to be broadly useful if we can integrate them responsibly into our society. While such systems have substantial potential to help people more efficiently and effectively achieve their own goals, they also create risks of harm. In this white paper, we suggest a definition of agentic AI systems and the parties in the agentic AI system life-cycle, and highlight the importance of agreeing on a set of baseline responsibilities and safety best practices for each of these parties.

MIT, Shaping The Future Of Work - Project Synicate: Two Models For Agentic AI - “In an op-ed for Project Syndicate, Daron Acemoglu argues that “agentic AI” is a crossroads moment for AI development. AI advisors could realize the promise of AI to support human decision-making, but autonomous AI agents could also accelerate the displacement of workers, increase inequality, and undermine human agency.”

ISG Research - Buyer Beware: The Evolution of Agentic AI in Business Software - A practical assessment of the realities of agentic AI and the challenges of implementation and use.

MITSloan, Procurement in the Age of Automation - Automated negotiations can cause anxiety among business leaders, buyers, and suppliers despite the benefits.

Understanding Artificial Intelligence - Fundamentals, Use Cases and Methods for a Corporate AI Journey, Ralf T Kreutzer and Marie Sirrenberg, Springer

Example applications of Agentic AI in business

Coupa's AI-Driven Procurement Platform, an AI agent network that manages data objects, interacts with buyers and sellers, and provides transaction recommendations. By leveraging community-generated AI, Coupa's platform analyzes $7 trillion of anonymised customer data to offer insights that improve business decisions. This approach has enabled companies like Caterpillar and Coca-Cola to optimise their procurement strategies effectively. Coupa

Ivalua's Unified Procurement Platform, Ivalua emphasises the importance of a unified procurement platform that centralises and standardises data across the Source-to-Pay (S2P) lifecycle. By integrating agentic AI, Ivalua's platform enhances accuracy, compliance, and efficiency in procurement processes, allowing for more informed decision-making and streamlined operations. You can also access ‘Agentic AI in Procuremenrt Guide’ throught this link - Ivalua

Moveworks' Autonomous Workflow Management, Moveworks utilizes agentic AI to automate complex workflows with minimal human oversight. Their AI systems can understand user needs based on business context and autonomously formulate plans to accomplish specific goals. This capability has been applied across various business operations, including IT support and HR processes, leading to increased productivity and integration. There’s also a guide and some case stdies you can access through this link Moveworks

Akira's Agentic AI in Procurement, Akira has implemented agentic AI to revolutionise procurement analytics. Their platform enables autonomous agents to coordinate intelligent workflows, extract insights, and generate real-time recommendations from complex data. This approach has improved decision-making and reduced manual intervention in procurement tasks. Akira

Deloitte and EY's Agentic AI Platforms, Deloitte and EY have introduced agentic AI platforms developed with Nvidia, offering digital agents to aid in tasks like financial management and tax compliance. These platforms aim to enhance productivity, reduce costs, and transform business operations, potentially leading to new commercial models based on outcomes rather than hours worked. Deloitte Zora AI and EY.ai - While these platforms are primarmily focused today on finance, tax and risk, they are just the start of what’s likely to be a much more comprehensive roll-out of agentic AI in all buness processes.

IBM employs agentic AI within its procurement operations, utilising AI agents to automate tasks such as supplier evaluation, contract management, and order processing. The same technology is available to IBM customers, Watsonx Orchestrate Procurement Agents. These agents can independently assess supplier performance, negotiate terms, and ensure compliance with procurement policies, thereby reducing manual intervention and accelerating decision-making.

SAP is developing agentic AI solutions aimed at transforming supply chain and sales operations. Their AI agents are designed to determine optimal pricing strategies, manage inventory levels, and coordinate delivery schedules, all while adapting to real-time market conditions. This approach aims to enhance responsiveness and efficiency in procurement activities. SAP Joule AI

Amazon has implemented agentic AI to automate various aspects of its procurement and customer service operations. AI agents manage supplier interactions, monitor inventory levels, and process orders, ensuring timely replenishment and efficient supply chain management. This automation reduces human error and accelerates procurement cycles. Amazon Bedrock, Amazon DeepSeek R1 SageMaker - Forbes, Amazon Unleashes New AI Agents Ready To Take Over Your Daily Tasks


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