The big picture: Many companies run AI pilots but struggle to capture value. Growth leaders are rewiring their commercial processes with agentic AI to help sellers strengthen customer relationships and drive real change.
Why it matters: Pilots alone do not guarantee returns. Leaders must redesign workflows and seller roles around AI agents to unlock commercial impact and create competitive advantage.
The big picture: AI is transforming the architecture, engineering, and construction sector. Companies that adapt quickly by reimagining workflows, improving data use, and automating work sites will have competitive advantage.
Why it matters: Enterprise leaders in AEC need to move now to stay competitive. Waiting means risking market position to firms that successfully integrate AI into core operations.
The big picture: Software is becoming central to how medtech organizations create value. A new approach to quality assurance in software-as-a-medical-device development, using agentic AI, helps organizations capture that value.
Why it matters: Medtech leaders should evaluate how agentic AI can streamline compliance and quality processes. Better QA approaches can accelerate time to market while maintaining regulatory standards.
The big picture: Logistics company C.H. Robinson deployed AI agents and saw a 45% productivity gain. CEO Dave Bozeman has found measurable ROI from the company's AI investments.
Why it matters: This is a concrete example of AI delivering financial impact at scale in a traditional industry. Enterprise leaders can learn how a real company turned AI deployment into tangible business results.
The big picture: As AI agents make real-time decisions, leading companies are moving away from predefined customer journeys. They are redesigning toward dynamic, cross-channel orchestration that responds to moment-to-moment interactions.
Why it matters: Static, pre-planned customer experiences will become outdated as AI agents operate continuously. Companies that build adaptive orchestration will deliver faster, more relevant interactions and competitive advantage.
The big picture: OpenAI CEO Sam Altman cautioned that the company's new GPT-5.6 Sol model may face performance issues soon, citing rapid growth straining inference capacity. Anthropic and SpaceX AI are also launching flagship models, intensifying competition for computing resources.
Why it matters: Even leading AI companies struggle to scale infrastructure fast enough to meet demand. Enterprise leaders should recognize that compute constraints and competition for resources will shape the pace and cost of AI deployment.
The big picture: McKinsey's Brooke Weddle examines the practical methods companies use to expand AI programs. The focus is on real-world approaches that work at scale.
Why it matters: Enterprise leaders trying to grow AI initiatives need proven playbooks. Understanding what actually succeeds in scaling helps avoid common pitfalls and accelerates results.
The big picture: Companies are investing heavily in AI agents while scrambling to control costs. The risk is optimizing for spending without understanding what business outcomes justify the investment.
Why it matters: Enterprise leaders often focus on cost reduction as the primary metric for AI projects. But missing the actual value creation can lead to expensive systems that don't move the business forward.
The big picture: Companies are using generative AI and large language models to access and analyze their internal content about customers and markets. This hybrid approach uses retrieval-augmented generation to combine AI capabilities with existing knowledge.
Why it matters: Customer-oriented companies gain new ways to extract insight from data they already own. Better customer understanding drives competitive advantage and informs strategic decisions.
The big picture: McKinsey's Alex Wolkomir outlines how housing companies can win by using AI to improve customer experiences, redesign workflows, and build trust. These changes span the real estate ecosystem.
Why it matters: Real estate leaders face pressure to modernize operations and customer relationships. AI offers concrete paths to competitive advantage through experience, efficiency, and stakeholder confidence.
The big picture: Columbia Business School professors draw lessons from VAR (video assistant referee) in sports. The case illustrates how human judgment and AI systems interact in practice.
Why it matters: Enterprise leaders often talk about human-in-the-loop AI without clarity on what that really means. Real-world examples from sports show where humans and machines must stay connected.
The big picture: Frontier AI users experience the technology as transformative, capable of building companies and writing software. Most Americans experience it as incremental improvement in search, email, and ambient utility.
Why it matters: Enterprise AI leaders need to account for the fact that AI's economic value and workforce impact are unevenly distributed. This divide will shape how different constituencies view AI investment and deployment in organizations.
The big picture: Marketing leaders gathered at Cannes Lions to discuss how AI is changing the CMO role. The emerging consensus is that AI demands marketing leaders think and operate at a CEO level.
Why it matters: Enterprise AI leaders should recognize that marketing functions are being repositioned as strategic business drivers rather than execution teams. This shift signals how AI is pushing other departments to claim broader organizational authority.
The big picture: The Associated Press is navigating AI adoption while confronting questions about business structure and content quality. The CEO emphasizes that editorial independence and trusted content remain central to the strategy.
Why it matters: As enterprises integrate AI into customer-facing operations, they face the same tension. Automation and efficiency gains only stick if they preserve the trust and judgment customers rely on.
The big picture: A four-year study at a large U.S. public university introduced generative AI tools to leaders and staff in 2026. Despite the rollout, staffing levels and work hours remained stable across the period studied.
Why it matters: Organizations often assume GenAI will reduce headcount or hours, but this research shows the tools may deliver value in other ways. Enterprise leaders should rethink how they measure AI success beyond simple labor reduction.
The big picture: Customers are approximately three times more likely to use third-party GenAI tools than company-provided chatbots when dealing with customer service issues.
Why it matters: Building proprietary chatbots may not be the winning strategy. Leaders should consider how third-party tools shape customer experience and where proprietary solutions add real value.
The big picture: Customers can now run analytical and AI workloads across SAP and non-SAP data without moving it. The deal feeds SAP's Business Data Cloud and agentic roadmap.
Why it matters: Data foundation consolidation is agent readiness. The vendors know where transformations stall.
The big picture: Qodo's "Compliance as Code" framework automates enterprise AI compliance through pull request checks. It targets the data-privacy and security gaps that manual reviews miss at scale.
Why it matters: Governance failure is what keeps AI stuck in experimentation. Automating compliance turns trust into a build step instead of a manual bottleneck.
The big picture: Healthcare faces a productivity crisis that more staff and more technology separately cannot solve. The solution requires human workers and AI systems working together in integrated workflows.
Why it matters: Hospitals and health systems wasting resources on purely technical or purely staffing solutions will fail. Leaders must design workflows where AI and humans complement each other's strengths.
The big picture: More than 70% of mainframe exit projects starting in 2026 will fail because organizations are overestimating what generative AI tooling can do. Companies expect GenAI to automate legacy system replacement more completely than it can.
Why it matters: Mainframe exits are large, expensive bets. Leaders planning these projects must set realistic expectations for GenAI capabilities or risk failed migrations and wasted investment.