More AI Potential – The Third Wave of AI

Thoughts And Observations about Agentic AI:

"In just a few years, we've already witnessed three generations of A.I. … First came predictive models that analyze data. Next came generative A.I., driven by deep-learning models like ChatGPT. Now, we are experiencing a third wave — one defined by intelligent agents that can autonomously handle complex tasks."  Marc Benioff – U.S. business entrepreneur co-founder, chairman and CEO of the software company Salesforce .

“The next frontier of artificial intelligence is agentic AI, which uses sophisticated reasoning and iterative planning to autonomously solve complex, multi-step problems. And it’s set to enhance productivity and operations across industries. … Agentic AI systems ingest vast amounts of data from multiple data sources and third-party applications to independently analyze challenges, develop strategies and execute tasks. Businesses are implementing agentic AI to personalize customer service, streamline software development and even facilitate patient interactions.” Posted October 22, 2024, on the NVIDIA Blog by Erik Pounds – U.S. business executive, leads product marketing/enterprise computing and AI at Nvidia.

“Generative AI and agentic AI are closely related. You couldn't have one without the other. Definitions vary, but in general, agentic AI refers to AI technology that's capable of performing agent-like behavior that can autonomously accomplish complex tasks on your behalf. Companies working on AI agents say they are intended to one day be digital coworkers or assistants to human workers in fields spanning from healthcare and supply chain management to cybersecurity and customer service.” Posted January 14, 2025, on Business Insider by Sarah Jackson– U.S. journalist.

“Today’s AI models perform tasks such as generating text, but these are ‘prompted’ — the AI isn’t acting by itself. That is about to change with agentic AI, or AI with agency. By 2028, 33% of enterprise software applications will include agentic AI, up from less than 1% in 2024, enabling 15% of day-to-day work decisions to be made autonomously. Intelligent agents in AI are goal-driven software entities that use AI techniques to complete tasks and achieve goals. They don’t require explicit inputs and don’t produce predetermined outputs. Instead, they can receive instructions, create a plan and use tooling to complete tasks, and produce dynamic outputs. Examples include AI agents, machine customers and multiagent systems. Intelligent agents in AI are nascent but quickly maturing - While agentic AI is still in early stages, it's not too soon to gain an understanding of the technology, determine how to manage risk and prepare your tech stack. The future of AI is about agency — and productivity -- By giving artificial intelligence agency, organizations can increase the number of automatable tasks and workflows. Software developers are likely to be some of the first affected, as existing AI coding assistants gain maturity. Agentic AI has the potential to significantly empower workers. It’ll enable them to develop and manage complicated, technical projects — whether micro automations or larger projects — through natural language. Intelligent agents in AI will change decision making and improve situational awareness in organizations through quicker data analysis and prediction intelligence.  Posted October 1, 2024, on the Gartner Blog by Tom Coshow – U.S. business analyst, senior research analyst at Gartner.

“Agents are smarter. They’re proactive – capable of making suggestions before you ask for them. They accomplish tasks across applications. They improve over time because they remember your activities and recognize intent and patterns in your behavior. Based on this information, they offer to provide what they think you need, although you will always make the final decisions. … Agents are not only going to change how everyone interacts with computers. They’re also going to upend the software industry, bringing about the biggest revolution in computing since we went from typing commands to tapping on icons. Agents won’t simply make recommendations; they’ll help you act on them.”  Bill Gates – U.S. entrepreneur, best known for co-founding Microsoft .

“(A survey conducted by Wolters Kluwer) shows that while 6% of finance leaders are currently employing agentic AI, a further 38% intend to adopt agentic AI in the next 12 months. With 44% of finance teams set to be using agentic AI in 2026, this represents an increase of over 600%. … Unlocking the potential of AI through hiring and data readiness -The survey showed that 85% of finance leaders would consider AI skills as important when recruiting for their finance function, with 11% considering AI skills to be of essential importance. 44% of respondents identified data readiness as the key driver for increasing AI adoption. 25% highlighted the need for AI-powered corporate performance management (CPM) technologies and 23% cited the need for AI training. … 40% of finance leaders cited increasing accuracy and reducing human error as the key advantage of AI adoption, with 36% identifying efficiency and productivity gains as the primary benefit.  Focusing on efficiency and productivity, 42% of respondents reported they expected to save 10% of working time (26 days) through AI usage in the next 12 months, enabling a greater focus on strategic outputs. 24% expected to see timesaving of 20% (52 working days) and 22% expected to save 5% (13 working days).”  Posted May 28, 2025, on Business Wire from Karen Abramson – U.S. business executive, CEO at Wolters Kluwer Corporate Performance Management & ESG Global Division.

 “While certain enterprises pilot small-scale “AI assistant” prototypes, others are grappling with how to orchestrate multiple agents across diverse and complex business processes. The recent emergence of agentic protocol frameworks like model context protocol (MCP) from Anthropic and agent to agent (A2A) from Google are trying to address interoperability and integration functionality, which adds another layer to the AI stack. …  Agentic AI is not just about using prompts or simple chatbots. In the current era of AI, with the availability of large language models (LLMs) and, more recently, large reasoning models (LRMs), AI agents have taken on wider implications and applications. Unlike traditional software that executes predefined instructions, agentic systems make adaptive, autonomous decisions grounded in reasoning.  They are not limited to being software entities that act to fulfill a specified goal.” Posted May 19, 2025, on CIO by Shail Khiyara – U.S. technologist, CEO of SWARM ,  and Sandeep Mehta – U.S. technologist, currently on the advisory board of EAIGG: Ethical AI Governance Group .

“The generative AI boom, catalyzed by OpenAI’s ChatGPT in late 2022, ushered in a new era of intelligent systems. But as businesses push beyond static language models, two paradigms have emerged in automation, central to the future of enterprise AI: AI agents and agentic AI. While both represent an evolution from generative systems, their operational scopes are redefining how organizations approach automation, decision-making and AI transformation. As enterprise leaders seek to integrate next-gen AI into their workflows, understanding the distinctions between AI agents and agentic AI for automation — and their distinct strategic advantages — has now become an operational imperative. …  Traditional AI agents are autonomous software systems that execute specific, goal-oriented tasks using tools like APIs and databases. They are typically built on top of large language models such as GPT-4 or Claude 3.5, and they excel in domains like customer service, scheduling, internal search and email prioritization. What differentiates AI agents from generative AI is their tool-augmented intelligence — they don’t just respond to prompts; they plan, act and iterate based on user goals set up earlier in the process.  …  Agentic AI represents an architectural leap beyond standalone agents. These systems are composed of multiple specialized agents — each performing distinct subtasks — coordinated by a central orchestrator or decentralized communication layer. Think of it as an intelligent ecosystem rather than a single-function intelligent tool.  Agentic systems shine in high-complexity environments requiring breaking down goals, contextual memory, dynamic planning and inter-agent negotiation. In applications like supply chain optimization, autonomous robotics and research automation, they outperform single-agent systems by enabling concurrent execution, feedback loops and strategic adaptability.  … While promising, both AI agents and agentic AI face notable challenges. AI agents struggle with hallucinations, brittleness in prompt design and limited context retention. Agentic AI, on the other hand, contends with coordination failures, emergent unpredictability and explainability concerns.  While the challenges are prevalent for both AI agents and agentic AI, emerging solutions are on the rise, and its only a matter of time before we work out the kinks and live in a world run by agents. …  Although we’re still very much in the infancy stages, AI continues its meteoric rise and the transition from reactive generative models to autonomous, orchestrated agentic systems marks a pivotal inflection point. AI agents have already proven their value in automating tasks, but agentic AI is redefining what’s possible in strategic domains — from scientific research to logistics and healthcare. For business leaders, organizations that master this next frontier of intelligence and automation won’t just become more efficient and productive — they have the chance to innovate, scale and lead in ways never been seen before.” Posted May 29, 2025, on Forbes by Sol Rashidi, MBA - U.S. technologist, head of technology-startup division at Amazon .

“Traditionally, DWS (Digital Workplace Services) has focused on delivering seamless IT support, enhancing user experience, and enabling hybrid work. But the next evolution demands more than just operational efficiency. It calls for intelligent, adaptive, and human-centric digital environments — a vision Agentic AI is uniquely positioned to fulfill. Human-Centered Design: AI agents should be built around user needs, preferences, and contexts. This means designing interfaces that are intuitive, personalized, and inclusive.  Trust and Transparency: Employees need to understand how agents make decisions and what data they use. Transparent, explainable AI fosters trust and boosts adoption. New Skills and Roles: The workforce must evolve to include AI-literate employees — capable of collaborating with digital agents, interpreting AI outputs, and co-designing workflows. DWS teams will need AI trainers, digital behavior analysts, and experience designers.”  Posted May 30, 2025, on ETCIO by Chidambaram Ganapathi – Indian technologist, AVP, and Head - Digital Workplace Services at Infosys .

“AI-powered digital labor—specifically AI agents—is rapidly becoming a reality, shaking up traditional approaches to workforce planning. As digital workers integrate into the workforce, companies must shift their focus from simply filling roles to securing skills and deciding whether humans or digital entities are best suited for certain tasks. This shift poses significant implications for human resources leaders and their business stakeholders as they transition to a hybrid human-digital workforce to supercharge both routine and complex tasks. …  - 85% of organizations have already started implementing AI into their business operations, with 47% leveraging AI for workforce planning and management.  In comparison to ChatGPT tools, AI agents are designed to collaborate directly with human talent or function autonomously for specific tasks. AI agents act independently in pursuit of goals, whether finding information, analyzing data, or running end-to-end processes. As AI agents mature and build upon LLM models, platforms, and data infrastructure, the workforce must adapt to balance human and digital roles and activities, necessitating a refined approach to talent management. Organizations will need not only to orchestrate work across the human-AI continuum, but to evolve their talent strategies, approach to upskilling, performance measurement, workforce planning, and other traditionally human-centered talent processes to span both the human and digital labor constructs.  Companies reported an average increase in productivity by 35% after integrating AI agents into their regular workforce operations.  62% of organizations are using AI to identify skill gaps and develop targeted upskilling programs to fill those gaps. 69% of companies plan to integrate AI-powered continuous learning platforms within the next two years to ensure their workforce remains agile and adaptive. 77% of executives believe that AI will necessitate significant investment in upskilling and reskilling programs for their workforce. Many organizations report that strategic workforce planning initiatives generate cost savings of an average of 10 percent of their annual labor budget through minimized attrition, optimized staffing, and improved resource allocation.” Posted May, 2025 on the KPMG Website. Survey data compiled by KPMG.

“Agentic AI has what we think of as six senses: Purpose: It is purpose-built, for a specific task or set of tasks. Interoperability: It can go across platforms without being “told” which specific platform it should go to because it will learn which platforms have the right information or tools to do something. Contextual Adaptation: It has the ability to adapt to a specific context, so you don't have to explain to it what to do; its purpose defines the systems and parameters in which it works. Learnability: It continuously improves itself by examining the outcomes of its actions, learning from them and making adjustments to its processes. Continuous Optimization: Through its continuous examination and improvement of its own processes, it always works to its own highest potential of execution. (And when it notes something “off,” it fixes itself.) Autonomy: It does all of this without a human being behind the dashboard telling it what to do.  … Agentic AI is notable for its autonomous decision-making. This occurs within its parameters or its purpose, of course; you can limit the autonomy.” Posted January 28, 2025, on Forbes by  Koray Köse – U.S. technologist, CEO & Founder of KŌSE ADVISORY.

“Agentic AI systems promise to transform many aspects of human-machine collaboration, especially in areas of work that were previously insulated from AI-led automation, such as proactively managing complex IT systems to pre-empt outages; dynamically re-configuring supply chains in response to geopolitical or weather disruptions; or engaging in realistic interactions with patients or customers to resolve issues.”  Posted December 12, 2024, on HBR by Mark Purdy – U.S. economist, Managing Director of Purdy & Associates.

 “Unlike traditional GenAI tools that simply assist users with information, agentic AI will proactively resolve service requests on behalf of customers, marking a new era in customer engagement … Organizations will need to rethink their approach to managing inbound service interactions, preparing for a future where AI-driven requests become the norm. In this future, automation will need to become the dominant strategy for all service teams. … As customers increasingly leverage agentic AI-powered agents to initiate, manage, and negotiate service requests on their behalf, service teams must adapt to this transformative shift, embracing new roles and skills to effectively collaborate with these intelligent systems. … The integration of agentic AI can lead to the creation of new roles focused on AI management and the evolution of existing customer service positions towards complex problem-solving and personalized interactions… By fostering a culture of continuous learning and adaptation, companies can reassure their workforce and ensure that employees are equipped to work alongside AI technologies, ultimately enhancing collaboration and service quality.” Daniel O’Sullivan – U.S. business analyst, senior director analyst at Gartner .

“As AI matures, the availability of so-called “digital labor” is exploding, expanding the very definition of a qualified workforce. What was once the exclusive domain of human talent has now been joined by AI agents capable of handling many tasks onc considered beyond the reach of automation—and as a result, according to Salesforce CEO Marc Benioff, the total addressable market for digital labor could soon reach the trillions of dollars.   …  Emerging research out of Harvard Business School and the Digital Data Design Institute shows that AI agents are fast becoming much more than just sidekicks for human workers. They’re becoming digital teammates—an emerging category of talent. To get the most out of these new teammates, leaders in HR and procurement will need to start developing an operational playbook for integrating them into hybrid teams and a workforce strategy.”  Posted May 22, 2025, on HBR by  Jen Stave, PhD – U.S. technologist, chief operator of the Digital Data Design Institute at Harvard (D^3) , and Ryan Kurt – U.S. technologist,  lead AI and digital labor advisor for the staffing and recruiting Industry at Salesforce , and John Winsor  - U.S. entrepreneur,  co-author of Open Talent: Leveraging the Global Workforce to Solve Your Biggest Challenges, founder and chairman of Open Assembly , executive fellow at Harvard Business School ’s Digital, Data, and Design Institute.

“Most companies are still figuring out how to deploy even one AI-powered agent that can perform a task autonomously or in coordination with humans. But developers are creating protocols to harness these agents into teams that handle everything from customer service and coding to supply chain, logistics, finance, marketing and business strategy. Given the pace of innovation and the time it takes for organizations to adapt, companies will do themselves a favor by getting ready now for multiagent systems increasingly available later this year.  Accenture’s chief AI officer, Lan Guan, says only 10% to 15% of her clients currently use multiagent systems, but she expects that percentage to exceed 30% within 18 to 24 months. The professional services company has created a 15-agent system used for marketing, for example, comprising three “super agents” that are responsible for coordinating 12 agents trained for specific tasks. It can plan a marketing campaign around a topic such as “2025 trends,” conducting research, identifying similar past campaigns and addressing questions like a human would, according to Guan.  All told, Accenture has more than 50 multiagent systems today for a range of industries and markets and expects that number to hit more than 100 by the end of the year. The firm said customers such as carmaker BMW, consumer-brands company Unilever and sports giant ESPN are currently adopting these systems.  … A true multiagent system, Recchia said, involves agents that dynamically reason, negotiate or collaborate in real time without requiring human-defined workflows, explicit prompts or manual coordination. In other words, the agents take initiative, adapt to new information and interact fluidly with other agents without waiting for human instruction. …  Companies can start to prepare for multiagents systems simply by creating standard, stand-alone agents. Once the proper protocols are ready, companies can orchestrate these agents into tackling complex, collaborative systems.  Principal Financial Group has embedded individual AI agents across domains including software engineering co-pilots, claims summarization and post-call analytics, according to Chief Information Officer Kathy Kay. They largely operate within defined scopes, but the investment management and insurance company is actively building the technical foundation to support agent-to-agent collaboration, Kay said. That means developing data pipelines and governance models. Workflows will also have to evolve to accommodate real-time collaboration between humans and intelligent, adaptive AI systems. … ‘These are not isolated functions,’ Kay said. ‘They are systems of tasks that, when connected through intelligent agents, can drive faster insights and better outcomes across the enterprise.’” Posted May 5, 2025 on The Wall Street Journal by Steve Rosenbush – U.S. journalist, chief of the enterprise technology bureau at WSJ Pro.

“As its name suggests, agentic AI has ‘agency’: the ability to act, and to choose which actions to take. Agency implies autonomy, which is the power to act and make decisions independently. When we extend these concepts to agentic AI, we can say it can act on its own to plan, execute, and achieve a goal—it becomes ‘agentic.’ The goals are set by humans, but agents determine how to fulfill those goals.” Posted on TMT Predictions 2025 by the Deloitte Center for Technology, Media & Telecommunications.

“Agentic AI is more than hype. The combination of autonomous workflows and intelligence will change how organizations operate. When thoughtfully deployed, it can: Improve agility. Agents can adapt quickly to changing inputs—whether it’s shifting customer needs, internal goals, or operational disruptions—helping organizations stay responsive in dynamic environments. Strengthen coordination. By working across systems and silos, agents can streamline handoffs, flag breakdowns, and help teams stay better aligned around shared objectives. Increase efficiency. Agents reduce manual effort by handling repetitive or time-consuming tasks—freeing up time, reducing friction, and speeding up workflows. Reinforce shared values. When built with purpose, agents can carry out decisions that reflect what the organization stands for—amplifying trust, consistency, and cultural alignment. When agents handle repetitive tasks, humans can spend more time on creativity, connection, and problem-solving. … Agentic AI is stirring up excitement—and with it, a wave of unrealistic expectations. The problem isn’t just that the hype is premature. It’s that it glosses over how much effort is required to make these systems truly work in the messy reality of business. The idea of fully autonomous AI agents sounds appealing. But in practice, most deployments fall short—not because the tools aren’t capable, but because the environments they operate in aren’t ready. Building Agentic AI that’s useful, responsible, and aligned with human needs means doing the hard work of integration. That includes threading agents into the organization’s data architecture, business logic, and operational workflows—something far more complex than dropping in a chatbot or assigning a task bot. Let’s unpack a few of the most common misconceptions: “The AI can manage itself.” Not exactly. These systems can follow steps and pursue goals—but they don’t know your values, edge cases, or cultural norms unless you explicitly teach them. “We can replace entire departments with agents.” In narrow, well-defined processes, maybe. But most real-world work is cross-functional, exception-heavy, and full of emotional nuance. Agents aren’t built for that—yet. “It’s like hiring another team member.” It’s more like hiring a tireless, literal-minded intern. Fast and consistent, yes. But without judgment, empathy, or awareness of what’s unsaid.  The takeaway? Agentic AI isn’t plug-and-play. It needs to be trained, tuned, and deeply embedded into your systems. That includes connecting to data sources, orchestrating across tools, and aligning to how work really flows. And all of that has to be shaped around human needs—not just machine logic.  …. Agentic AI enables systems to make decisions and take action across tasks—but it acts on goals and logic, not values or human judgment. That’s why leaders must treat it as a design challenge: shaping agents to reflect the organization’s purpose, align with its values, and know when to pause, escalate, or ask for help.”  Posted June 10, 2025, on Humanity At Scale: Redefining Leadership Podcast by Bruce Temkin – U.S. business consultant, podcast host.

“The hype has been strong on agentic artificial intelligence (AI) and the potential business benefits are real. However, their greater autonomy means you can go off the rails without introducing guardrails from the start to reduce risk and avoid cost blowouts.  Sunil Agrawal, chief information security officer (CISO) at AI platform Glean, says it’s worth the fight. AI agents can reshape how work is done, helping to surface and make sense of needed data. But scaling these systems securely and responsibly is critical. Agents must respect user roles and data governance policies from day one, especially in highly regulated environments, and observability of what’s going on is crucial. This covers what data they access, how they reason and which models they rely on. ‘AI agents are only as reliable as the data they’re built on,’ Agrawal says. ‘Ground them in accurate, unified internal knowledge. And threats like prompt injection, jailbreaking and model manipulation require dedicated defenses. A strong governance framework helps ensure agents operate safely, ethically and aligned with organizational policy.’” Posted May 30, 2025 on Computer Weekly by Fleur Doidge – U.K. journalist.

“Any AI augmented automation will be subject to risks of data misinterpretation, bias, flawed logic, overconfident speculation (collectively referred to as hallucination) along with temporal and context errors and malicious attempts such as model or prompt poisoning and traditional cyberattacks.  Further, in highly regulated environments (e.g., HIPAA in healthcare, SOX in finance), companies demand traceability with audit trails. AI agents that autonomously gather and act on data must leave a digital footprint for each decision: Traceable decision logs. If an agent rejects an invoice or flags a suspicious claim, logs should clarify why it took that action (transparency in their reasoning logic). Controlled delegation that maintains human control over high-stakes decisions while automating low-risk activities to agents.  Circuit breakers-To prevent agents from making critical decisions without verification. Continuous monitoring and robust error handling, including making compensating adjustments when detected or through human oversight.  Data governance-Access control, anonymization strategies and safe harbor approaches help ensure compliance even when agents are cross-referencing multiple data sources. Agent supervision- Human in the loop - While greater autonomy can reduce manual work, it also raises questions of safety, security, reliability and ethical oversight. If humans are only asked to intervene in exceptional cases, they may lack context when an alert finally arrives and in critical infrastructure cases context switching is fraught with risk of last minute human intervention.  Strategies to address this issue include:  Periodic “checkpoints” and inserted friction. Agents pause at specific milestones for human review, especially in risk-sensitive domains like finance or healthcare with vulnerable populations. Additional verification steps can be inserted in those high-stakes use cases.  Maintaining operator engagement-A state of flow is essential to human engagement. Just dashboards or notifications that keep operators aware of ongoing tasks, even when no intervention is requested, may not be sufficient. They need to be augmented with training and gaming scenarios that maintain skills, interest and focus in machine-human hybrid workflows. Training and cultural awareness. Protocols are needed for regular training of human operators to keep their AI awareness and skills up to date, incorporate their feedback into error handling and clear awareness of the systems limitations so the operators don’t over-surrender their agency.” Posted May 19, 2025, on CIO by Shail Khiyara – U.S. technologist, CEO of SWARM ,  and Sandeep Mehta – U.S. technologist, currently on the advisory board of EAIGG: Ethical AI Governance Group .

“Unlike chatbots, which require a human to type in a prompt before it can spit out a response, agentic A.I. can act on its own. A customer could create a complex goal, like predicting which factory machines will need maintenance or booking a trip, and the A.I. would automatically complete the required tasks. Or at least, that’s the idea. Most agentic A.I. is still in the ‘possibility’ stage.” Posted September 6, 2024 on The New York Times by Erin Griffith – U.S. journalist.

“Gartner analysts are projecting that by 2028, a third of enterprise software will include agentic AI — up from just 1% in 2024 — powering 15% of daily business decisions to be made autonomously by that time, (but) many of which never scale into production or end up failing during deployment. For context, 85% of AI projects fail. And when you ask the people building these tools what’s really going on, the consistent theme is that while they have AI agents, they don’t really have the ecosystem to support them. (According to Aishwarya Singh, SVP of Digital Collaboration Services at NTT DATA) - ‘The biggest economic bottlenecks include the high initial investment in infrastructure and technology, the cost of integrating AI with existing systems and the need for specialized talent to manage and maintain AI systems … Many leaders underestimate the time, effort and resources required for successful integration. Ignoring this can lead to project delays, cost overruns and suboptimal performance.’” Posted May 26, 2025 on Forbes by Kolawole Samuel Adebayo (KSA) – Nigerian journalist.

“Giving AI systems decision-making power is a huge step, and it won’t be easy to get that done. Small improvements will happen and will take time.”  Jonathan Frankle – U.S. technologist, chief AI scientist at Databricks .

“Authorizing AI agents to access apps and websites could be the last major challenge in building agents that can perform complex tasks for people. The AI ecosystem is working on the “plumbing” that will make such complex AI agents possible. … The large language models at the core of these agents are good enough for many tasks. But there is a growing emphasis on connecting the LLMs inside agents to a plethora of tools that they will need to get their jobs done. … Agents will need permission to access apps, APIs and websites if they are ever going to call an Uber or book a flight, the kind of expectation that has been established over the past year.  … The introduction of app stores in 2008 abruptly and broadly changed the norms by which people interact with the world. AI agents could be very close to triggering something just as big.”  Posted May 17, 2025, on the The Wall Street Journal by Steve Rosenbush - U.S. journalist, chief of the enterprise technology bureau at WSJ Pro.

“As agents become more widespread, more intelligent, and more sophisticated, it will likely change the way we think about computers in the first place – in the same way that the transition from a command line interface to a graphical interface completely revolutionized the way we interact with computers. … Generative AI Is just the beginning, AI agents are what comes next.”  Daoud Abdel Hadi – U.K. technologist, data scientist lead at Eastnets .

“AI agents will transform the way we interact with technology, making it more natural and intuitive. They will enable us to have more meaningful and productive interactions with computers.” Fei-Fei Li – U.S. technologist/academic, professor of computer science at Stanford University , co-founder and CEO of World Labs .

“The way humans interact and collaborate with AI is taking a dramatic leap forward with agentic AI. Think: AI-powered agents that can plan your next trip overseas and make all the travel arrangements; humanlike bots that act as virtual caregivers for the elderly; or AI-powered supply-chain specialists that can optimize inventories on the fly in response to fluctuations in real-time demand. These are just some of the possibilities opened up by the coming era of agentic AI.” Posted December 12, 2024 on HBR by Mark Purdy - U.S. economist, Managing Director of Purdy & Associates.

“Think augmented, not autonomous agents. It took Waymo $30B and lots of years to deploy self-driving cars in one city. Decision-making by AI won’t be delegated overnight.” Nenshad Bardoliwalla – U.S. technologist, director of product management – Vertex AI platform at Google Cloud .

“The printing press democratized knowledge. The internet connected humanity. AI, in its agentic form, has the potential to amplify human capabilities in ways we’re only beginning to comprehend.”  Pascal BORNET – U.S. technologist, business consultant, author.