Europe’s household air-conditioning market has remained stubbornly small despite increasingly severe heatwaves. The window units prevalent in much of the word are typically incompatible with
Every summer, Europe gets hotter. Heatwaves arrive earlier, last longer, and grow more intense. Keeping homes cool is increasingly becoming a necessity. The U.N. World Health Organization estimates that 200,000 people have died from heat-related illness across the Europe over the past four years alone.
Generative AI initiatives often stall not because of the board, vendors, or training, but because middle managers determine how AI is translated into day-to-day work. There are five distinct
The most common failure point in generative AI is not the boardroom, the vendor shortlist, or the training portal. It is the layer of managers who must turn executive ambition into everyday behavior. That is why Gen AI adoption keeps stalling even in organizations where senior leaders have approved the budget, picked the platform, and announced the mandate. Middle managers decide whether AI becomes a workflow, a side experiment, a compliance headache, or a quiet casualty of organizational avoidance.
In the August 31 edition of The Insider newsletter, managing editor Gretchen Gavett shares an argument for why organizational transformation should be a learning journey, a formula to gauge your
Most organizational transformations kick off with a detailed roadmap and a fixed destination due the complexity involved and coordination required. But you may be more likely to succeed if you admit you don’t entirely know where you’re headed.
How do you scale innovation in a fragmented system you don’t fully control? That is the challenge Highland Electric Fleets faced as it worked to replace diesel school buses with electric vehicles across the United States. While Highland provided financing, infrastructure, and fleet operations, success depended on external partners, including manufacturers, utilities, and districts. Delays and disruptions forced the company to stay nimble and deliver under pressure.
In this episode from 2025, Harvard Business School professor Rosabeth Moss Kanter and Highland Electric founder and CEO Duncan McIntyre join host Brian Kenny to discuss the Harvard Business School case, “‘The Wheels on the Bus’ Go Electric: Highland Electric Fleets and Partners,” and what it takes to scale a climate solution while sustaining momentum and coordinating across public and private sectors.
How much does a leader actually matter to an organization’s success? We tend to credit CEOs, founders, and other powerful figures with shaping the institutions they lead, but historian Jared Diamond argues that we often underestimate the role of timing, circumstance, industry, and plain luck. Diamond explains his “Hamlet test,” which asks whether someone else could have achieved the same outcome or whether the leader was uniquely qualified to do it, and how it applies to many business leaders. He also explains why some leaders are able to seize a fleeting moment of opportunity, why founders can have an outsized influence on institutions, and why boards may overestimate the impact of replacing a struggling CEO. Diamond is author of Prophets, Profits, Coaches, and Kings: (When) Do Leaders Matter?
Over the past decade, we’ve seen big changes happening for enterprise artificial intelligence (AI). Organizations have invested in chatbots and copilots designed to help workers with productivity, but these ambitions have moved from assistance to autonomy. Leaders now want to deploy systems that can plan and launch complex workflows all on their own.
Today, organizations are moving toward an elastic enterprise model, designed to reshape tasks, systems, and teams in real time. In an elastic enterprise, business processes become fluid, bringing together services, data, and human expertise to resolve business outcomes.
Scaling Agentic Capacity with Extended Reasoning
One of the biggest drivers for this change is the evolution of frontier AI models. Early AI operated on a one-step, immediate-response model. But today’s models use extended reasoning, giving them the ability to plan, self-correct, and evaluate their work before final output.
Models allowed to think longer across multiple scenarios can simulate different paths and refine their strategies. This extended reasoning lets models automate processes without constant human intervention.
Establishing an AI Delegation Framework
As agents take on deep-reasoning tasks, the move from automation to delegation closely mirrors how we interact with teammates every day.
To scale this safely, leaders of elastic enterprises focus on key decisions and protocols:
Transfer of authority and boundaries: Define the authority, limits, and system permissions to identify when the agent needs to escalate decisions to a human.
Accountability and verifiability: Be deliberate about verifying data. Automate tasks like code generation but require reputation scores or human-in-the-loop reviews for sensitive tasks like client communications.
Dynamic cognitive friction: If instruction is ambiguous or risky, the agent must pause for human review.
Designing a Secure Agentic Architecture
Elastic enterprises use architectures that align throughout the organization. Before deploying workflows, companies establish capability models and maturity assessments across infrastructure, data, applications, security, and operations.
These support agentic work at scale by identifying core areas that may be in need of development, including:
Agent building: Design and architecture for individual AI agents and complex multi-agent systems.
AgentOps: Framework for deploying, monitoring, and optimizing agent performance.
Data readiness: Agents connect securely to enterprise data assets.
Security and governance: Guardrails that identify and control potential AI system risks before they escalate.
Clearing Up Disconnected Data
Organizations that adopt an elastic enterprise model often must reengineer their workflows and infrastructures. Data simplification becomes a top priority as well, providing resources for teams and clean data for large language models.
This groundwork helps teams drive transformation through three strategic pathways:
Plug-and-play integration: Connects agents directly to high-quality data architecture.
Pro-code necessity: Uses specialized agents to act as the bridge for modern agentic workflows.
Practicing Token Economics
When introducing AI agents, enterprises must develop a new approach to cloud FinOps that focuses on cost tracking and value governance. Because agents use token-based billing, organizations need a way to track total cost of ownership and value generated.
Working toward minimizing token waste and overall costs, elastic enterprises use specific methods for streamlining and refinement:
Speculative routing: Directing tasks and reasoning to fast models like Gemini Flash to handle high-frequency work at minimal cost.
Batching: Separating time-sensitive tasks from latency-tolerant workloads to use asynchronous batch application programming interfaces.
Guardian validation: Deploying specialized, low-cost agents to monitor outputs that break if they detect background looping or agent sprawl.
Inference pruning: Using research on deep-thinking tokens to prevent inefficient reasoning paths early on, matching premium model accuracy at half the token cost.
Adapting Elastic Talent and Human-over-the-Loop Collaboration
Agentic AI introduces a new category of digital talent into the workforce that requires new human roles and organizational culture. The relationship between workers and AI agents is a collaborative, elastic spectrum that evolves based on task, risk, and agent reliability.
In an elastic enterprise, human workers act as managers of digital talent through governance and boundaries. When a new agent is deployed, teams maintain human-in-the-loop oversight. As the agent builds trust and reputation score over many successful tasks, managers may scale back to human-over-the-loop involvement, monitoring as needed but focusing on actionable value instead.
The Path Forward
Moving beyond legacy architectures to address the opportunities of AI agents is what makes elastic enterprises unique. More than just a change in technology, this transition is a shift in mindset and culture, giving teams a way to build an organization that can learn and lead in real time. With the elastic enterprise model, leaders can now readily prioritize agentic AI for their organization, turning ambition into autonomy.
Carlo Giovine is the leader of Delta, Google Cloud Consulting’s Forward Deployment Engineers team.
Vasu Gupta is the leader of Google Cloud Consulting’s Architecture and Industries team.
With expertise from Google’s own at-scale AI deployments and product knowledge, Google Cloud Consulting acts as an extension of a customer’s team to advise and implement agentic use cases and platforms. Interested in your own agentic transformation toward an elastic organization? Google Cloud Consulting can assemble the right team for your specific challenges, combining Google’s expertise with the specialized skills of our trusted partners. Contact Google Cloud Consulting to learn how our personalized offerings can guide your AI transformation.
HBR Executive delivers trusted insights and actionable strategies to guide leaders through consequential decisions. Practical tools, and forward-looking perspectives cut through the noise to equip you to navigate uncertainty, stay ahead of emerging trends, and plan for a stable future.
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