Tag: Software Development

  • Three AI Trends That Change Jobs

    Three AI Trends That Change Jobs

     What Sam Altman’s Vision at the Cisco AI Summit Means for Enterprise Workforce Strategy

    By Maribel Lopez

    Cisco builds over 70% of its AI software products using AI. Not on a roadmap. Not as a pilot. Today, in production, through its partnership with OpenAI’s Codex platform. When Jeetu Patel, Cisco’s Chief Product Officer, shared this AI trend at the Cisco AI Summit alongside OpenAI CEO Sam Altman, the audience heard more than a product update. They heard a preview of how labor itself is about to be restructured.

    For CIOs and CEOs rethinking workforce strategy, three shifts from this conversation demand immediate attention: AI that acts on your behalf, the transformation of software development roles, and the emergence of AI-only companies as a new category of outsourced labor.

    Shift 1: AI That Acts on Your Behalf

    AI is crossing from finding information to acting on it. For years, the promise centered on surfacing insights, answering questions, connecting dots across silos. What Altman described is an evolution of the agentic AI trend. He described an always-on AI that accesses your computer, browses the web, edits your documents, and executes tasks without waiting for human approval.

    This shift is already underway. Consumers use OpenClaw’s Clawdbot as a personal assistant, granting it access to everything (risky, but the usefulness is undeniable). On the enterprise side, SaaS vendors are embedding agents into customer service platforms, IT operations workflows, and sales processes where AI doesn’t just recommend an action but completes it. These deployments remain narrow: an agent that resolves a tier-one support ticket, triages security alerts, or drafts and sends a follow-up email after a sales call. But they mark the beginning of a fundamental change in knowledge work. The AI no longer waits for you to act on its suggestion. It acts.

    Altman described giving Codex full access to his computer and lasting only two hours before he couldn’t go back. He acknowledged the real challenges this creates around security, data access, and permissioning. Existing software, hardware, and even legal frameworks weren’t designed for always-on AI that watches what you do and takes action on your behalf.

    For enterprise buyers, this reinforces a message I’ve been sharing for some time: the AI infrastructure conversation extends well beyond models and compute. Identity frameworks, governance stacks, observability, and security architectures all need rethinking, because they were designed for people, not AI agents. The good news is that organizations already investing in these foundational capabilities will absorb AI labor more safely and more quickly. But it requires treating security and governance as enablers of AI adoption, not obstacles to it.

    Shift 2: Software Development Roles Are Being Redefined, Not Eliminated

    Patel described how Cisco works with OpenAI and Codex to fundamentally change how it develops software. AI Defense, a security product Cisco launched last year, will have nearly 100% of its code written by Codex within weeks. This reflects a pattern that will spread across the enterprise.

    Developers aren’t going away, but their job is evolving. The core competency shifts from writing code to constructing precise software requirements, evaluating whether AI output meets those requirements, and articulating what needs to change when it doesn’t. Running tests, writing documentation, producing boilerplate? AI handles that. Defining what the software should accomplish and judging whether it got there? Still human.

    Altman described Codex as feeling less like a tool and more like a teammate: “The Codex app is the first time, to me, it has truly felt like interacting with a teammate.” That distinction matters. When AI shifts from tool to collaborator, the human role shifts from operator to supervisor. CIOs should already be rethinking team composition, performance evaluation, and career development within their engineering organizations.

    Even with AI doing the heavy lifting, design still matters enormously. As Altman noted: “There’s so much value in how you package it, how you have users interact with it, how easy you can make it.” Better models alone don’t guarantee better outcomes. The interface, the workflow, the experience determine whether adoption accelerates or stalls.

    A deeper shift sits underneath this AI trend.  The future of software requires designing it to work equally well whether a human or an AI operates it. That’s not how software works today. Most software isn’t even easy for humans to use, let alone optimized for AI agents. Altman illustrated this with a telling example: his AI agent used Slack on his behalf, marked everything as read, and broke his workflows. Software built for one type of user doesn’t automatically serve another. This is a design problem as much as a technology problem, and product teams and CIOs need to tackle it now.

    Shift 3: AI-Only Companies and the New Workforce Marketplace

    The third shift is the most speculative but potentially the most disruptive. Altman described a future with “full AI companies”: a coding model creates a complete, complex piece of software and also interacts with the real world to build a company around it.

    Consider what that implies. Not AI-assisted companies. AI-only companies: entities with no human employees, just AI systems performing the work. You would hire them the same way you hire a consulting firm or a staffing agency today.

    The concept follows a natural progression from agentic AI. Today, leading enterprises build a variety agents with the aim of having the agents collaborate to accomplish specific goals. Agents perform a task here, an automated workflow there. As agents grow more sophisticated, more of a given role consolidates into a single agentic entity. That entity can then be sold as a digital employee, just as you would hire a temporary worker from an agency or outsourcing firm.

    I can see a marketplace emerging where enterprise buyers source these agents. Today, you acquire them from software vendors and hyperscalers. But nothing prevents a person from building an entirely new AI workforce company. However, it’s probably too soon to call this an AI trend, but I expect we’ll see a variant of this soon. Envision your company hiring a cybersecurity agent from an AI agent company to build new security playbooks. The technology to support this is coming together now.

    But CEOs and CIOs need to understand something: the existence of an agent marketplace doesn’t mean you can just show up and shop. It will be like facing a thousand choices in the cereal aisle. You need to know whether you want hot or cold cereal before you walk into the store, and that’s just the first filter. Cold cereal? Sweet like Fruity Pebbles or plain like Rice Krispies? True success requires knowing exactly what talents your organization lacks and targeting AI to fill those specific gaps. Skip the hard work of defining the skills and roles you actually need, and you’ll end up overwhelmed by options or acquiring agents that don’t solve your real problems. The companies that benefit most from this marketplace will be the ones that mapped their talent gaps first.

    The implications run deep. Outsourcing firms that provide human labor for repeatable tasks face a direct competitive threat and must learn to integrate AI faster and better than their customers do. Companies struggling with persistent talent gaps in cybersecurity, data engineering, or compliance could discover a genuinely new category of solution. But it also raises hard questions about governance, accountability, and quality assurance when the “worker” is an AI system contracted from a third party.

    The Real Shift Is in Software Itself

    All three of these changes point to the same underlying transformation. The question isn’t whether AI will change your workforce. It already has. The question is whether your software, your infrastructure, and your design thinking are ready for a world where AI isn’t just a tool your employees use but a co-worker that uses your software right alongside them.

    Software is changing not just in how it gets developed but in who the user is. When the cloud emerged, companies had to rethink applications for a new delivery model. When mobile took off, they had to redesign for a second screen. AI demands something bigger: a new UX paradigm where humans and AI agents work within the same systems, using the same data, without breaking each other’s workflows. The companies that design for that world will be the ones that capture the value from everything Altman described. Start there. 

  • Three Shifts in AI-Driven Labor That CIOs and CEOs Can’t Ignore

    Three Shifts in AI-Driven Labor That CIOs and CEOs Can’t Ignore

    What Sam Altman’s Vision at the Cisco AI Summit Means for Enterprise Workforce Strategy

    By Maribel Lopez, Lopez Research  |  February 2026

    Cisco builds over 70% of its AI software products using AI. Not on a roadmap. Not as a pilot. Today, in production, through its partnership with OpenAI’s Codex platform. When Jeetu Patel, Cisco’s Chief Product Officer, shared this at the Cisco AI Summit alongside OpenAI CEO Sam Altman, the audience heard more than a product update. They heard a preview of how labor itself is about to be restructured.

    For CIOs and CEOs rethinking workforce strategy, three shifts from this conversation demand immediate attention: AI that acts on your behalf, the transformation of software development roles, and the emergence of AI-only companies as a new category of outsourced labor.

    Shift 1: AI That Acts on Your Behalf

    AI is crossing from finding information to acting on it. For years, the promise centered on surfacing insights, answering questions, connecting dots across silos. What Altman described goes further: always-on AI that accesses your computer, browses the web, edits your documents, and executes tasks without waiting for human approval.

    This shift is already underway. Consumers use OpenClaw’s Clawdbot as a personal assistant, granting it access to everything (risky, but the usefulness is undeniable). On the enterprise side, SaaS vendors are embedding agents into customer service platforms, IT operations workflows, and sales processes where AI doesn’t just recommend an action but completes it. These deployments remain narrow: an agent that resolves a tier-one support ticket, triages security alerts, or drafts and sends a follow-up email after a sales call. But they mark the beginning of a fundamental change in knowledge work. The AI no longer waits for you to act on its suggestion. It acts.

    Altman described giving Codex full access to his computer and lasting only two hours before he couldn’t go back. He acknowledged the real challenges this creates around security, data access, and permissioning. Existing software, hardware, and even legal frameworks weren’t designed for always-on AI that watches what you do and takes action on your behalf.

    For enterprise buyers, this reinforces a message I’ve been sharing for some time: the AI infrastructure conversation extends well beyond models and compute. Identity frameworks, governance stacks, observability, and security architectures all need rethinking, because they were designed for people, not AI agents. The good news is that organizations already investing in these foundational capabilities will absorb AI labor more safely and more quickly. But it requires treating security and governance as enablers of AI adoption, not obstacles to it.

    Shift 2: Software Development Roles Are Being Redefined, Not Eliminated

    Patel described how Cisco works with OpenAI and Codex to fundamentally change how it develops software. AI Defense, a security product Cisco launched last year, will have nearly 100% of its code written by Codex within weeks. This reflects a pattern that will spread across the enterprise.

    Developers aren’t going away, but their job is evolving. The core competency shifts from writing code to constructing precise software requirements, evaluating whether AI output meets those requirements, and articulating what needs to change when it doesn’t. Running tests, writing documentation, producing boilerplate? AI handles that. Defining what the software should accomplish and judging whether it got there? Still human.

    Altman described Codex as feeling less like a tool and more like a teammate: “The Codex app is the first time, to me, it has truly felt like interacting with a teammate.” That distinction matters. When AI shifts from tool to collaborator, the human role shifts from operator to supervisor. CIOs should already be rethinking team composition, performance evaluation, and career development within their engineering organizations.

    Even with AI doing the heavy lifting, design still matters enormously. As Altman noted: “There’s so much value in how you package it, how you have users interact with it, how easy you can make it.” Better models alone don’t guarantee better outcomes. The interface, the workflow, the experience determine whether adoption accelerates or stalls.

    A deeper shift sits underneath this conversation: the future of software requires designing it to work equally well whether a human or an AI operates it. That’s not how software works today. Most software isn’t even easy for humans to use, let alone optimized for AI agents. Altman illustrated this with a telling example: his AI agent used Slack on his behalf, marked everything as read, and broke his workflows. Software built for one type of user doesn’t automatically serve another. This is a design problem as much as a technology problem, and product teams and CIOs need to tackle it now.

    Shift 3: AI-Only Companies and the New Workforce Marketplace

    The third shift is the most speculative but potentially the most disruptive. Altman described a future with “full AI companies”: a coding model creates a complete, complex piece of software and also interacts with the real world to build a company around it.

    Consider what that implies. Not AI-assisted companies. AI-only companies: entities with no human employees, just AI systems performing the work. You would hire them the same way you hire a consulting firm or a staffing agency today.

    The concept follows a natural progression from agentic AI. Today, leading enterprises build a variety agents with the aim of having the agents collaborate to accomplish specific goals. Agents perform a task here, an automated workflow there. As agents grow more sophisticated, more of a given role consolidates into a single agentic entity. That entity can then be sold as a digital employee, just as you would hire a temporary worker from an agency or outsourcing firm.

    I can see a marketplace emerging where enterprise buyers source these agents. Today, you acquire them from software vendors and hyperscalers. But nothing prevents a person from building an entirely new AI workforce company. Envision your company hiring a cybersecurity agent from an AI agent company to build new security playbooks. The technology to support this is coming together now.

    But CEOs and CIOs need to understand something: the existence of an agent marketplace doesn’t mean you can just show up and shop. It will be like facing a thousand choices in the cereal aisle. You need to know whether you want hot or cold cereal before you walk into the store, and that’s just the first filter. Cold cereal? Sweet like Fruity Pebbles or plain like Rice Krispies? True success requires knowing exactly what talents your organization lacks and targeting AI to fill those specific gaps. Skip the hard work of defining the skills and roles you actually need, and you’ll end up overwhelmed by options or acquiring agents that don’t solve your real problems. The companies that benefit most from this marketplace will be the ones that mapped their talent gaps first.

    The implications run deep. Outsourcing firms that provide human labor for repeatable tasks face a direct competitive threat and must learn to integrate AI faster and better than their customers do. Companies struggling with persistent talent gaps in cybersecurity, data engineering, or compliance could discover a genuinely new category of solution. But it also raises hard questions about governance, accountability, and quality assurance when the “worker” is an AI system contracted from a third party.

    The Real Shift Is in Software Itself

    All three of these changes point to the same underlying transformation. The question isn’t whether AI will change your workforce. It already has. The question is whether your software, your infrastructure, and your design thinking are ready for a world where AI isn’t just a tool your employees use but a co-worker that uses your software right alongside them.

    Software is changing not just in how it gets developed but in who the user is. When the cloud emerged, companies had to rethink applications for a new delivery model. When mobile took off, they had to redesign for a second screen. AI demands something bigger: a new UX paradigm where humans and AI agents work within the same systems, using the same data, without breaking each other’s workflows. The companies that design for that world will be the ones that capture the value from everything Altman described. Start there.

  • Microsoft Build 2025: Bridging the Gap Between AI POCs and Agentic AI Success

    Microsoft Build 2025: Bridging the Gap Between AI POCs and Agentic AI Success

    The AI Acceleration Challenge

    The AI market has rapidly evolved from early chatbot failures to a landscape of simplified access to information driven by generative AI, with agentic AI coming soon. Yet, most organizations are still struggling to move beyond proof-of-concept projects to production deployments that deliver measurable business value. Over 80% of firms interviewed by Lopez Research report significant technical skills shortages and challenges with change management when deploying AI systems. The landscape has also become increasingly complex, with thousands of AI models and multiple approaches to designing, deploying, and managing AI solutions.

    Before enterprises had even fully embraced conversational interfaces within various SaaS solutions, the technology industry rapidly moved toward creating agentic AI products. Instead of AI that assists people, the industry has pushed toward systems that can operate autonomously, reason through complex problems, and take action without always requiring human guidance. While agentic systems represent the future of AI, the inherent risks in autonomous systems terrify all but the most fearless companies.

    Amplified Risks in an Autonomous Agentic AI World

    AI systems face significant reliability challenges, including unpredictable outputs, “hallucinations” where they generate false information, and brittleness when encountering unfamiliar scenarios. These systems can also experience model drift as underlying data patterns change over time, leading to degraded performance without obvious warning signs. From a security perspective, AI systems remain vulnerable to sophisticated attacks, including adversarial inputs designed to manipulate outputs, data poisoning that corrupts training datasets, and inference attacks that can extract sensitive information from trained models.

    These challenges become exponentially more serious with agentic AI. Reliability issues that may cause minor inconveniences when a human is in the loop will become critical safety concerns when agents can act autonomously. An agentic AI experiencing model drift or brittleness could make consequential decisions affecting business operations, financial transactions, or even physical systems without human oversight. Security vulnerabilities become particularly dangerous, as adversarial attacks can manipulate agents into taking harmful actions, while data poisoning can corrupt not only outputs but entire chains of autonomous decision-making.

    Companies Flock To Strategic Vendors For AI Platforms

    The AI sprawl of startup vendors and highly specialized solutions only creates increased anxiety. Today, most business leaders are looking to a handful of strategic technology vendors to offer more comprehensive systems for designing, securing, maintaining, and governing AI that supports many but not all of the AI functions. It’s crucial that these strategic vendors also provide ecosystem-friendly platforms that can connect with other third-party technology vendors when necessary.

    Microsoft Responds with Advances in Agentic AI 

    At Microsoft’s Build 2025 conference, CEO Satya Nadella advanced the company’s vision for an “open, agentic web.” Microsoft’s announcements encompass over 50 new AI tools and platforms, focusing on agentic AI capabilities that promise to transform how organizations develop software, conduct research, and manage business processes. 

    Microsoft calls its Microsoft 365 Copilot “the UI for AI,” providing a place where people and teams interact with agents in the flow of work. However, since last year’s Build conference, the company has moved well beyond Microsoft Copilot as a conversational interface to also offering a more comprehensive platform for designing and using agents. This is in addition to providing its own AI models and special-purpose AI hardware. The latest Microsoft Build announcements showcase several key updates that demonstrate Microsoft’s commitment to addressing enterprise concerns about the deployment of autonomous AI while maximizing its transformative potential.

    1. Evolving Software Development

    Microsoft Build has always focused on tools for software development. The evolution of GitHub Copilot from code suggestion to an autonomous coding agent represents a significant shift in software development lifecycle management. The new agent handles end-to-end programming tasks, including bug fixes, feature implementation, and code refactoring. Embedded directly into GitHub, the agent activates when developers assign a GitHub issue to Copilot or prompt it in VS Code, spinning up secure and fully customizable development environments powered by GitHub Actions.

    The autonomous handling of routine coding tasks allows development teams to focus on architecture and innovation, effectively multiplying the strategic impact of existing technical staff. Beyond productivity gains, the system ensures consistent application of best practices across entire codebases, reducing quality variations that often plague large development organizations. Perhaps most significantly for enterprise operations, the platform reduces dependency on individual developer expertise, creating more resilient and maintainable systems where institutional knowledge embedded in the AI agent ensures continuity when personnel changes occur.

    The advances in GitHub Copilot address one of the most persistent challenges facing technical leaders: the growing gap between the demands of software development and the available talent. At Build, Microsoft shared that Ramp, a spend management platform, saves approximately 30,000 hours of manual work per month by utilizing GitHub Copilot. Cathay Pacific, Hong Kong’s largest airline, similarly leveraged GitHub Copilot to save developer time and increase productivity across their development teams.

    2. Delivering Choice and Interoperability

    Microsoft’s Azure AI Foundry Agent Service represents the cornerstone of enterprise agentic AI deployment. Now generally available, the platform provides enterprise-grade infrastructure for production AI agent deployments, integrating Semantic Kernel and AutoGen into a unified SDK while supporting Agent-to-Agent (A2) communication and the Model Context Protocol (MCP). The support for A2A and MCP means it will be easier for companies to create Agentic AI systems where agents can connect and collaborate across various applications and data sources. 

    Microsoft’s commitment to the Model Context Protocol across its entire platform stack addresses a critical enterprise concern: avoiding AI vendor lock-in while enabling sophisticated integrations. MCP integration with Windows will provide a standardized framework for AI agents to connect with native Windows applications, allowing seamless agent-based interactions. The support for the A2A protocol, an open standard, also enables Microsoft to integrate with other major technology companies, including Google (the originator of the protocol), Atlassian, Box, Cohere, Intuit, LangChain, MongoDB, PayPal, Salesforce, SAP, ServiceNow, UKG, and many others.

    Organizations will also gain model flexibility with access to over 11,000 models through a Hugging Face integration in addition to the existing 1,900 models in the Microsoft catalog. These capabilities enable organizations to integrate best-of-breed AI models regardless of vendor and optimize costs by selecting the right model for the right workload. 

    3. Advanced Workflow Automation

    The introduction of multi-agent systems enables sophisticated workflow automation where specialized agents collaborate to handle complex business processes within the Microsoft ecosystem and across applications. This multi-agent orchestration transforms organizational thinking about business process automation, shifting from rigid, predetermined workflows to intelligent systems that adapt and collaborate in real time.

    Cross-platform integrations demonstrate the versatility of this capability. Adobe’s marketing agent, integrated with Microsoft 365 Copilot, enables marketers to access audience analysis without needing to switch applications. ServiceNow AI agents work with Microsoft 365 Copilot for service management. SAP integration through SAP Joule enables the creation of custom AI agents for SAP workloads using Azure AI services while also allowing SAP users to access data within Microsoft 365 applications.

    Financial operations benefit similarly, with compliance agents working alongside analysis agents to produce automated reporting that maintains accuracy while reducing manual oversight requirements. Supply chain management becomes more responsive as demand forecasting agents coordinate directly with inventory management agents, creating dynamic, self-optimizing systems that respond to market changes in real time.

    4. Observability for Agentic Systems

    For technology leaders grappling with the complexities of AI deployment, this platform addresses several fundamental concerns that have historically hindered the success of AI initiatives. In addition to supporting open protocols for agent communication and governed access to multiple types of AI, it also offers built-in observability. Observability provides real-time metrics for performance, quality, cost, and safety, giving executives the visibility needed to manage AI operations with confidence. 

    Rather than deploying AI as a black box, organizations can now monitor and optimize their AI investments with the same rigor applied to traditional enterprise systems. The platform’s integrated compliance controls help prevent “agent sprawl,” which many organizations fear as AI adoption accelerates, enabling centralized oversight while increasing accessibility through a centralized agent store.

    AI Can Deliver Impact

    Real-world implementations demonstrate measurable impact. Accenture has leveraged Azure AI Foundry for AI and agent-led business process transformations, achieving a 30% increase in efficiency and a 50% reduction in AI application development time. Carvana developed an agent that analyzes customer interactions, resulting in a 40% reduction in inbound sales calls. The Indiana Pacers created an in-arena real-time captioning system with error rates reduced to just 1 percent. 

    Early implementations highlight the potential of agentic AI in mission-critical environments. Stanford Medicine’s Healthcare Agent Orchestrator, built on Azure AI Foundry, transforms cancer care delivery across approximately 4,000 tumor board meetings per year. The system consolidates fragmented information from multiple sources, integrates patient history with radiology data, medical literature, and clinical trials, and generates comprehensive reports for clinicians, thereby reducing the time spent on manual information gathering. Deployed into Microsoft Teams with a foundation in specific clinical notes, the solution enhances patient decision-making by making it more efficient, faster, and potentially more accurate while enabling the sharing of advanced medical AI capabilities with community hospitals to democratize these capabilities. 

    The NFL’s implementation demonstrates the impact of agentic AI on data-driven decision-making. Azure AI Foundry revolutionized scouting operations by combining previously scattered data systems, enabling teams to ask specific player questions and receive immediate comparative analysis, complete data filtering in seconds rather than hours, access detailed player information in real-time during evaluations, and perform instant queries like “give me the fastest 40 times of a defensive lineman.” This transformation gave NFL teams significant competitive advantages in player evaluation by fundamentally changing how they process and analyze scouting data.

    Build Your Foundation Wisely

    For technology leaders, the strategic imperative is clear: organizations that successfully implement agentic AI will gain substantial competitive advantages in efficiency, innovation speed, and operational capability. The key to success lies not in the technology itself but in thoughtful implementation that addresses governance, security, and change management challenges while maximizing the transformative potential of autonomous AI systems. While organizations should proceed with caution, the latest offerings announced by Microsoft and others signal that the technology market is providing more mature offerings to mitigate risks, suggesting that the foundation for safe, effective agentic AI deployment is rapidly solidifying.