The Rise of AI Agents: Transforming Business Productivity and Workflow Automation

AI Agents Can Help Businesses Be '10 Times More Productive,' According to an Nvidia VP. Here's What They Are and How Much They Cost.

AI agents have a variety of practical use cases, from writing code to helping with everyday paperwork. AI agents, advanced software programs that act autonomously to perform specific tasks without human assistance, have become the latest buzzword in tech and business. Nvidia's Vice President of AI Software, Kari Briski, told Entrepreneur that AI agents will change how we think about our daily tasks, from writing code and research papers to managing calendars and payroll. "I think agents will transform the way we work," Briski said. According to a Franklin Templeton 2025 technology outlook paper, AI agents have a variety of practical use cases for everyday life, from acting as tutors to planning meals to booking travel. In business, they can automatically ask and respond to customer questions by looking up answers in internal documents. Meanwhile, companies from Salesforce to Nvidia have introduced their own AI agents with the intention of helping workers and customers make their lives easier. But how does the technology actually help businesses? Related: Nvidia CEO Jensen Huang Says the New ChatGPT Needs '100 Times More' of His Company's AI Chips Briski said that entrepreneurs and founders who want to start their own companies could tap into AI agents to write code and build business plans. Agents can even act as a "sparring partner" to brainstorm ideas or think through a situation. "If you've ever noticed that you're a little bit more productive when you can spar off somebody, agents can provide that to help you be more productive," Briski said. "Instead of having to go seek out an expert who might not have time, [it's] able to iterate now." Briski also noted that AI writing code has come a long way. In the past, the tech was focused on code completion, where AI finishes a line of code as programmers type it out. Now, AI agents write full programs on their own, making software developers "10 times more productive," she says. This frees up time for the developer to focus on their system architecture, product, and end-user experience. Related: How Nvidia CEO Jensen Huang Transformed a Graphics Card Company Into an AI Giant: 'One of the Most Remarkable Business Pivots in History' The technology can also help employees research markets faster by surfing the web, finding sources, and writing thorough research papers based on a prompt -- complete with citations to the web sources they consulted to craft the paper. Competitor OpenAI, for example, demonstrated its ChatGPT deep research agent earlier this year, which can create detailed reports with nearly 30 sources in about 10 minutes. OpenAI relies on Nvidia chips to power its AI products, though it is working on developing its own AI chips by the end of the year. Briski noted that over the past two years, Nvidia's technology has saved ServiceNow, a cloud-based platform that helps businesses create and automate workflows, over 400,000 hours of human labor. "AI agents offer complex decision-making capabilities and the ability to interact with and influence the environment," Briski said. Nvidia did not disclose how much its AI agents cost, but IT support company Transputec estimates that the technology can cost anywhere from $0.99 per conversation for simpler tasks to $30 per hour for more complex ones. Salesforce AI agents are priced at $2 per conversation and can be used for tasks like customer service and employee support. OpenAI is reportedly preparing PhD-level AI agents priced between $2,000 and $20,000 monthly. The company's ChatGPT Pro paid plan with deep research capabilities costs $200 per month. "AI agents that are available as a service start off very expensive because they've got very big infrastructures and services," Briski said. Still, the high price tag hasn't stopped companies from using AI agents. Accenture, Datastax, and Oracle all use Nvidia's AI to build agents for their companies.

The secret to success with AI agents: Bridging front-end intelligence and back-end data mastery

AI copilots have shifted the way businesses and individuals work by assisting with decisions, automating tasks, and enhancing productivity. However, while copilots remain indispensable, their carefully prescribed structure limits their effectiveness. Now, the industry is already looking toward the next transformation: AI agents. AI agents are autonomous systems designed to take full control of tasks and shift how companies address customer interactions and business operations. Agents have much broader abilities to achieve outcomes by providing instant, scalable customer support to handle inquiries without human intervention, and streamlining operations by automating repetitive and decision-intensive tasks. Understanding how agentic AI can benefit your organization and creating a strategy to harmonize data are the keys to taking advantage of this powerful technology. Here are three practical strategies to help you build toward success:

Agentic AI in Action: Transforming Industries with Real-Time Workflow Automation

By Sushant Shetty The modern industrial landscape is no longer just powered by human ingenuity -- it's driven by artificial intelligence. Over the past decade, machine learning and AI have evolved from experimental tools to indispensable assets. Industries like manufacturing, retail, and healthcare now rely on these technologies to predict equipment failures, personalize customer experiences, and identify at-risk patients. But while traditional AI excels at analyzing data and generating insights, it often stops short of taking action. For instance, a predictive maintenance system might flag a faulty machine, but scheduling repairs still requires human intervention. This gap between insight and execution has lingered -- until the rise of Agentic AI, a groundbreaking evolution that doesn't just analyze or recommend but acts autonomously in real time. Generative AI laid the groundwork for this shift. Tools like ChatGPT and DALL-E showcased AI's ability to create content, from marketing copy to 3D prototypes, accelerating workflows across sectors. Marketing teams use GenAI to draft campaigns in minutes, while pharmaceutical companies simulate drug interactions to shorten R&D cycles. Yet GenAI's role remains collaborative -- it generates outputs, but humans must refine and implement them. This is where Agentic AI diverges. Imagine a system that not only designs a product prototype but also adjusts the blueprint based on real-time feedback from engineers, orders materials autonomously, and schedules production -- all without human input. This level of end-to-end automation is redefining what's possible. Take supply chain management as an example. Companies like Amazon are deploying Agentic AI to tackle delays dynamically. When a storm disrupts a delivery route, the system doesn't just alert managers -- it reroutes shipments, adjusts inventory orders across warehouses, and updates delivery estimates for customers, all within seconds. Similarly, in healthcare, Agentic AI is transforming patient care. Hospitals using AI-powered systems can monitor patients through wearable devices. If a patient's vital signs deteriorate, the AI doesn't just notify a nurse -- it adjusts medication dosages via connected dispensers, schedules urgent tests, and updates electronic health records, ensuring timely interventions. These systems operate within predefined safety protocols but eliminate the lag of human decision-making, saving critical time and resources. The manufacturing sector offers another compelling case. Siemens, for instance, uses Agentic AI to maintain seamless factory operations. When a robotic arm malfunctions on an assembly line, the AI instantly redistributes tasks to other machines, orders replacement parts from suppliers, and reschedules production timelines -- minimizing downtime. In finance, fraud detection systems powered by Agentic AI go beyond flagging suspicious transactions. They freeze compromised accounts, initiate investigations, and notify customers proactively, preventing losses before they escalate. These examples underscore Agentic AI's core strength: closing the loop between analysis and action. By integrating with IoT devices, enterprise software, and real-time data streams, it enables industries to respond to challenges with unprecedented speed and precision. The implications are profound. Agentic AI isn't just automating tasks -- it's reimagining workflows. Retailers, for example, now deploy AI that personalizes shopping experiences in real time. As a customer browses an online store, the system analyzes behavior, adjusts product recommendations, offers dynamic discounts, and even predicts cart abandonment, all while optimizing inventory levels behind the scenes. This level of autonomy allows businesses to scale operations without proportional increases in human labor. Challenges remain, of course. Ethical oversight, data privacy, and workforce adaptation require careful navigation. But the benefits -- reduced operational costs, fewer errors, and faster decision cycles -- are driving rapid adoption. As industries embrace Agentic AI, they're not just streamlining processes -- they're pioneering a new era of autonomy. From supply chains that self-correct to healthcare systems that act before emergencies unfold, the technology is proving its transformative potential. The future belongs to businesses that leverage AI not just as a tool but as an active, decision-making partner. In this agentic world, the boundary between human and machine collaboration is blurring, unlocking possibilities that were once confined to science fiction. The question is no longer if industries will adopt this technology, but how swiftly they can adapt to thrive in an era where AI doesn't just assist -- it acts. (The author is Sushant Shetty, GM Data Science at Findability Sciences, and the views expressed in this article are his own)

How to integrate AI agents into your business and drive speed to value

Business leaders looking to harness AI agents need to think in terms of a process change and a mindset change. At Davos this year, the hallways were abuzz with talk of agentic AI: small intelligent systems that take actions and automate tasks. The pace of innovation in the world of AI can be overwhelming for business leaders and policy-makers, and many are unsure how to act on the opportunity. While these concepts may seem complex, business leaders can use a simple framework to navigate and unlock the value of agents. Much of the conversation so far has focused on agents driving productivity, but the real impact goes beyond that. Agents will make work easier, faster and more effective - whether by handling tedious manual tasks, speeding up handovers between teams, or accelerating market delivery. These improvements drive speed to value - this is how we should measure the outcome of our investment in agentic systems. It's not about efficiency for its own sake; it's about how quickly value is delivered. To get the most from agents, they should be integrated across the entire value chain. Here's a straightforward framework to help you cut through the complexity and unlock value for your business. The Discover, Decide, Deliver framework is a useful model to frame how business leaders should think about their investment and approach to adopting AI and agentic frameworks. Let's explore the process of bringing a revolutionary medicine to market with agents using Discover, Decide, Delive. Traditional drug development is a long and costly journey, often taking several years. Agentic AI can automate repetitive manual steps and create more seamless transitions between stages, accelerating time to market. The process begins with Discovery. This is where initial research takes place to understand the disease, decide which aspect of disease biology to target, and then develop a compound that will alter that aspect of the disease. It also includes data analysis to test molecules and compounds. Next is the Decision phase, where researchers and clinicians land on how and where the drug will be used, from speeding up new clinical trial protocols to analyzing massive amounts of complex data from these trials. Then there's the Delivery phase, which involves producing the drug, educating patients and doctors, and bringing it to market. While discovery is happening faster than ever, manual processes between and within stages can cause delays, errors and miscommunications, slowing down overall progress. In this case, speed to value isn't just a metric - it's the difference between getting a drug to market faster or losing market share. For instance, one of the world's largest pharmaceutical manufacturers, Novartis, applied Generative AI in its drug development process even before agents came along. Imagine what they could do now. Ideally, agents can cut timelines from decades to years and years to months, enabling faster decisions, delivery and learning. Here, agents don't just help you do more with less - they accelerate value by getting you to the right outcomes faster. This concept applies across industries. For example, Levi Strauss & Co built a powerful but complex traditional AI system to detect fashion trends. Agents could optimize this process by analyzing online images daily, tracking the shift from fitted to baggy jeans across demographics and delivering insights faster. The Coca-Cola Company is taking it a step further by using AI to enhance its relationships with retailers. Imagine an agent that gives recommendations on how to arrange products in coolers based on current consumer preferences. In these cases, speed to value is achieved by 1) accelerating the transition from analysis to action and 2) reducing the delay between insights and action across companies. In a recent article, we explored the evolving role of leadership in the age of AI. The rise of agents requires a similar mindset shift. Below, we apply the Discover, Decide, Deliver framework to help leaders ask the right questions in an agent-driven world. The key to leveraging agents in discovery is understanding your objective and using the right data to fuel faster insights and analysis from more places. Agents can quickly pull data from multimodal sources - text, audio, visual - and spot patterns that reveal opportunities. Using reasoning capabilities and accelerated computing, they help connect the dots faster, turning data into answers. Leaders need to ask: Across industries, leading companies like Netflix have built their success on high-velocity decision-making. They thrive because they minimize the iteration cycle and execute quickly to deliver innovation. Agents that can reason allow any organization to streamline decision-making, reduce the cost of experimentation and take action faster. With agents handling manual, repetitive, error-prone tasks, leaders can focus on more strategic decisions sooner, accelerating the innovation cycle. Leaders need to ask: Today, the speed of delivery depends on how well you collaborate with customers, vendors and partners. Agents enable seamless collaboration across organizations, speeding up delivery by ensuring that processes are in sync between teams, suppliers and customers (just like APIs did for digital transformation). Leaders need to ask: Agentic AI is changing how businesses drive outcomes by accelerating speed to value. The companies that will dominate the next decade won't just use AI - they'll fully integrate agent-driven actions across their business and partnerships. To capture the benefits of speed to value, this will need to be both a process change and a mindset change across leadership and throughout the organization. How to get started: Success comes down to implementing a scalable and repeatable framework that works within your existing processes. Discover, Decide, Deliver, Learn is one example that will help you break through the complexity and unlock the promise of agents.

The next frontier in AI: Intelligent agentic AI systems that think and act

Artificial intelligence has evolved far beyond a mere buzzword. Today, it is at the heart of a technological revolution that is reshaping core business processes -- from sales and customer service to healthcare and retail. Companies like FlashIntel are pioneering what they call "Agentic AI," a form of technology designed not just to automate tasks but to adapt dynamically and operate with a degree of decision-making that mimics human reasoning. Rethinking automation: from predefined rules to adaptive intelligence Traditional automation solutions have long focused on executing predefined tasks. These systems excel at repetitive work but often lack the ability to adjust to new circumstances or interpret context in real time. In contrast, Agentic AI aims to overcome these limitations by incorporating adaptive learning and autonomous decision-making into everyday workflows. For instance, FlashIntel's AI Parallel Dialer is engineered to do more than just schedule calls. It is intended to analyze real-time data, prioritize leads, and adjust outreach strategies on the fly. Similarly, autonomous customer experience agents are designed to engage with clients immediately, route more complex issues to human operators, and continuously learn from each interaction. Addressing industry challenges with intelligent solutions In today's fast-paced business environment, inefficiencies in sales and customer service can significantly hinder growth. Sales teams often contend with fragmented tools, lengthy manual processes, and low connection rates, while customer service centers struggle with delayed responses and generic interactions. Agentic AI presents a potential solution to these challenges by providing systems that: Expanding applications across multiple sectors The potential of Agentic AI extends well beyond the realms of sales and customer support. In healthcare, adaptive systems could monitor patient conditions in real time and alert clinicians to emergent issues. In the retail industry, they might manage inventory more effectively, personalize shopping experiences, and predict trends based on customer behavior. Financial services, manufacturing, and even logistics are beginning to explore how such intelligent systems can address industry-specific pain points. Moreover, as businesses adopt these technologies, the focus shifts from simply automating tasks to creating collaborative environments where human insight and machine efficiency complement each other. This approach not only optimizes workflows but also empowers employees to engage in higher-value, strategic activities. The road to advanced AI: bridging today's solutions with tomorrow's possibilities While Agentic AI is already making inroads in transforming operational processes, its evolution is viewed as a stepping stone toward broader advancements -- namely, the development of Artificial General Intelligence (AGI). AGI represents a future where machines could perform any intellectual task that a human can, but experts caution that such a leap requires careful, incremental progress. Key areas of focus include: Leadership and vision: insights from FlashIntel and Yi Shi At the helm of FlashIntel is Yi Shi, a serial entrepreneur known for founding multiple ventures such as Avazu, DotC United, and oBike. His experience in navigating complex technological challenges informs his approach to developing Agentic AI solutions that seek to redefine business workflows. Under his leadership, FlashIntel has positioned itself as an innovator in the AI space by integrating advanced data analytics with adaptive algorithms. Implications for the future of work and beyond The shift toward intelligent, adaptive systems raises critical questions about the future workforce. While some fear that automation might lead to job displacement, proponents argue that technologies like Agentic AI will ultimately free employees from monotonous tasks, allowing them to focus on creative, strategic, and relationship-based work. As organizations navigate this transition, a collaborative human-AI model may offer the best of both worlds -- where technology enhances productivity without eroding the value of human expertise. Challenges, limitations, and the need for independent verification As promising as these developments are, several challenges remain: Financial Disclaimer: The performance figures and claims mentioned in this article are based on internal data and material provided by FlashIntel and related sources. Readers are advised to conduct their own research.

From Microsoft to Google and OpenAI: These 5 AI agents are pushing boundaries of AI

Artificial intelligence has been making waves in nearly every sector, from customer service to high-stakes financial modelling. But one of the most transformative advancements in recent years has been the rise of AI agents - specialised, autonomous systems designed to handle complex tasks that once required human intervention. Whether you're a business leader looking to streamline operations, a software developer aiming to optimise workflows, or a researcher seeking high-level data insights, AI agents are reshaping how we interact with technology. Unlike traditional AI models that require direct prompts and oversight, AI agents work proactively. They can conduct research, manage sales pipelines, debug code, and even drive enterprise decision-making with minimal human input. What was once the realm of science fiction - an AI assistant that can schedule meetings, generate reports, or even close deals - is now a reality. This shift is not just about automation; it's about intelligence, adaptability, and efficiency at an unprecedented scale. Leading the charge in this revolution are companies like OpenAI, Google, Microsoft, and Anthropic, each introducing AI agents tailored to specific industries and professional needs. Whether it's OpenAI's elite research agents, Microsoft's AI-powered sales assistants, or Google's advancements in conversational AI, these innovations are shaping the future of work. In this article, we take a deep dive into how AI agents are changing the landscape, what they offer, and the potential implications for businesses and individuals alike. The emergence of AI agents is more than just a trend; it signifies a fundamental shift in how tasks are automated and optimised. Companies across sectors are leveraging these intelligent virtual assistants to handle complex workflows, analyse large datasets, and offer conversational intelligence that rivals human interaction. Avaamo's third-generation Intelligent Virtual Assistants (IVA) exemplify how AI is reshaping enterprise workflows. These agents utilise advanced Natural Language Understanding (NLU) to engage in humanlike conversations, automate business operations, and improve customer service efficiency. With built-in multi-intent recognition and deep integrations with enterprise security and compliance tools, Avaamo's IVAs enhance productivity while maintaining organisational security standards. The use of AI-generated conversation flows means businesses no longer need to manually design scripts for customer interactions. Instead, AI dynamically structures responses based on user queries, ensuring a seamless and efficient experience. Google's recent AI advancements include the Gemini 2.0 Flash, a high-performance conversational assistant embedded in the Gemini app. The company has also introduced the Google Cloud Automotive AI Agent, which enhances in-car voice assistants, allowing natural language interactions with drivers. Another significant AI agent from Google is NotebookLM Plus, which acts as an intelligent research assistant for businesses. It enables users to synthesise complex ideas and streamline project management. Meanwhile, Google's AI-powered tools are making their way into education, offering personalised learning experiences for students and teachers alike. Microsoft has taken AI agents into the sales domain with its latest additions to Microsoft 365 Copilot. The Sales Agent and Sales Chat are designed to help sales teams close deals more efficiently by automating lead generation, scheduling meetings, and analysing CRM data. These AI agents integrate directly with Microsoft Dynamics 365 and Salesforce, enabling seamless business operations. With the ability to retrieve actionable insights from emails, meetings, and customer interactions, they provide real-time assistance that reduces manual workload and improves sales performance. Anthropic's Claude Code is an AI agent focused on software development. It integrates with development environments, assisting programmers by editing files, debugging code, resolving merge conflicts, and optimising workflows. By embedding AI directly into the coding process, Claude Code streamlines development and reduces the time spent on troubleshooting. The AI agent operates within a terminal interface and can execute tasks such as running tests, reviewing pull requests, and generating documentation, making it an invaluable tool for software engineers. OpenAI is pushing the boundaries of AI-driven research with its specialised AI agents designed for high-income professionals. The company's forthcoming AI lineup includes an agent for researchers priced at $20,000 per month, which is tailored for PhD-level work. These advanced AI models are intended to handle in-depth data analysis, scientific research, and strategic decision-making at an unprecedented scale. While the pricing of these elite AI agents raises questions about accessibility, they highlight the growing demand for AI-driven expertise in academia and business intelligence. Companies investing in these high-end solutions aim to leverage AI's potential to maximise productivity and innovation. As AI agents continue to evolve, their applications will expand into more industries, from healthcare and finance to customer support and education. However, concerns regarding ethical AI use, job displacement, and accessibility remain. The high cost of premium AI agents, such as OpenAI's research assistant, underscores the need for more democratised access to AI-powered tools. Additionally, issues such as AI hallucinations - where models generate inaccurate or misleading information - must be addressed to ensure reliability. Developers and organisations must implement robust fact-checking mechanisms to mitigate risks associated with AI-generated content.

Automation Revolution : OpenAI's AI Agents Are Changing Everything

Imagine a world where the tasks that once required years of education, training, and expertise could be handled by a machine. It sounds like science fiction, but OpenAI is making it a reality. With their latest AI agents, capable of performing everything from coding and debugging to PhD-level research, the professional landscape is on the brink of a seismic shift. These agents, priced between $2,000 and $20,000 a month, promise to transform industries by automating complex tasks. But with such innovation comes a wave of uncertainty -- what does this mean for the workforce, for businesses, and for society as a whole? If you've ever wondered how technology might reshape your career or the industries around you, this is a moment worth paying attention to. While the potential for efficiency and cost savings is undeniable, the implications for job displacement and economic inequality are equally pressing. OpenAI's ambitious plans raise questions that go beyond technology -- questions about ethics, accessibility, and the future of work itself. In this overview by Wes Roth explore the opportunities and challenges posed by these AI agents, offering insights into how businesses, governments, and individuals can prepare for what's to come. OpenAI anticipates a substantial increase in revenue, projecting growth from $1 billion in 2023 to $12.7 billion by 2025. A notable portion of this growth -- estimated at 20-25% -- is expected to stem from the adoption of AI agents. These agents are positioned as fantastic tools for businesses, capable of performing tasks that rival or even surpass the expertise of human professionals. While businesses stand to benefit from cost savings and operational efficiency, the economic shift poses significant challenges. Automation threatens to displace jobs, particularly in sectors such as software development and research, where entry-level and mid-level roles are most vulnerable. This disruption could lead to workforce instability, necessitating proactive measures. Governments and organizations will need to invest in reskilling programs to prepare workers for new opportunities and establish regulatory frameworks to manage the transition effectively. OpenAI's pricing structure reflects the advanced capabilities of its AI agents, which are tailored to meet diverse business needs and levels of complexity: These agents excel in automating intricate processes, such as configuring software environments, executing commands, and troubleshooting technical issues with minimal human intervention. For example, tools like Claude Coder demonstrate the ability to streamline technical workflows, saving time and resources. However, limitations remain. Tasks requiring nuanced judgment, creativity, or the interpretation of visual data still demand human expertise, highlighting the current boundaries of AI capabilities. Enhance your knowledge on AI agents by exploring a selection of articles and guides on the subject. Despite their potential, AI agents face significant challenges related to cost, accessibility, and ethical implications. For smaller businesses, the high price of advanced AI systems may act as a barrier, limiting their ability to compete with larger organizations that can afford such technologies. Open source AI alternatives may offer more affordable solutions, but they also come with risks, such as accelerating workforce disruption without adequate safeguards in place. Ethical concerns are equally pressing. The automation of skilled roles raises questions about job displacement and the societal impact of widespread AI adoption. Entry-level and mid-level workers are particularly at risk, which could exacerbate economic inequality and social divides. Addressing these challenges will require a balanced approach, where innovation is pursued alongside measures to protect livelihoods and ensure equitable access to AI technologies. OpenAI and other stakeholders must prioritize the development of ethical guidelines and strategies to mitigate these risks. The rapid advancement of AI technologies has intensified calls for regulatory oversight to address their societal impact. Policymakers face the challenge of fostering innovation while managing concerns about job displacement, economic inequality, and ethical use. Striking this balance will be critical to making sure that AI adoption benefits society as a whole. Human oversight remains a cornerstone of responsible AI deployment. While tools like OpenAI's "Operator" showcase the potential for AI to mimic human behavior in interacting with computers, they still require human intervention for tasks involving complex judgment or troubleshooting. This underscores the importance of maintaining a collaborative approach, where automation complements human expertise rather than replacing it entirely. As AI systems continue to evolve, making sure a balance between automation and human involvement will be essential to achieving responsible and effective outcomes. The timeline for the widespread adoption of AI agents remains uncertain, with estimates ranging from 2025 to the late 2020s. OpenAI faces competition from other players in the AI space, such as Anthropic, which are also advancing their capabilities. Meanwhile, open source developments could accelerate adoption by reducing costs, but they also introduce risks of uncontrolled disruption and misuse. As businesses and governments navigate this rapidly changing landscape, the focus must remain on managing the transition effectively. Key strategies include investing in workforce reskilling, implementing regulatory oversight, and establishing ethical guidelines to govern AI use. These measures will be critical to making sure that the benefits of AI are realized without deepening societal inequalities. By addressing these challenges proactively, stakeholders can help shape a future where AI serves as a tool for progress and inclusivity.