Agentic AI: Transforming Business Strategy and Operations

How agentic AI is transforming the very foundations of business strategy

Business is on a never-ending quest to boost efficiency, cut costs, and increase productivity. Some of the earliest known businesses -- ancient Mesopotamian traders -- inspired the invention of writing. (Record keeping -- now that's a competitive advantage!) Similar needs have existed in every economic period. The big difference now is that AI technology can boost these efficiencies in new and exponentially profitable ways. Agentic AI is at the core of this efficiency boost. According to Dan Priest, chief AI officer at PwC US, "agentic AI refers to AI systems that can autonomously perceive, decide, and act within a defined scope to achieve goals, capable of collaborating with humans, systems, or other agents." (PwC, a.k.a. PricewaterhouseCoopers, is one of the "Big Four" -- the world's four largest professional services firms.) Also: 5 ways to be a great AI agent manager, according to business leaders Agentic AI systems are different from the previous generation of algorithmic business management systems we've been using for the past few decades. Agentic AI can understand context, respond to changing situations without running from a script, and work toward defined goals autonomously. Compared to traditional automation (and some human managers), agentic AI systems can be flexible, handle ambiguity, and make informed decisions at the speed of business operations. Agentic AI, Priest says, "helps organizations operate with greater speed, intelligence, and scalability, fundamentally transforming how work gets done and decisions are made." However, you can't simple wave a magic wand and get enterprise-wide agentic AI that works perfectly. There are many challenges, including the existing technical debt deeply entrenched with legacy tools and processes, aversion to change, regulatory challenges, and lack of understanding and technical AI skills within the organization. "Common barriers to achieving integrated agent systems include fragmented data environments, lack of interoperability between tools, and siloed organizational structures," says PwC's AI expert. Ironically, the implementation process itself can hinder successful AI adoption. Many companies start by following an IT best practice: implementing a new system in small increments. Unfortunately, the most helpful AI systems thrive on cross-organizational information, so the stepwise approach often results in fragmentation, inefficiencies, and pushback among stakeholders. "Overcoming these challenges requires not just technology upgrades, but also cultural and operational shifts to allow for cross-functional alignment and scalable integration," Priest explains. "Additionally, concerns around security, compliance, and governance can slow adoption, especially in regulated industries." Also: The AI complexity paradox: More productivity, more responsibilities To successfully deploy agentic AI enterprise-wide and experience its benefits, managers need to reevaluate business processes, develop cross-functional coordination strategies, get full executive-level buy-in, and foster cultural change throughout the organization. It's natural for managers to initially be reluctant about giving up human processes to a machine. However, the key to successful deployment is proof of concept (POC). PwC's AI guru says, "POCs matter more than ever, especially in environments where skepticism still runs deep." By initiating early-stage deployments that showcase the benefits and smooth transition to AI-based operations, the technology itself can demonstrate its effectiveness and benefits. Also: 4 questions to ask yourself before betting on AI in your business - and why "The path from proof-of-concept to enterprise-scale AI starts well before the POC itself," suggests Priest. "It begins with a smart strategy. Success hinges on picking the right opportunities: high-potential, high-certainty use cases where AI is well-positioned to deliver real value. That early judgment call, where leaders are placing their bets, is what separates organizations that scale AI from those that stall out. When you choose wisely, you set the stage for a POC that isn't just a test of feasibility, but a demonstration of tangible business impact." Naturally, there will be failures at this stage. But the key is not misdiagnosing failures as AI failures when the root cause can be traced to errors in planning or strategy. Since POCs need to generate real value early, be sure to find ways to measure that value so that you can turn what might be claims of success into tangible, measurably provable successes. Achieving buy-in can be a challenge. One side-effect of improved efficiency and agentic AI deployment is often a reduction in job security for the very stakeholders who might champion such a deployment. Although the company's bottom line might benefit, individual employees often fear the change associated with enterprise-wide AI adoption. To counter this concern, Priest advises business leaders to look for indications that team members are willing or enthusiastic about being assisted by AI. He says, "Successful adoption hinges on human openness to using it." Also: AI agents will threaten humans to achieve their goals, Anthropic report finds Building trust in AI agents hinges on humans believing there's a meaningful value proposition at the end of the AI journey. People need to see clear benefits, whether it's efficiency, insight, or new capabilities. Trust isn't just about performance, Priest says, "It's about relevance. If users don't believe AI is working in their interest or delivering tangible value, skepticism will grow, regardless of how advanced the technology is." PwC's AI guru tells ZDNET, "We believe AI agents should be used to empower people, not replace them. The ingredients required of a great team are ones that AI agents are not able to replicate, which include deep specialization and expertise, diversity of thought and opinion, and the ability to be forward-thinking and creative." He recommends that leaders prepare their people for an AI-enabled future, which involves learning to work alongside agents, to unlocking value from data, to building high-performing teams where humans and agents collaborate to drive innovation. Also: How I personalized my ChatGPT conversations - why it's a game changer AI agents can augment the workforce by taking on routine, repetitive tasks, allowing employees to focus on more strategic, creative, and value-generating work. They can serve as intelligent assistants by helping with tasks like research, summarization, workflow automation, and decision making. "That kind of augmentation enhances productivity," Priese says, "While preserving the human judgment and context that machines can't replicate." PwC helps clients integrate AI agents into their workforce strategies. When asked to identify practical success stories, the company shared three examples in technology, hospitality, and healthcare. Technology: A major technology company reimagined customer engagement by deploying an AI agent-powered, omnichannel contact center. With predictive intent modeling, adaptive dialogue, and real-time analytics, PwC says the system reduced phone time by nearly 25%, cut call transfers by up to 60%, and boosted customer satisfaction by approximately 10%. Hospitality: A large hospitality company streamlined management of its brand standards across its global portfolio by deploying agile workflows within a modern, AI-powered platform. Intelligent agents now automate updates, approvals, and compliance tracking, which has reduced review times by up to 94%. Also: I'm an AI tools expert, and these are the only two I pay for (plus three I'm considering) Healthcare: A global healthcare company transformed cancer care by deploying agentic AI workflows across oncology practices. Intelligent agents streamlined clinical and operational processes. They automated the extraction, standardization, and querying of unstructured documents. This made it about 50% easier for doctors and researchers to find useful clinical information for precision treatments and studies. It also drove a nearly 30% reduction in staff administrative burden through AI-powered document search and synthesis. Infrastructure and governance go hand in hand. Agents, by their very nature, must travel across organizational units and communicate among disciplines and systems. As soon as interoperability is introduced at that level, technical compatibility becomes a major challenge and requirement. Standards, modular systems, and open source implementations can reduce long-term risks and increase compatibility and maintainability. PwC recommends enterprises invest in scalable, secure platforms that support orchestration, observability, and integration across systems. This includes robust data pipelines, APIs, and governance frameworks to help agents operate reliably and responsibly at scale. Also: What are AI agents? How to access a team of personalized assistants "Effective governance frameworks for AI agents combine clear accountability, robust oversight, and alignment with regulatory standards," says Priest. "Principles like transparency, explainability, data privacy, and bias mitigation should be embedded into both the technical architecture and organizational policies." This is an ongoing process. Incorporate reviews, model validation, and include human-in-the-loop mechanisms to help maintain control as agents scale. PwC predicts that, over the next two years, agentic AI will transform how teams operate. Intelligence will become an intrinsic part of business, leading to better decisions, more informed leaders, and highly specialized experts. "I'm excited about this period because it marks the beginning of a high-performance era, where agents elevate teams to become the smartest in the history of humanity," Priest says. Also: Managing AI agents as employees is the challenge of 2025, says Goldman Sachs CIO Looking ahead five years, agentic AI will likely evolve into a foundational layer of enterprise infrastructure. These agents will become increasingly autonomous, capable of continuous learning, adapting to business goals in real time, and collaborating seamlessly with humans and other agents. Priest tells ZDNET, "With these changes, it's important to remember the big picture. The shift we're experiencing isn't temporary, it's foundational and won't go away." Are you exploring agentic AI? Have you already begun integrating AI agents into your workflows? What challenges have you faced or do you anticipate when it comes to adoption, governance, or employee buy-in? Are there specific use cases where you think AI agents could have a real impact in your business? Let us know in the comments below.

Moving beyond AI agent hype: The execution gap that's holding enterprises back

There's still a significant gap between AI experimentation and real-world business impact. Increasingly, that gap is being measured in actual competitive edge. There's a playbook for that, says Matthew Kropp, CTO, managing director and senior partner at BCG. As gen AI matures -- especially with the rise of agentic AI -- organizations need to understand how to maximize its potential value, and responsibly deploy these new AI-powered teammates at scale. That requires zeroing in on the organization's focus, and three interconnected value plays. "Using our 'deploy, reshape, invent' framework, we help clients identify clear goals from the top," says Kropp. "We take a 10-20-70 approach: 10% algorithms, 20% tech and data, and 70% people and processes, which lets our clients set ambitious targets and create substantial value with powerful agentic AI backing them up." Case in point: BCG recently worked with global consumer goods company Reckitt to optimize marketing capabilities and increase productivity by changing workflows across marketing with custom automated solutions. They acclimated hundreds of marketers across categories and markets to a whole new way of working and new workflows on an innovative technology platform, and saw time spent on routine activities drop by up to 90%, while output quality improved two-fold. Meanwhile, the global cosmetics company L'Oreal reinvented the consumer experience and increased conversions five- to tenfold over traditional digital channels with a gen AI-powered beauty assistant. From deployment to reinvention Before reaching the more transformative phases, many companies are still in the early deployment stage -- integrating AI into existing tools and processes. Virtually every business application will soon include embedded AI, meaning every employee is going to be interacting with these tools in their day-to-day work. But simply turning on AI features isn't enough. "You're not going to see major impact if people keep doing things the same way," Kropp says. "A chatbot may help answer questions better, but that doesn't change the process of using the data within the company. That's where reshaping functions and workflows comes in." That next phase, "reshape," involves rethinking entire processes to reduce toil and enhance both quality and speed. It means redesigning workflows so an org can take full advantage of AI augmentation - not to remove humans, but to amplify what they do. And AI agents represent a step change in workflow transformation. For instance, one of BCG's clients, a shipbuilding company, used an autonomous, multiagent architecture with reasoning and planning capabilities to automate design tasks, which reduced the engineering resources required by 45% and lead time per ship deck by 80%. Another client, a global logistics company, used agents to automate its request-for-proposal response process, achieving 30% to 50% efficiency gains. BCG helped a large bank in Southeast Asia increase assets under management by 5% to 10% and increased customer conversions four- to sixfold, with agents that give relationship managers real-time input as they develop personalized offerings. And a leading industrial goods company increased its EBIT margins by 3 to 10 points with an agent that can run supply chain planning simulations, identify risks and their impact on operations, and propose mitigations. "It's an unbelievable multiplier," Kropp notes. "You're essentially turning every team member into a manager of AI collaborators. Teams are being reshaped from the ground up, as agents take on repetitive tasks and humans focus on oversight, creativity, and higher-order decisions. The reshape phase becomes a launchpad for radical innovation." Making the leap to innovation Few companies have made it to the "invent" stage, but that stage is what holds the most transformational potential: creating entirely new offerings, services, or business models powered by agentic AI and proprietary data. This is where companies can drive real differentiation. "The most mature organizations in our AI surveys are leaning into this invent phase," Kropp says. "They're using their unique strengths and agentic AI to outpace competitors in revenue and shareholder return." What separates companies that successfully move from reshaping and optimization to invention? According to Kropp, it starts with clarity of purpose -- a vision linked directly to company strategy. It also requires disciplined execution: setting targets, allocating investment, and tracking impact. "If your goal is to grow a new business line, you don't get there through random experimentation," he says. "You define what success looks like, and then invest intentionally in the AI capabilities and organizational changes to get there." Finding real competitive edge However, every organization is going to be building AI agents into many, if not most of their process, which means just having an agent doesn't mean automatic competitive advantage. What sets companies apart in the market is using proprietary data and human strengths in unique ways. Many organizations already have valuable data by virtue of their business: An airline with a loyalty program has in-depth data on those customers. A biopharma company doing drug discovery has vast proprietary data on their clinical trials and research. The key is recognizing where that data can drive innovation. "Companies need to focus on creating competitive advantage by identifying proprietary data that creates value, and where they have real human expertise, unique capabilities, unique culture and so on," Kropp says. "Then deploying AI agents to reshape an organization, its processes and people in a way that lets them fully realize the value of the advantages that they already have."

Don't wait for a 'bake-off': How Intuit and Amex beat competitors to production AI agents

Want smarter insights in your inbox? Sign up for our weekly newsletters to get only what matters to enterprise AI, data, and security leaders. Subscribe Now As generative AI matures, enterprises are shifting from experimentation to implementation -- moving beyond chatbots and copilots into the realm of intelligent, autonomous agents. In a conversation with VentureBeat's Matt Marshall, Ashok Srivastava, SVP and Chief Data Officer at Intuit, and Hillary Packer, EVP and CTO at American Express at VB Transform, detailed how their companies are embracing agentic AI to transform customer experiences, internal workflows and core business operations. From models to missions: the rise of intelligent agents At Intuit, agents aren't just about answering questions -- they're about executing tasks. In TurboTax, for instance, agents help customers complete their taxes 12% faster, with nearly half finishing in under an hour. These intelligent systems draw data from multiple streams -- including real-time and batch data -- via Intuit's internal bus and persistent services. Once processed, the agent analyzes the information to make a decision and take action. "This is the way we're thinking about agents in the financial domain," said Srivastava. "We're trying to make sure that as we build, they're robust, scalable and actually anchored in reality. The agentic experiences we're building are designed to get work done for the customer, with their permission. That's key to building trust." These capabilities are made possible by GenOS, Intuit's custom generative AI operating system. At its heart is GenRuntime, which Srivastava likens to a CPU: it receives the data, reasons over it, and determines an action that's then executed for the end user. The OS was designed to abstract away technical complexity, so developers don't need to reinvent risk safeguards or security layers every time they build an agent. Across Intuit's brands -- from TurboTax and QuickBooks to Mailchimp and Credit Karma -- GenOS helps create consistent, trusted experiences and ensure robustness, scalability and extensibility across use cases. Building the agentic stack at Amex: trust, control,and experimentation For Packer and her team at Amex, the move into agentic AI builds on more than 15 years of experience with traditional AI and a mature, battle-tested big data infrastructure. As GenAI capabilities accelerate, Amex is reshaping its strategy to focus on how intelligent agents can drive internal workflows and power the next generation of customer experiences. For example, the company is focused on developing internal agents that boost employee productivity, like the APR agent that reviews software pull requests and advises engineers on whether code is ready to merge. This project reflects Amex's broader approach: start with internal use cases, move quickly, and use early wins to refine the underlying infrastructure, tools, and governance standards. To support fast experimentation, strong security, and policy enforcement, Amex developed an "enablement layer" that allows for rapid development without sacrificing oversight. "And so now as we think about agentic, we've got a nice control plane to plug in these additional, additional guardrails that we really do need to have in place," said Packer. Within this system is Amex's concept of modular "brains" -- a framework in which agents are required to consult with specific "brains" before taking action. These brains serve as modular governance layers -- covering brand values, privacy, security, and legal compliance -- that every agent must engage with during decision-making. Each brain represents a domain-specific set of policies, such as brand voice, privacy rules, or legal constraints and functions as a consultable authority. By routing decisions through this system of constraints, agents remain accountable, aligned with enterprise standards and worthy of user trust. For instance, a dining reservation agent operating through Rezi, Amex's restaurant booking platform, must validate that it's selecting the right restaurant at the right time, matching the user's intent while adhering to brand and policy guidelines. Architecture that enables speed and safety Both AI leaders agreed that enabling rapid development at scale demands thoughtful architectural design. At Intuit, the creation of GenOS empowers hundreds of developers to build safely and consistently. The platform ensures each team can access shared infrastructure, common safeguards, and model flexibility without duplicating work. Amex took a similar approach with its enablement layer. Designed around a unified control plane, the layer lets teams rapidly develop AI-driven agents while enforcing centralized policies and guardrails. It ensures consistent implementation of risk and governance frameworks while encouraging speed. Developers can deploy experiments quickly, then evaluate and scale based on feedback and performance, all without compromising brand trust. Lessons in agentic AI adoption Both AI leaders stressed the need to move quickly, but with intent. "Don't wait for a bake-off," Packer advised. "It's better to pick a direction, get something into production, and iterate quickly, rather than delaying for the perfect solution that may be outdated by launch time." They also emphasized that measurement must be embedded from the very beginning. According to Srivastava, instrumentation isn't something to bolt on later -- it has to be an integral part of the stack. Tracking cost, latency, accuracy and user impact is essential for assessing value and maintaining accountability at scale. "You have to be able to measure it. That's where GenOS comes in -- there's a built-in capability that lets us instrument AI applications and track both the cost going in and the return coming out," said Srivastava. "I review this every quarter with our CFO. We go line by line through every AI use case across the company, assessing exactly how much we're spending and what value we're getting in return." Intelligent agents are the next enterprise platform shift Intuit and American Express are among the leading enterprises adopting agentic AI not just as a technology layer, but as a new operating model. Their approach focuses on building the agentic platform, establishing governance, measuring impact, and moving quickly. As enterprise expectations evolve from simple chatbot functionality to autonomous execution, organizations that treat agentic AI as a first-class discipline -- with control planes, observability, and modular governance -- will be best positioned to lead the agentic race. Editor's note: As a thank-you to our readers, we've opened up early bird registration for VB Transform 2026 -- just $200. This is where AI ambition meets operational reality, and you're going to want to be in the room. Reserve your spot now.

Brainpower unleashed: agentic AI and beyond bots

Agentic AI orchestrates intelligent automation, empowering future-proof digital transformation What truly separates us from machines? Free will, creativity and intelligence? But think about it. Our brains aren't singular, monolithic processors. The magic isn't in one "thinking part," but rather in countless specialized agents -- neurons -- that synchronize perfectly. Some neurons catalog facts, others process logic or govern emotion, still more retrieve memories, orchestrate movement, or interpret visual signals. Individually, they perform simple tasks, yet collectively, they produce the complexity we call human intelligence. Now, imagine replicating this orchestration digitally. Traditional AI was always narrow: specialized, isolated bots designed to automate mundane tasks. But the new frontier is Agentic AI -- systems built from specialized, autonomous agents that interact, reason and cooperate, mirroring the interplay within our brains. Large language models (LLMs) form the linguistic neurons, extracting meaning and context. Specialized task agents execute distinct functions like retrieving data, analyzing trends and even predicting outcomes. Emotion-like agents gauge user sentiment, while decision-making agents synthesize inputs and execute actions. The result is digital intelligence and agency. But do we need machines to mimic human intelligence and autonomy? Ask the hospital chief who's trying to fill a growing roster of vacant roles. The World Health Organization predicts a global shortfall of 10 million healthcare workers by 2030. Doctors and nurses pull 16-hour shifts like it's the norm. Claims processors grind through endless policy reviews, while lab technicians wade through a forest of paperwork before they can even test a single sample. In a well-orchestrated Agentic AI world, these professionals get some relief. Claim-processing bots can read policies, assess coverage and even detect anomalies in minutes -- tasks that would normally take hours of mind-numbing, error-prone work. Lab automation agents could receive patient data directly from electronic health records, run initial tests and auto-generate reports, freeing up technicians for the more delicate tasks that truly need human skill. The same dynamic plays out across industries. Take banking, where anti-money laundering (AML) and know-your-customer (KYC) processes remain the biggest administrative headaches. Corporate KYC demands endless verification steps, complex cross-checks, and reams of paperwork. An agentic system can orchestrate real-time data retrieval, conduct nuanced risk analysis and streamline compliance so that staff can focus on actual client relationships rather than wrestling with forms. Insurance claims, telecom contract reviews, logistics scheduling -- the list is endless. Each domain has repetitive tasks that bog down talented people. Yes, agentic AI is the flashlight in a dark basement: shining a bright light on hidden inefficiencies, letting specialized agents tackle the grunt work in parallel, and giving teams the bandwidth to focus on strategy, innovation and building deeper connections with customers. But the true power agentic AI lies in its ability to solve not just for efficiency or one department but to scale seamlessly across multiple functions -- even multiple geographies. This is an improvement of 100x scale. 1. Scalability: Agentic AI is modular at its core, allowing you to start small -- like a single FAQ chatbot -- then seamlessly expand. Need real-time order tracking or predictive analytics later? Add an agent without disrupting the rest. Each agent handles a specific slice of work, cutting development overhead and letting you deploy new capabilities without ripping apart your existing setup. 2. Anti-fragility: In a multi-agent system, one glitch won't topple everything. If a diagnostic agent in healthcare goes offline, other agents -- like patient records or scheduling -- keep working. Failures stay contained within their respective agents, ensuring continuous service. That means your entire platform won't crash because one piece needs a fix or an upgrade. 3. Adaptability: When regulations or consumer expectations shift, you can modify or replace individual agents -- like a compliance bot -- without forcing a system-wide overhaul. This piecemeal approach is akin to upgrading an app on your phone rather than reinstalling the entire operating system. The result? A future-proof framework that evolves alongside your business, eliminating massive downtimes or risky reboots. Generative AI was the breakout star a couple of years ago; agentic AI is grabbing the spotlight now. Tomorrow, something else will emerge -- because innovation never rests. How then, do we future-proof our architecture so each wave of new technology doesn't trigger an IT apocalypse? According to a recent Forrester study, 70% of leaders who invested over 100 million dollars in digital initiatives credit one strategy for success: a platform approach. Instead of ripping out and replacing old infrastructure each time a new AI paradigm hits, a platform integrates these emerging capabilities as specialized building blocks. When agentic AI arrives, you don't toss your entire stack -- you simply plug in the latest agent modules. This approach means fewer project overruns, quicker deployments, and more consistent outcomes. Even better, a robust platform offers end-to-end visibility into each agent's actions -- so you can optimize costs and keep a tighter grip on compute usage. Low-code/no-code interfaces also lower the entry barrier for business users to create and deploy agents, while prebuilt tool and agent libraries accelerate cross-functional workflows, whether in HR, marketing, or any other department. Platforms that support PolyAI architectures and a variety of orchestration frameworks allow you to swap different models, manage prompts and layer new capabilities without rewriting everything from scratch. Being cloud-agnostic, they also eliminate vendor lock-in, letting you tap the best AI services from any provider. In essence, a platform-based approach is your key to orchestrating multi-agent reasoning at scale -- without drowning in technical debt or losing agility. 1. Data: Plugged into a common layer Whether you're implementing LLMs or agentic frameworks, your platform's data layer remains the cornerstone. If it's unified, each new AI agent can tap into a curated knowledge base without messy retrofitting. 2. Models: Swappable brains A flexible platform lets you pick specialized models for each use case -- financial risk analysis, customer service, healthcare diagnoses -- then updates or replaces them without nuking everything else. 3. Agents: Modular workflows Agents thrive as independent yet orchestrated mini-services. If you need a new marketing agent or a compliance agent, you spin it up alongside existing ones, leaving the rest of the system stable. 4. Governance: Guardrails at scale When your governance structure is baked into the platform -- covering bias checks, audit trails, and regulatory compliance -- you remain proactive, not reactive, regardless of which AI "new kid on the block" you adopt next. A platform approach is your strategic hedge against technology's ceaseless evolution -- ensuring that no matter which AI trend takes center stage, you're ready to integrate, iterate, and innovate. Agentic AI isn't entirely new -- Tesla's self-driving cars employs multiple autonomous modules. The difference is that new orchestration frameworks make such multi-agent intelligence widely accessible. No longer confined to specialized hardware or industries, Agentic AI can now be applied to everything from finance to healthcare, fueling renewed mainstream interest and momentum. Design for platform-based readiness. Start with a single agent addressing a concrete pain point and expand iteratively. Treat data as a strategic asset, select your models methodically, and bake in transparent governance. That way, each new AI wave integrates seamlessly into your existing infrastructure -- boosting agility without constant overhauls. We list the best IT Automation software.

Cutting through the chaos: Why AI needs a unified platform

Rare diseases affect more than 30 million people in the U.S. -- 10% of the population -- yet many do not have treatments. At AstraZeneca, that gap creates urgent pressure, not only to discover life-saving therapies but to deliver them faster and more efficiently to patients. To meet that challenge, AstraZeneca turned to the ServiceNow AI Platform to accelerate onboarding, streamline lab requests, and help eliminate manual work -- freeing scientists and support teams to focus on work that matters most. "These aren't incremental gains," says Paul Fipps, president of global customer operations at ServiceNow. "We're helping AstraZeneca save more than 30,000 hours a year so they can focus on what matters most: discovering, developing, and delivering treatments that save lives." AstraZeneca offers a glimpse into a broader transformation -- one that could fundamentally change how the world works. In fact, IDC projects that investments in AI solutions and services will drive over $20 trillion of global impact by 2030. "Companies creating the right conditions for agentic AI -- autonomous AI agents -- are already seeing efficiency gains of 20-50%," Fipps says. These results are tangible: faster customer service resolution, reduced administrative overhead, and optimized supply chains. When executed correctly, the competitive advantage is unprecedented. "Integrating AI into business isn't just about cutting costs -- it's about rethinking priorities," he adds. "Think about everything we can achieve with the extra time, resources, and brainpower. It's an opportunity to redeploy our efforts toward solving problems that really matter." Realizing AI's value through a platform-based approach Successfully integrating AI requires overcoming three core challenges: data quality, legacy infrastructure, and integration complexity. According to McKinsey, 70% of organizations struggle to quickly integrate data into AI models. Simply layering AI agents over these fragmented systems will only exacerbate complexity and limit potential. "Work doesn't happen in a single department, so neither can your AI strategy," says Fipps. "Agentic AI needs a platform-wide strategy -- one that spans every corner of the business, not just a single domain." That's the approach ServiceNow is taking with Vodafone, one of the world's largest telecom providers with more than 340 million customers. Together, they're redefining service delivery for business clients. "We've built an AI-powered Enhanced Service Monitoring solution that proactively identifies and resolves issues -- often before the customer is even aware there's a problem," Fipps says. "It's about reducing disruptions, accelerating response times, and delivering seamless experiences across complex networks and cloud environments. This isn't just an upgrade -- it's setting a new benchmark for how AI can transform customer experience at scale." Going beyond superficial AI Many so-called agentic AI tools are little more than basic robotic process automation (RPA). True transformation runs deeper. It requires AI that's fully integrated across the enterprise -- connecting across the enterprise -- linking customer data, knowledge bases, and operational systems to enable intelligent, fast decision-making. That's where platform architecture becomes critical. ServiceNow's 20 years as the workflow automation leader is an incredible advantage: a powerful foundation of built-in workflows, automations, and knowledge bases. This legacy offers a head start -- and a clear distinction between superficial AI and enterprise-wide transformation. "Effective AI deployment isn't a one-time 'ta-da' moment," says Fipps. "It's about improving thousands of processes and tasks. When those improvements occur on a unified platform, they build on each other and can create exponential efficiency gains." These gains are already real at ServiceNow. "We're running more than 100 different AI projects internally," Fipps shares. "So far, we estimate we've saved more than $350 million through automation and process optimization." For instance, if one of ServiceNow's 26,000+ employees have a finance-related question -- say, their paycheck or their commission statement -- they no longer wait four days for a response. With agentic AI, it now takes seconds. Scaling AI by removing barriers True transformation goes beyond efficiency -- it's about scale. AI Agents on the ServiceNow platform learn from trillions of transactions and billions of workflows the company sees on its platform, identifying repeatable patterns and turning them into pre-built agents that help solve high-impact problems across industries. "We're continuously refining our AI agent strategy to tackle our customers' most pressing problems," Fipps says. This is where the ServiceNow AI Platform sets a new standard. The AI Control Tower serves as the single command center that orchestrates all AI agents, models, and workflows -- providing full visibility, enterprise-grade compliance, and control at scale. It aligns AI initiatives with broader business and technology goals to ensure meaningful value delivery. With ServiceNow AI Agent Fabric, AI agents from the likes of ServiceNow, Microsoft, Deloitte or in-house teams can communicate and collaborate seamlessly across systems. These agents act as a unified, intelligent system: sharing context, coordinating tasks, and delivering measurable outcomes. The time for AI is now The urgency of agentic AI means the time to act is now. "To stay competitive, the smartest move is to make a platform bet," Fipps says. "It's not just about saving money. The most forward-looking companies are reinvesting those gains to accelerate what matters -- like AstraZeneca speeding up drug discovery, or Vodafone enhancing customer service." In each case, AI isn't replacing people -- it's enhancing their ability to solve complex challenges faster. While the platform matters, the true power of AI lies in the people it empowers. "The organizations making the most progress are those using AI to elevate human potential," he says. "They're not just automating tasks -- they're driving human-centered innovation and transforming how work gets done."

The great AI agent acceleration: Why enterprise adoption is happening faster than anyone predicted

Want smarter insights in your inbox? Sign up for our weekly newsletters to get only what matters to enterprise AI, data, and security leaders. Subscribe Now The chatter around artificial general intelligence (AGI) may dominate headlines coming from Silicon Valley companies like OpenAI, Meta and xAI, but for enterprise leaders on the ground, the focus is squarely on practical applications and measurable results. At VentureBeat's recent Transform 2025 event in San Francisco, a clear picture emerged: the era of real, deployed agentic AI is here, is accelerating and it's already reshaping how businesses operate. Companies like Intuit, Capital One, LinkedIn, Stanford University and Highmark Health are quietly putting AI agents into production, tackling concrete problems, and seeing tangible returns. Here are the four biggest takeaways from the event for technical decision-makers. 1. AI Agents are moving into production, faster than anyone realized Enterprises are now deploying AI agents in customer-facing applications, and the trend is accelerating at a breakneck pace. A recent VentureBeat survey of 2,000 industry professionals conducted just before VB Transform revealed that 68% of enterprise companies (with 1,000+ employees) had already adopted agentic AI - a figure that seemed high at the time. (In fact, I worried it was too high to be credible, so when I announced the survey results on the event stage, I cautioned that the high adoption may be a reflection of VentureBeat's specific readership.) However, new data validates this rapid shift. A KPMG survey released on June 26, a day after our event, shows that 33% of organizations are now deploying AI agents, a surprising threefold increase from just 11% in the previous two quarters. This market shift validates the trend VentureBeat first identified just weeks ago in its pre-Transform survey. This acceleration is being fueled by tangible results. Ashan Willy, CEO of New Relic, noted a staggering 30% quarter over quarter growth in monitoring AI applications by its customers, mainly because of the its customers' move to adopt agents. Companies are deploying AI agents to help customers automate workflows they need help with. Intuit, for instance, has deployed invoice generation and reminder agents in its QuickBooks software. The result? Businesses using the feature are getting paid five days faster and are 10% more likely to be paid in full. Even non-developers are feeling the shift. Scott White, the product lead of Anthropic's Claude AI product, described how he, despite not being a professional programmer, is now building production-ready software features himself. "This wasn't possible six months ago," he explained, highlighting the power of tools like Claude Code. Similarly, OpenAI's head of product for its API platform, Olivier Godement, detailed how customers like Stripe and Box are using its Agents SDK to build out multi-agent systems. 2. The hyperscaler race has no clear winner as multi-cloud, multi-model reigns The days of betting on a single large language model (LLM) provider are over. A consistent theme throughout Transform 2025 was the move towards a multi-model and multi-cloud strategy. Enterprises want the flexibility to choose the best tool for the job, whether it's a powerful proprietary model or a fine-tuned open-source alternative. As Armand Ruiz, VP of AI Platform at IBM explained, the company's development of a model gateway -- which routes applications to use whatever LLM is most efficient and performant for the specific case -was a direct response to customer demand. IBM started by offering enterprise customers its own open-source models, then added open-source support, and finally realized it needed to support all models. This desire for flexibility was echoed by XD Huang, the CTO of Zoom, who described his company's three-tiered model approach: supporting proprietary models, offering their own fine-tuned model and allowing customers to create their own fine-tuned versions. This trend is creating a powerful but constrained ecosystem, where GPUs and the power needed to generate tokens are in limited supply. As Dylan Patel of SemiAnalysis and fellow panelists Jonathan Ross of Groq and Sean Liu of Cerebras pointed out, this puts pressure on the profitability of a lot of companies that simply buy more tokens when they are available, instead of locking into profits as the cost of those tokens continues to fall. Enterprises are getting smarter about how they use different models for different tasks to optimize for both cost and performance -- and that may often mean not just relying on Nvidia chips, but being much more customized -- something also echoed in a VB Transform session led by Solidigm around the emergence of customized memory and storage solutions for AI. 3. Enterprises are focused on solving real problems, not chasing AGI While tech leaders like Elon Musk, Mark Zuckerberg and Sam Altman are talking about the dawn of superintelligence, enterprise practitioners are rolling up their sleeves and solving immediate business challenges. The conversations at Transform were refreshingly grounded in reality. Take Highmark Health, the nation's third-largest integrated health insurance and provider company. Its Chief Data Officer Richard Clarke said it is using LLMs for practical applications like multilingual communication to better serve their diverse customer base, and streamlining medical claims. In other words, leveraging technology to deliver better services today. Similarly, Capital One is building teams of agents that mirror the functions of the company, with specific agents for tasks like risk evaluation and auditing, including helping their car dealership clients connect customers with the right loans. The travel industry is also seeing a pragmatic shift. CTOs from Expedia and Kayak discussed how they are adapting to new search paradigms enabled by LLMs. Users can now search for a hotel with an "infinity pool" on ChatGPT, and travel platforms need to incorporate that level of natural language discovery to stay competitive. The focus is on the customer, not the technology for its own sake. 4. The future of AI teams is small, nimble, and empowered The age of AI agents is also transforming how teams are structured. The consensus is that small, agile "squads" of three to four engineers are most effective. Varun Mohan, CEO of Windsurf, a fast-growing agentic IDE, kicked off the event by arguing that this small team structure allows for rapid testing of product hypotheses and avoids the slowdown that plagues larger groups. This shift means that "everyone is a builder," and increasingly, "everyone is a manager" of AI agents. As GitHub and Atlassian noted, engineers are now learning to manage fleets of agents. The skills required are evolving, with a greater emphasis on clear communication and strategic thinking to guide these autonomous systems. This nimbleness is supported by a growing acceptance of sandboxed development. Andrew Ng, a leading voice in AI, advised attendees to leave safety, governance, and observability to the end of the development cycle. While this might seem counterintuitive for large enterprises, the idea is to foster rapid innovation within a controlled environment to prove value quickly. This sentiment was reflected in our survey, which found that 10% of organizations adopting AI have no dedicated AI safety team, suggesting a willingness to prioritize speed in these early stages. Together, these takeaways paint a clear picture of an enterprise AI landscape that is maturing rapidly, moving from broad experimentation to focused, value-driven execution. The conversations at Transform 2025 showed that companies are deploying AI agents today, even if they've had to learn tough lessons on the way. Many have already gone through one or two big pivots since first trying out generative AI one or two years ago -- so it's good to get started early. For a more conversational dive into these themes and further analysis from the event, you can listen to the full discussion I had with independent AI developer Sam Witteveen on our recent podcast below. We've also just uploaded the main-stage talks at VB Transform here. And our full coverage of articles from the event is here. Listen to the VB Transform takeaways podcast with Matt Marshall and Sam Witteveen here:

The Impact of Agentic AI on 'Human + AI' Collaboration | AIM

From task execution to autonomous decision-making, AI's evolution has given rise to a new class of systems known as Agentic AI. Gartner recognises it as the most prominent technological trend of 2025, with predictions stating that 33% of enterprise software will incorporate Agentic AI by 2028, autonomously influencing 15% of daily work decisions. By definition, AI agents handle repetitive, data-heavy tasks, while human beings are allowed to focus on creative and strategic endeavours. However, AI tools become truly agentic when they go beyond mere automation and feature distinct characteristics that fundamentally change how work is performed. In this aspect, an AI agent mimics human behaviours such as reasoning, decision-making, learning, and communication. Its integration has significant potential across industries like healthcare, insurance, finance, and manufacturing. By blending human and AI efforts, these agents open new avenues for collaboration and problem-solving. AI agents use several tools to perform complex tasks autonomously and adaptively. They use sophisticated decision-making mechanisms, such as multi-tiered decision trees and strategy algorithms, to evaluate actions and choose optimal paths. AI agents operate autonomously, making informed decisions through prioritisation frameworks and reasoning engines. Their adaptability allows them to adjust to new information or shifting circumstances, ensuring resilience in dynamic environments. Designed to achieve specific goals, agents decompose tasks into manageable components and maintain context awareness across multi-step processes. They also have built-in safety mechanisms, such as value alignment and permission frameworks, to ensure their actions stay within acceptable boundaries. Lastly, these agents continuously improve through feedback loops, learning from outcomes and refining their processes over time. Integrating AI agents into business processes can greatly improve efficiency, adaptability, and innovation across various departments. In IT, agents can dynamically resolve issues, learn from past incidents, and personalise system access. For the human resources teams, agents can automate tasks like recruitment, onboarding, and employee support, allowing them to focus on strategic initiatives. In finance, AI agents enable rapid analysis of financial data, generate reports, and ensure process compliance. When it comes to security, it can monitor networks, detect threats, and trigger automated protective actions. In engineering, agents optimise processes and automate tasks, freeing up engineers to work on innovative projects. Lastly, in customer service, AI agents offer 24/7 personalised assistance, reducing response times and boosting customer satisfaction. Capitalising on this rapid evolution of AI, ValueLabs, an Agentic AI services and solutions company, has embraced the 'Human + AI' paradigm, driving business process maturity. Its Agentic AI-driven Enterprise OS platform, AiDE®, streamlines digital engineering integration across the Software Development Life Cycle (SDLC) and beyond. The AiDE® platform takes a unique approach to agent integration by ensuring structured, context-aware systems where agents plan, reason, and verify the outcomes before acting. They work in tandem to solve problems while ensuring that ValueLabs stays true to its outcome-based model of offering solutions. Unlike traditional models, AiDE® combines AI-driven execution with human-like planning, resulting in better accuracy, fewer errors, and more reliable automation. Its key features include structured execution through the Plan-Act model, long-term memory with the Memento system for adaptive decision-making, and the ability to automate web interfaces via the Web Agent. The platform's hybrid approach sets it apart in the Agentic AI space. The rise of agents signals a shift towards 'autonomous everything', where AI processes collaborate, creating compounded capabilities that elevate work beyond simple automation. This interconnected autonomy allows enterprises to embrace AI agents and transform traditional roles into more efficient, collaborative systems. The integration of AI agents signals a shift in how responsibilities are shared between humans and machines, as traditional boundaries continue to blur and evolve. It encourages enterprises that were previously hesitant about automation to adopt AI agents. The power of this revolution lies in the network effect, where autonomous processes can collaborate, creating compound capabilities greater than the sum of their parts. In conclusion, integrating AI agents with human capabilities marks a significant step towards true autonomy in business operations. By leveraging AI-driven execution, organisations can achieve speed and efficiency without barriers, ensuring streamlined processes. The creation of self-sufficient teams eliminates single points of failure, providing resilience and reliability in complex systems. Furthermore, AI agents go beyond simple automation, fostering autonomous business ecosystems that continuously adapt and evolve. This transformation requires a cultural shift in viewing AI not merely as a tool but as a force multiplier that amplifies human potential.

AI Agents: Strengths, Weaknesses, and Business Implementation

Enter your email to get Benzinga's ultimate morning update: The PreMarket Activity Newsletter An AI agent is software that can take actions independently. Unlike chatbots that just respond to questions, AI agents (virtual assistants) can access databases, run calculations, use tools, make decisions, and execute tasks without constant human supervision. How they work: They combine foundation models (like what powers ChatGPT, Claude, or Copilot) with access to specific tools and knowledge bases. They follow instructions through a decision-making loop: observe the situation, plan what to do, execute actions, evaluate the outcome, and repeat. The complex technical framework involves prompt engineering, retrieval systems, memory management, and tool integration. Companies start with a general-purpose framework and then customize it for specific domains and tasks. The level of customization varies dramatically. Some just connect GPT-4 to APIs and call it done. Others build complex systems with specialized knowledge and extensive guardrails. Where AI Agents Fit in the Tech Landscape AI agents sit between simple bots and human decision-makers. Bots are rule-based, rigid systems that follow commands without adapting. They are fast but fragile, breaking when conditions change. Humans, on the other hand, are highly adaptable. We handle complexity, nuance, and emotion, but we are limited by time, energy, and focus. AI agents bridge the gap. They are more capable than bots - able to learn, adjust, and handle moderately complex tasks. But they are not human. They still struggle with context, ambiguity, and unpredictable situations. In business, think of AI agents as decision virtual assistants. They analyze data, identify patterns, and suggest actions. They are great for repetitive, structured tasks, freeing up people to focus on what requires creativity, empathy, or strategic thinking. However, they are still not fully autonomous. When the environment shifts or the stakes are high, human oversight is essential. You set the rules and boundaries; the agent works within them. Planning vs. Reality In theory, these systems work like this: They start by analyzing the main objective and breaking it down into smaller tasks. For example, a sales AI virtual assistant might begin by identifying past customer interactions as the first step. It then chooses the right tools and methods for each task, such as running database queries, using analytics frameworks, or calling external APIs. Based on the insights gathered, the system takes targeted actions to move toward its goal. In reality, though, this process comes with limitations: 1) AI virtual assistants struggle with vague goals. Unlike humans, they cannot read between the lines or infer intent when objectives are not clearly defined, which is often the case in real-world business scenarios. 2) They miss the subtle business context. While humans intuitively understand that a 30% profit margin is healthy in one industry but problematic in another, AI agents lack that kind of contextual awareness. 3) An AI assistant is only as effective as the tools and data it can reach. If it needs customer sentiment data but cannot access the necessary customer records, it is essentially stuck. 4) AI agents follow decision trees, not judgment. They do not understand consequences or recognize when circumstances have fundamentally changed, especially in the security sphere, like a zero-day attack. The most effective deployments today are narrow in scope and have well-defined success metrics like document processing, initial customer service triage, or data extraction. The broader the objective, the more human oversight is needed. Bridging the Gap Between Efficiency and Failure Let's imagine two retail companies -- Acme Analytics and Precision Insights -- both decided to implement AI agents to improve their operations. But they took entirely different approaches. The key difference was not the AI itself; both used similar models. Precision Insights understood that AI is a tool, not a replacement for human judgment. They built around AI's strengths in pattern recognition while accounting for its limitations in business context and nuance. Results After Six Months Acme Analytics: The AI ignored key seasonal trends, leading to major inventory mistakes. Trust in the system collapsed, and employees stopped using it. The rollout was costly, with additional significant losses in revenue. In addition, several senior staff members resigned over the chaos. Precision Insights: Their focused AI reduced stockouts by several percentage points and improved profit margins. It flagged anomalies for human review instead of acting blindly. The entire investment paid off within just a couple of months. Employees welcomed the support, as the AI handled routine tasks while humans made complex decisions. The Myth of Unlimited AI Agents: Why Focused Deployment Wins A narrow focus is essential for AI agents. This is not a weakness; these systems only deliver value when applied to well-defined, structured processes and accurately reflect how real businesses operate. Each process involves plenty of use cases and context-specific decisions. What works for inventory forecasting in retail will not apply to capacity planning in manufacturing without significant changes -- different data, goals, and logic. The first 3-5 AI virtual assistant implementations typically target the obvious, high-value, well-structured processes where the payoff is clear. After those are covered, each additional implementation faces higher complexity and lower returns. Integrating additional AI agents is not plug-and-play; each agent requires custom connections, rules, and data, adding technical debt. Human oversight must also scale with each agent's complexity and risk. As a relatively new technology for most companies, it is wise to manage only a handful at first and evaluate the results. The promise of "AI agents for everything" is just another myth. Real gains come from deploying several agents where decision logic is clear and structured, allowing humans to handle everything else. Implementing AI Solutions Most agentic AI systems are built on the same foundation: large language models connected to APIs. The difference between them often comes down to budget, scope, and how well they are implemented. Custom Agents AI agents follow learned rules and patterns based on company workflows. Setting them up takes time. They require detailed mapping of tasks and decision points. They handle routine decisions well, but humans step in when things get complex. System Integration AI agents connect with existing software systems through APIs or plugins. Their success depends on clean data, compatible systems, and stable infrastructure. Multi-Agent Systems Multiple agents can work together like an assembly line. Each one handles a specific task and passes it along. They work best when tasks are clearly defined, though troubleshooting issues can be challenging. AI Virtual Assistant Trends to Watch Here are the top AI trends shaping the future of work: Agents will start handling entire tasks with less guidance, but high-stakes decisions will still need human oversight. They will not just wait for commands; they will detect issues, suggest actions, and sometimes act on their own. Be prepared to manage their choices. Businesses will shift from general AI to agents trained for their specific, complex, and regulated workflows. Teams of agents will collaborate on complex tasks, speeding up multi-step processes. Agents will improve at understanding human intent and tone but will still struggle with emotional nuance. Expect deeper integration with tools like CRMs, ERPs, and IoT -- siloed agents will not cut it. 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