Latest news with #digitalLabor
Forbes
18-07-2025
- Business
- Forbes
From Tools To Teammates: How AI Agents Will Become Digital Labor
OpenAI just released its ChatGPT Agent - is this the beginning of AI agents going mainstream? The future of work just arrived. On July 17, 2025, OpenAI launched ChatGPT Agent, marking a pivotal moment in artificial intelligence evolution. This isn't just another AI chatbot. This is the beginning of digital labor. Think of having a digital colleague that can now create presentations, navigate websites, conduct deep research, and complete complex tasks on their own (in AI speak, 'autonomously'). For businesses and consumers alike, this represents a fundamental shift in how everyone will work, shop, and interact with technology. Understanding agentic AI's game-changing potential The numbers tell a compelling story: Grand View Research estimates the global AI agents market is set to explode from $5 billion in 2024 to $50 billion by 2030, a 46% compound annual growth rate. More importantly, according to new research from the Capgemini Research Institute, AI agents could generate up to $450 billion in economic value by 2028 through revenue growth and cost savings. Yet despite this massive opportunity, only 2% of organizations have deployed AI agents at scale, creating a narrow window for competitive advantage that won't remain open for long. Unlike traditional AI that responds to prompts, agentic AI possesses genuine 'agency' - the ability to set goals, make decisions, and take actions with minimal human oversight. Harvard Business Review describes these systems as having "supercharged reasoning and execution capabilities" that go far beyond simple question-answering to actually performing complex tasks. The distinction is crucial: while generative AI is more about language to language and creates content, agentic AI is about multi-step reasoning, planning and it acts. It can book your flights, process insurance claims, manage inventory, and even conduct comprehensive research across hundreds of sources. This autonomous capability transforms AI from a tool into a true digital teammate. What Exactly Is an AI Agent? Unlike traditional AI that responds to prompts, an AI agent is artificial intelligence that handles multistep tasks without requiring a human to steer it the whole time. This is now the next phase of the AI era - 'Agentic AI'. While ChatGPT answers questions, AI agents actually do things - they book flights, process invoices, debug code, and conduct research across hundreds of sources autonomously. An example of OpenAI's ChatGPT Agent in action. The key differentiator: agents can take multiple actions, connect to various applications, and work for extended periods. OpenAI's Codex agent can work for up to 30 minutes without human supervision, while Anthropic's Claude 4 can tackle coding problems for up to seven hours straight. The Seven Species of Digital Workers While there will eventually be millions of agents, let's try to organize them into the distinct types of AI agents that are now entering the workforce. The Information had a nice way to summarize the different kinds of digital labor: What they do: Handle enterprise workflows across multiple software applications Digital labor: Invoice processing, data entry, document classification, scheduling Examples: UiPath, Microsoft Power Automate, Zapier + AI What they do: Resolve customer support and employee questions through dialogue Digital labor: Customer service, IT tickets, HR tasks Examples: Salesforce Agentforce, ServiceNow NowAssist, Sierra, Decagon What they do: Retrieve, analyze, and validate information from trusted sources Digital labor: Academic research, citation sourcing, technical analysisExamples: OpenAI Deep Research, Perplexity Pro, Scite Assistant, AlphaSense What they do: Analyze data to produce graphics, charts, and reports Digital labor: Data querying, dashboard creation, business insights Examples: Power BI Copilot, Tellius, ThoughtSpot, Glean What they do: Handle complex coding tasks for software engineers Digital labor: Code completion, debugging, documentation, site reliability Examples: Cursor, GitHub Copilot, Claude Code, Cognition's Devin What they do: Specialized work in regulated fields like law, medicine, finance Digital labor: Contract analysis, medical triage, financial analysis Examples: Harvey (legal), Hippocratic AI (healthcare), Rogo and Hebbia (finance) What they do: Navigate websites and handle repetitive online tasks Digital labor: Form filling, online ordering, social media posting Examples: OpenAI Operator, Google Project Mariner, Anthropic Computer Use OpenAI's bold vision becomes reality OpenAI's agent rollout began with Operator in January 2025, an AI capable of using web browsers like humans - clicking buttons, filling forms, and navigating websites. Then came Deep Research in February, which analyzes hundreds of sources to generate fully-cited reports in minutes. The July launch of ChatGPT Agent unified these capabilities, creating what The Wall Street Journal calls "an agent that can make spreadsheets and PowerPoints" while handling complex multi-step workflows. Sam Altman, OpenAI's CEO, predicts these agents will "materially change the output of companies" in 2025, estimating they can already handle "a single-digit percentage of all economically valuable tasks in the world." With 41.6% accuracy on complex reasoning benchmarks (double previous models) these agents represent a quantum leap in AI capability. Transforming experiences across consumer and business landscapes AI agents are revolutionizing both consumer experiences and business operations at unprecedented scale. For consumers, the transformation is happening at remarkable speed: recent reports show 95% of customer interactions are predicted to be handled by AI in 2025, while current deployments show AI-powered systems reducing resolution times by up to 52%and delivering 31.5% higher customer satisfaction scores compared to traditional support methods. The consumer impact extends far beyond convenience. Klarna's AI assistant reduced average customer issue resolution from 11 minutes to just 2 minutes while maintaining customer satisfaction scores equal to human agents. Virgin Money's AI assistant "Redi" has handled over 2 million customer interactions with a 94% satisfaction rate, demonstrating that consumers readily embrace AI-powered service when it delivers superior results. The retail sector shows equally impressive adoption, with 24% of consumers already comfortable with AI agents making purchases on their behalf—a figure that jumps to 32% among Gen Z shoppers, while 75% of customer inquiries can now be resolved by AI tools without human intervention. The business case for AI agents is equally compelling and backed by remarkable real-world results. Organizations implementing AI report 6-10% average revenue increases, with 62% of companies expecting full 100% or greater returns on investment. The operational improvements are staggering: companies report 83% experiencing revenue growth versus 66% without AI implementation, 76% improvement in operational efficiency, and financial institutions seeing increases in profitability through enhanced fraud detection and personalized service. Real-world success stories illustrate the transformative potential across industries. JPMorgan Chase's AI-driven "Coach" tool helps wealth advisers retrieve research 95% faster, contributing to a 20% year-over-year increase in asset management sales. The bank's AI initiatives have already saved nearly $1.5 billion through fraud prevention and operational efficiencies. Wiley achieved a 40% increase in case resolution with AI agents, while 76% of e-commerce teams credit AI with revenue growth and 92% of service teams report cost reductions. Manufacturing leaders report 40% reduction in downtime through AI-driven predictive maintenance. Employee productivity transformation is equally impressive, ranging from customer service agents answering more inquiries per hour, business professionals writing more documents per hour, and programmers coding more projects per week using AI agents. These are just early use cases, but you can already see how agentic ai will fundamentally redefine what exceptional customer experiences and business performance looks like. Why This Is Just the Beginning We're in the early innings of digital labor. Current agents still make mistakes and require human oversight, but they're evolving rapidly. The combination of cheaper reasoning models, better orchestration software, and expanding application integrations means agent capabilities are compounding quickly. The workforce of 2030 won't just include humans - it will be a hybrid ecosystem where digital agents handle routine tasks while humans focus on creativity, strategy, and relationship-building. We're not just automating work; we're creating a new category of digital colleague that augments human capability rather than simply replacing it. The age of digital labor has begun. The question isn't whether these AI agents will transform work - it's how quickly businesses and consumers will adapt to this new reality.
Bloomberg
09-07-2025
- Business
- Bloomberg
Salesforce's Head of AI Sees Digital Labor Revolution
Bloomberg Markets: The Close "The moment of the digital labor revolution is upon us," Adam Evans, Salesforce's head of AI, says. Speaking on "Bloomberg The Close," Evans discusses scaling the technology and driving productivity. (Source: Bloomberg)
Harvard Business Review
22-05-2025
- Business
- Harvard Business Review
Agentic AI Is Already Changing the Workforce
As AI matures, the availability of so-called 'digital labor' is exploding, expanding the very definition of a qualified workforce. What was once the exclusive domain of human talent has now been joined by AI agents capable of handling many tasks once considered beyond the reach of automation—and as a result, according to Salesforce CEO Marc Benioff, the total addressable market for digital labor could soon reach the trillions of dollars. This requires a major shift in outlook. Emerging research out of Harvard Business School and the Digital Data Design Institute shows that AI agents are fast becoming much more than just sidekicks for human workers. They're becoming digital teammates—an emerging category of talent. To get the most out of these new teammates, leaders in HR and procurement will need to start developing an operational playbook for integrating them into hybrid teams and a workforce strategy. Those who take the time to do so will unlock not just efficiency but a more scalable and resilient form of collaboration. It's already happening: Deloitte, for example, reports that it in the process of applying AI agents to 'every' enterprise process, including a marketing agent that orchestrates many tasks focused on optimizing their customers' journeys through their site. And some talent firms, among them rPotential (a spin-off from global staffing giant, Adecco) have reimagined themselves as not just providers of human talent but also architects of a broader model that includes both people and AI. To succeed in this new environment, your organization must actively shape how AI is integrated into its labor strategy. Leaders in HR and procurement who act now will retain control over how AI is sourced, structured, and regulated in the enterprise, whereas those who hesitate risk missing out on new growth opportunities—or, worse, being blindsided by compliance, ethics, and performance issues they never saw coming. This is a general strategic concern when it comes to anything disruptive: You can no longer ignore new technologies, especially AI, hoping they'll go away. You have to be deeply engaged with building your systems so that you deeply understand how you need to adapt as the world changes. Firms that delay will also struggle to attract top human talent, as more candidates will expect smart, AI-supported workflows that enhance their productivity and creativity. Meanwhile, faster-moving competitors will embed AI directly into their operating models, enabling them to out scale and outlearn by increasing output without adding headcount. In addition, as large enterprise and government buyers begin to demand robust, auditable AI policies and governance frameworks, organizations that lack maturity in these areas will find themselves at a disadvantage, potentially losing out on critical contracts and partnerships. Inaction, therefore, is not just a missed opportunity—it's a tangible business risk. Seven Critical Actions Drawing on our collective experience within data science and AI, and in the open-talent and staffing ecosystem, we've devised framework to help you get started—a guide to help HR and procurement teams design, test, and scale a new kind of workforce strategy based on the idea of human-AI teams. Map work tasks and outcomes. The objective here is to deconstruct each role or project into its component tasks and outcomes. Just as you would define competencies for human candidates, you need to identify the tasks that can be better, faster, or more cost-effectively handled by AI agents. For instance, high-volume data validation or repetitive call-center functions might be prime candidates for AI. Meanwhile, tasks requiring complex judgment, persuasion, or deep domain expertise may still lean on human insight—or a hybrid approach. The key thing to remember is that you're not just 'buying labor' anymore. You're buying an outcome that might come from a combination of people and AI. So, start each sourcing discussion with a nuanced breakdown of tasks, so you can tailor the right mix of talent. Assess AI capability. It's vital to understand which AI models and platforms align best with your specific tasks and workflows. To do that, build an internal taxonomy of AI capabilities that map to your common roles not only in, say, data validation or call-center functions but also other roles, including marketing analyst, customer-support rep, scheduling coordinator, and so on. Language models might excel at drafting marketing copy, but specialized computer-vision agents might be best for quality checks in manufacturing. By creating a capabilities 'catalogue,' you can avoid a one-size-fits-all approach. In procurement terms, this is your RFP for AI solutions. Know which models solve which problems, and partner with staffing or technology vendors that can prove domain expertise. This ensures you're not paying top dollar for AI hype that may not match the actual needs of the business. Integrate your hybrid team. If you want AI agents and human teams to work well together, you need crystal-clear role boundaries. So, develop a hybrid-workforce strategy in which you define which tasks AI will own, which tasks people will own, and how the escalation of problems should happen. For example, if an AI customer-service agent receives a complaint about a complex billing dispute that concerns an amount above a certain dollar threshold, a rule might automatically route it to a human specialist. By documenting roles, protocols, and 'hand-off' points when responsibilities shift in a workflow from one party to another, you build trust across your organization, preventing conflict or duplication. Redesign your business (and workforce) model. This requires envisioning new ways to procure and deploy talent, including full-time employees, temporary hires, freelancers, and AI. To do that, you'll need to consider multi-tiered models such as client-owned digital labor (you license or build your own AI solutions, effectively bringing 'digital employees' in-house); leased digital labor (you 'rent' AI agents from a third party, in a manner akin to traditional temp staffing); and fully outsourced AI subdepartments (you partner with a vendor that runs entire processes, such as order fulfillment or call centers, using both AI and a small team of human experts). Align these models with your financial, compliance, and strategic needs. For instance, leasing AI might be ideal for seasonal spikes, whereas fully outsourced solutions could be more efficient for repetitive but critical functions. Keep in mind that your role is to integrate and manage a wide spectrum of labor types, which will require developing new KPIs and cost structures that reflect digital labor's unique economics (scalability, near-constant uptime, quick retraining) compared to human labor. Set legal and ethical ground rules. The point here is to proactively address bias, liability, data governance, and broader societal implications of AI-led work. To do that, you'll need to collaborate with legal, compliance, and ethics teams to draft enterprise-wide standards for AI usage. These policies should define whether and how AI learns from proprietary data, how to detect and remedy bias, and how to safeguard personal or sensitive information. If your enterprise is global, anticipate a patchwork of regulations in different jurisdictions. This is important: Ethical missteps —like discriminatory hiring algorithms or data misuse—can ignite PR crises and create regulatory liabilities. Many governments are moving quickly on AI legislation. Firms that proactively create frameworks and foster AI culture early will be able to adjust and react to future legislation much better than firms who wait and are forced to start the process after any legislation. Capture value continuously as it evolves. To do this properly, you'll need to constantly monitor performance, measure outcomes, and refine your AI-human mix. Think beyond a one-time AI deployment. Establish feedback loops that measure performance, update AI training data, and revise your sourcing strategies. For instance, if your AI-based scheduling tool frequently encounters edge cases requiring human intervention, you may need more advanced AI training or more robust human oversight. Traditional 'set it and forget it' approaches to staffing don't apply here. AI's value compounds over time as it learns from interactions. Negotiate contractual agreements that let you capture improvements while also respecting the intellectual property (IP) rights of your vendor or the data sovereignty of your enterprise. Remain human-centric. AI reduces the need for people to conduct mundane tasks and elevates the importance of high-value, human-led tasks. Ensuring that employees can continue to carry these latter tasks out not only sustains morale but also delivers differentiating value to your enterprise, which is something your competitors can't simply download. So invest in forms of training and skill development that enable employees to not only adapt to working alongside AI but also leverage AI to amplify their own impact. Focus on capabilities like relationship building, ethical decision-making, and creativity—areas where humans still have a distinct edge. Preparing for Radical Change Whether you adopt AI labor directly or source it through a staffing or open-talent provider, ask yourself these pivotal questions to guide your strategy: When AI is trained on your proprietary data, who owns the resulting capabilities, you or the owner of the AI model or agent? Are new legal frameworks needed, including employment-like contracts for AI agents, and who bears liability if the AI makes a mistake? Are there oversight and equity questions that remain unresolved? What guidelines exist for choosing between humans and AI for certain jobs, especially when ethics, brand reputation, or job protection are on the line? And, ultimately, an open question for discussion: How will the very definition of 'work' evolve when AI agents become embedded in teams, possibly even gaining legal or ethical status? It's not necessary to answer all of these questions upfront, but use them as a guide and continue to answer them along your journey. These are not theoretical concerns—they're strategic inflection points. The organizations that move first to resolve them will shape how the future of work is defined and who controls its value. As you consider how to move forward, reflect on the guiding principle of human centricity. While AI can handle many tasks more quickly than humans, your enterprise still relies on the insight, empathy, and relationships that only people can deliver. By maintaining this dual focus—on unleashing AI's efficiency and safeguarding human creativity—you stand the best chance of driving sustainable growth.
