Part Three: Build vs. Buy vs. Partner; Strategic Decisions for Agentic AI Capabilities
The most sophisticated organizations recognize that the choice between building, buying, and partnering doesn't have to be binary or permanent. Hybrid approaches that combine different strategies across time or functional areas often provide optimal results by allowing organizations to balance speed, control, cost, and risk according to their specific circumstances and evolving needs.
Common hybrid models demonstrate how organizations can strategically sequence their approaches to maximize learning and minimize risk. The "buy to prototype, build for scale" model allows organizations to rapidly deploy vendor solutions to understand requirements and validate use cases before investing in internal development. This approach enables learning from real-world usage while maintaining the option to develop proprietary capabilities for strategic applications.
Part Two: Build vs. Buy vs. Partner; Strategic Decisions for Agentic AI Capabilities
In Part Two of Build vs. Buy vs. Partner we look at the three approaches in more detail. The criteria for choosing each scenario is very dependent on several factors including organizational capabilities, AI expertise, use cases, specific requirements versus speed of deployment and several other factors. Understanding all the relevant organizational context can lead to much more effective approaches to agentic AI deployment. In Part Three of the article we’ll look at the case for hybrid models and methods for phasing the implementation.
Part One: “Build vs. Buy vs. Partner: Strategic Decisions for Agentic AI Capabilities”
Enterprise technology is evolving as organizations move beyond viewing artificial intelligence as merely a collection of tools and begin embracing it as a source of autonomous digital teammates. This transformation is more than just technological evolution, it’s a strategic imperative that is reshaping how businesses think about automation, decision-making, and competitive advantage.
Agentic AI systems differ from the AI assistants and automation tools that preceded them. Where traditional AI might help you analyze data or automate repetitive tasks, agentic AI can reason through complex scenarios, make decisions within defined parameters, and take actions on behalf of the organization. These systems can manage customer inquiries from start to resolution, orchestrate complex business processes across multiple systems, and even generate new insights that drive strategic decisions.
Manufacturing's Digital Workforce: Beyond Automation to Intelligent Production
The factory floor is experiencing a transformation that goes far beyond the mechanical automation we've known for decades. While traditional automation focused on replacing human muscle with machines, today's manufacturing revolution centers on creating intelligent systems that can think, adapt, and collaborate. This shift is the emergence of what we call the "digital workforce”; a sophisticated ecosystem where artificial intelligence agents, smart robots, and connected systems work alongside human workers to create truly intelligent production environments.
Agentic AI Communication Protocols: The Infrastructure of Intelligent Coordination
Agentic AI is a paradigm shift from traditional AI systems that simply respond to queries (reactive) toward autonomous systems capable of perceiving their environment, reasoning about complex situations, and taking independent action to achieve goals (proactive). These intelligent agents can operate across diverse use cases, from managing customer service workflows to coordinating robotic systems in manufacturing environments. Unlike static AI models, agentic systems demonstrate agency through their ability to plan multi-step processes, adapt to changing conditions, and collaborate with both humans and other AI systems.
Agentic AI marks a transition from AI as a tool to AI as a collaborative partner. These systems can initiate actions, negotiate with other agents, and maintain persistent understanding of their operational context. This autonomy, however, requires sophisticated communication infrastructure to ensure these agents can effectively coordinate, share information, and work together toward common objectives.
The Digital Workforce Revolution Is Here: What 442 Business Leaders Just Revealed About Agentic AI
The business world is undergoing a transformation that most organizations are still struggling to understand. While everyone talks about artificial intelligence, a new category of AI systems, agentic AI, is quietly reshaping how work gets done. These aren't just tools that require constant human direction. They're digital workers capable of autonomous action, complex reasoning, and goal-directed behavior.
After eighteen months of research, interviews with leading practitioners, and extensive analysis of implementation approaches, I'm excited to announce my upcoming book: "Building the Digital Workforce: Strategies for Agentic AI Success." More importantly, I want to share some surprising findings from our comprehensive survey of 442 business leaders that challenge everything we thought we knew about Agentic AI adoption in the enterprise.
Integration Challenges: Making Agents Work with Legacy Systems
The promise of autonomous AI agents is compelling; intelligent systems that can handle complex workflows, make decisions, and execute tasks with minimal human intervention. For most businesses though, the biggest obstacle isn't building the agents themselves, but integrating them with the systems that run their business: aging, complex legacy infrastructure that powers mission-critical operations. Many companies have invested decades in building robust systems that handle everything from customer transactions to supply chain management. These systems work, but they weren't designed for the fast, flexible data access that AI agents require. Legacy systems are often siloed, poorly documented, and surprisingly fragile despite their critical importance. The result is a painful mismatch between what agents need to function effectively and what existing enterprise infrastructure can readily provide.
The Agentic Advantage: How AI Agents Create Sustainable Competitive Moats
Business is undergoing a profound transformation from a rapidly evolving set of AI technologies. While most companies are still grappling with basic AI implementation, using large language models and generative AI for content generation or basic automation, a new paradigm has emerged that promises to reshape business competitive dynamics. This paradigm centers on agentic AI: autonomous, goal-oriented systems that don't just respond to prompts but actively pursue objectives, learn from their environment, and adapt their behavior over time.
Think of the difference between a calculator and a financial advisor. A calculator performs specific computations when asked, much like today's assistive AI tools. A financial advisor, however, maintains context about your situation, proactively identifies opportunities, adjusts strategies based on market changes, and builds expertise through accumulated experience. This is the leap from traditional AI to generative AI to agentic AI, from reactive tools to proactive digital teammates.
Selling Agentic AI Internally: Overcoming Executive and Employee Resistance
The promise of agentic AI is transformative, but internal resistance can stall progress before it begins. While the technology itself may be ready, organizations often find their greatest challenge isn't technical implementation but rather navigating the complex web of stakeholder concerns, cultural inertia, and change resistance that emerges when introducing AI agents into existing workflows.
Success in deploying agentic AI requires more than just selecting the right technology stack or use cases. It demands a sophisticated approach to change management, stakeholder engagement, and organizational psychology. This article explores how to position agentic AI initiatives within an organization, focusing on strategies that address resistance at every level, from the C-suite to the front lines.
The Skills Revolution: Preparing Your Workforce for Human-Agent Collaboration
Agentic AI, and its potential to create a digital workforce, is reshaping the nature of work itself. Unlike previous waves of automation that primarily replaced manual tasks, autonomous AI agents can collaborate with humans on complex cognitive work ranging from strategic planning and creative problem-solving to relationship management and decision-making. As companies move to the hybrid workforce, we need a complete rethinking of workforce development, moving beyond traditional reskilling approaches to embrace new models of human-agent collaboration.
For business leaders, the question is not whether AI agents will transform their workforce, but how quickly they can prepare their people. Organizations that proactively develop human-agent collaboration capabilities will gain significant competitive advantages, while those that wait risk being left behind by more agile competitors.
Salesforce Makes Major AI Data Play with $8 Billion Informatica Acquisition
Complete, high-quality data is the foundation for any successful AI initiative, and automating data management is essential for a scalable agentic AI solution. Salesforce, with its Data Cloud, provides a solid data foundation, but has gaps in data automation and governance. Informatica fills in those gaps and more. Salesforce is doubling down on its Agentic AI strategy by acquiring enterprise data management leader Informatica for approximately $8 billion. This strategic move positions Salesforce to create what CEO Marc Benioff calls "the most complete, agent-ready data platform in the industry."
Google I/O 2025: AI Takes Center Stage with Gemini 2.5 and New Search Features
Google's annual developer conference delivered a broad portfolio of AI-powered innovations this past week, reshaping how we'll interact with Google search, productivity tools, and creative applications. Here's everything you need to know about the biggest announcements from Google I/O 2025.
Google is accelerating AI integration across all its products, with Gemini 2.5 now powering enhanced search experiences, new subscription tiers offering premium AI access, and revolutionary tools for coding, creativity, and productivity. The company is clearly positioning itself as the leader in practical AI applications.
Agentforce in Slack: Transforming Workplace Productivity with AI-Powered Digital Teammates
Your digital workforce is about to become as conversational and collaborative as your human team, thanks to Agentforce's native integration with Slack. With the launch of Agentforce for employees, organizations can now deploy specialized AI agents that work alongside teams in their existing collaboration spaces, transforming how work gets done.
From Tool to Team Member: How Specialized AI Agents Are Changing Organizational Structure
As specialized AI agents gain autonomy, domain expertise, and decision-making capabilities, they're forcing a rethinking of organizational structures that have remained largely unchanged for decades. What began as basic automation tools handling repetitive tasks has evolved into sophisticated AI systems capable of advanced decision-making support and complex reasoning. This evolution is more than just technological advancement, it’s a significant shift in how we conceptualize AI's role in the workplace.
The GTM Team and the Digital Workforce: Redefining Revenue Generation
The traditional Go-To-Market (GTM) team and models have been the backbone of revenue generation for businesses of all sizes. Historically structured around distinct human-driven functions: marketing creates awareness, sales drives conversion, and customer success ensures retention. These teams operated in a largely analog world despite digital tools supporting their work. For several reasons this model isn’t working correctly anymore. Customers mostly don’t buy the way sellers sell.
It’s time for a rethinking of this paradigm. Digital transformation has radically altered GTM dynamics, introducing new channels, dissolving boundaries between functions, and creating a mountain of customer data. The emergence of a digital workforce; intelligent systems powered by AI, automation, and digital agents, has potential to accelerate the transformation and to greatly improve outcomes.
Headless AI Agents: Decoupling Interfaces from Intelligence
For businesses looking to integrate AI capabilities across their digital ecosystem, a significant challenge has been the inefficiency of building separate AI solutions for each application or channel. Enter headless AI agents, a novel approach that decouples intelligence from interfaces, allowing organizations to develop AI capabilities once and deploy them everywhere, invisibly powering decisions across the enterprise.
Advanced Retrieval Approaches for Agentic Systems: How to Select the Best Fit for Your Agents
Autonomous, goal-driven agents are capable of planning, executing actions, and learning from outcomes without continuous human oversight. As these systems grow in complexity and capability, their need for precise, context-aware information retrieval becomes increasingly critical. In the architecture of intelligent agents, retrieval systems serve as both the foundation and scaffolding, determining not just what information is accessible, but how it flows through the reasoning process to produce meaningful actions.
So retrieval isn't merely a supplementary component in agentic architectures—it forms the foundation upon which reasoning, decision-making, and action are built. An agent's ability to access, filter, and synthesize relevant information directly impacts its performance across virtually all domains. Without effective retrieval mechanisms, even the most sophisticated reasoning capabilities fall short when operating on incomplete or irrelevant data.
The Role of Semantics in Agentic AI: Enabling Context, Intent, and Understanding
Artificial intelligence systems are rapidly evolving from reactive tools designed for narrow tasks to autonomous systems capable of tackling complex problems with minimal human intervention. These "agentic" AI systems, with their capability to operate autonomously, offer great potential value for businesses, but a crucial question remains: how do these agents "understand" enough to act meaningfully in our world? The answer lies in semantics – the study of meaning in language and symbols. Semantic understanding provides the conceptual framework that AI agents need to interpret inputs, reason through tasks, and make autonomous decisions that align with human goals. This article explores how semantics enables context-awareness, intent recognition, and decision-making capabilities that define modern agentic AI systems.
AI Agent Collaboration Models: How Different Specialized Agents Can Work Together
Artificial intelligence (AI) is rapidly evolving beyond single, generalist autonomous agents toward ecosystems of specialized AI agents working in concert. While powerful generalist agents provide remarkable versatility, the future of AI systems increasingly resembles human organizations, specialized entities collaborating to tackle complex challenges that exceed any individual's capabilities.
The Process-Led Approach to Agentic AI
As organizations race to deploy increasingly autonomous AI systems, a critical question emerges: how do we harness their power while maintaining alignment with business goals? We've moved beyond simple reactive tools to autonomous agents capable of goal-driven behavior, what we now call "agentic AI." These systems can independently plan, reason, and take actions to achieve objectives with minimal human supervision. However, as these agentic systems evolve from isolated use cases to orchestrated, multi-agent workflows, their complexity increases exponentially.