Customer service is undergoing a profound transformation, driven by agentic AI. As consumers use AI in various aspects of their lives and have positive experiences, they become increasingly open to having AI agents perform tasks on their behalf, including as part of customer service.
For companies, the stakes to get agentic AI right are high. Negative experiences will devalue brands and hurt companies, while positive experiences can strengthen relationships and build.
Every day, employees do work that systems should be handling. They move information between tools, reconcile conflicting data, track down approvals, and translate requests from one team to another.
In doing so, these employees have become the integration layer holding fragmented organizations together. Their work-arounds reveal where processes break down, how decisions actually move forward, and where human judgment fills the gaps. For HR leaders preparing to introduce AI agents, this hidden coordination provides a map of how work truly gets done.
And as HR leaders move from experimenting with AI to deploying it at scale, that map offers a blueprint for where agents can create value and what human logic they must preserve. How employees respond to the resulting experience is also one of the clearest measures of whether the transformation is working.
These manual handoffs and work-arounds have become widespread. According to Workday research:
82% of employees report spending significant time coordinating or translating work between teams or business systems.
81% spend significant time moving information between systems or tools.
77% must reconcile conflicting data or reports.
70% reenter the same information in multiple systems.
These statistics demonstrate how work really gets done: who needs to approve a request, how teams resolve conflicting information, when a policy needs interpretation, what to do when the standard process does not apply. This is the operating logic AI must understand before it can take on more of this work.
Organizations must recognize that employees are not the obstacle to AI transformation. Instead, employees show leaders where transformation needs to happen, and they’re often the clearest measure of whether that transformation is working.
The True Measure of Transformation
Attitudes toward AI don’t divide neatly into enthusiasm vs. resistance. Even those who are ready to use AI may have questions about what it means for their role, their relevance, and their work. This tension makes it imperative for CHROs to address the cultural dimension of AI transformation beyond treating it as a communications footnote.
Organizations are pressuring HR leaders to help them move quickly while managing workforce uncertainty with transparency and care. This means these leaders must demonstrate how AI can improve employees’ work while communicating how humans fit into this new AI-powered system.
Leaders who attempt to solve the technology problem without addressing these cultural questions may find that adoption stalls at the edges. Without employees’ trust and a clear sense of shared purpose, even the best tools can feel imposing rather than empowering.
Luckily, the most human-centric approach to introducing AI is also the most effective one: Embed it into the systems employees use, govern it in ways people understand, and use it to augment workers’ knowledge rather than dismiss it.
Fragmentation as a Business Risk
The success of this approach depends on the strength of the foundation beneath the AI. When HR data policies and workflows are fragmented, agents can’t provide the consistent, trustworthy experiences people need. The same disconnection that creates friction for the workforce today might become an even greater source of risk with AI acting across it.
While disconnected systems affect every function, the consequences of fragmentation are especially significant for HR.
At the intersection of hiring, pay, benefits, workforce planning, performance, compliance, and employee experiences, getting an answer “almost right” means getting it wrong. A small mistake can affect paychecks, expose sensitive data, compromise hiring decisions, or create regulatory risk. Too often, an enterprise architecture problem shows up most consequentially in the function responsible for many of an organization’s highest-stakes decisions.
HR teams experience higher rates of fragmentation-related friction than the global average. Of HR professionals responding to a recent Workday survey, 66% said that changes to core systems are slow and disruptive vs. 61% globally, and 69% say different teams disagree about whose numbers are correct vs. 64% globally.
This fragmentation slows decisions, weakens the employee experience, increases compliance risk, and keeps HR reactive. It also limits what AI can do. When workforce data, organizational knowledge, and business processes are scattered, an agent can offer only a partial answer based on the partial information available to it.
Partial or inaccurate answers are not good enough for HR. Employees need to know agent recommendations are based on consistent information and that the agent respects the same permissions, policies, and controls that should govern the underlying process. Without that foundation, AI agents risk accelerating fragmentation instead of resolving it.
Turning Work-Arounds Into Governed Workflows
The best agentic AI should not become another technology for employees to learn. It should operate within the tools and workflows where work already happens.
But introducing AI does not mean discarding the human logic behind today’s work-arounds. Employees have developed practical ways to resolve conflicting information, navigate exceptions, and keep decisions moving forward when formal processes fall short. Instead of discarding these work-arounds, organizations should ensure agentic AI inherits that logic, standardizes it where appropriate, and embeds it into governed HR workflows while preserving human judgment.
For this to work, organizations must ensure that agents are trained to respect existing access controls. An agent should not be allowed to reveal information simply because it can technically retrieve it. It must operate according to the permissions of the person using it. Otherwise, a tool intended to make work easier can quickly create privacy, security, and compliance risks.
This is especially important when AI’s open-ended capabilities meet HR processes that demand predictable outcomes. AI may help a manager explore ways to organize a team or suggest development opportunities for an employee. But processes such as payroll, benefits eligibility, and compensation must follow established rules.
Lawful agents operating within the core system can ensure actions comply with established policies, permissions, and approval processes. It can also help HR teams become more proactive as compliance controls become more embedded. Instead of identifying a problem after a job requisition, compensation change, or leave decision has been submitted, an agent can surface the issue while considering the action. Human review should remain explicit for hiring, compensation, reorganizations, and other consequential workforce decisions, but agents help HR spend less time correcting avoidable problems after the fact.
This is how organizations can gain the flexibility of AI without sacrificing the reliability HR requires.
2 Million Hours a Year
Governance is essential, but employees also need to experience tangible value. That value is greatest when AI agents work inside the trusted systems and workflows that already govern HR, with the data, context, security, and permissions required to do meaningful work.
When 7-Eleven introduced agents to automate 95% of its store hiring, it reduced time to fill by 70% and saved store managers more than 2 million hours annually. Once hiring moved faster, managers got meaningful time back, and the technology delivered value within an established HR process.
With HR agents working in the background, Ace Hardware reduced hiring coordination friction across 70 countries and 5,600 locations and now schedules more than 200 interviews every day. These agents—grounded in compliance, aware of permissions, and governed from the start—can connect data, coordinate routine steps, and anticipate needs while operating within the policies, workflows, and approval structures the organization already trusts.
To fully embrace the benefits of agentic HR, leaders must communicate that new operating models drive transformation, not new tools. And they must view their people as a map, not a barrier.
Employees have already shown leaders where agents can create the greatest value. Their work-arounds reveal where systems and processes fall short. Their judgment shows what the technology must preserve. And their willingness to use AI provides a meaningful measure of whether the transformation is improving how work gets done.
The opportunity for HR leaders is to turn those signals into a more connected and responsive way of working. When agents operate within trusted HR systems, they can carry more of the administrative and coordination burden without compromising security, compliance, or accountability. That allows HR teams to focus on the judgment, strategic thinking, creativity, and human connection AI can never replace.
Dave Wachtel is General Manager for Core HR Products at Workday.
Want to learn more about how Workday equips HR leaders to put AI to work? Explore Workday.com/hr.
Business travel has resumed and most trips have multiple transportation moments: flights, ground transportation from the airport to the hotel, travel to client meetings, and more. Rather than treating travel and ground transportation as costs to manage, leading organizations are leveraging enterprise travel programs to increase financial discipline, strengthen operational resilience, and create better employee experiences. The benefits are wide ranging. Employees are more likely to conform to company policies, while spending less time on expense reports. Meanwhile, the finance and procurement teams have better visibility and control.
What do you get when you pair American blue jeans with Norwegian sailing wear and skiwear? A company with complementary strengths, value-adding synergies, and growing market reach.
It’s the outcome the leaders of Greensboro, N.C.–based Kontoor had in mind when they set out to grow the company from its foundation as the parent of Wrangler and Lee jeans into a portfolio of three iconic lifestyle, outdoor, and workwear brands. Kontoor’s leadership was looking for brands that could extend its platform into adjacent high-growth categories while preserving the company’s operational discipline and strong foothold in heritage apparel and workwear.
Oslo-based Helly Hansen combines all those attributes as a nearly 150-year-old technical brand with credibility in both outdoor and professional workwear, meaningful international scale, and a growing U.S. market footprint. In addition, the leadership teams at both companies were a strong cultural fit, which helped establish trust and alignment from the outset. Kontoor closed its $900 million acquisition on May 31, 2025. For Kontoor, the main integration consideration was to unlock value while preserving the brand, culture, products, and technical knowledge that had made Helly Hansen successful.
Retail M&A often underperforms because buyers overestimate synergies, growth, or the capacity to integrate. In apparel, excessive standardization can dilute the capabilities that made the target attractive in the first place. But Kontoor and Helly Hansen approached the integration differently, building on a strong strategic and cultural fit that helped both organizations contribute while preserving the qualities that made each business successful.
Helly Hansen became the largest driver of Kontoor’s 2025 growth, helping lift its revenue 21% to a record $3.2 billion. The deal also improved gross margins and generated $100 million in cash within the first seven months of ownership. Investors noticed: Kontoor’s stock has risen 35% since its May 2025 closing, outperforming the S&P 500 by up to 10 percentage points. The deal offers practical lessons for acquirers seeking to drive growth without sacrificing the strengths of the assets they are acquiring.
Making a Strong Fit Stronger
Kontoor leaders set the stage by choosing an acquisition target that matched their needs on multiple levels.
“We were clear on where we wanted to take the portfolio,” said Joe Alkire, Kontoor’s president. “Outdoor, workwear, and lifestyle were attractive spaces for us, and Helly Hansen was one of the few assets that brought together these areas of strategic interest. We also saw clear opportunities to add value through operational discipline, supply chain capabilities, and financial rigor while providing the brand with the investment, resources, and long-term support needed to unlock its next phase of growth.”
After the announcement, leaders clearly communicated the strategy and deal value to both teams. Kontoor brought its scale, sourcing capabilities, operating discipline, and financial rigor to support Helly Hansen’s next phase of growth.
The integration also benefited from a shared willingness to engage deeply to make the deal successful. “When we met with the leadership team at Kontoor, it quickly became clear they were the partner we needed to support our continued growth,” said Børre Hegbom, SVP global head of Helly Hansen. “They understood the business, listened carefully to our ambitions, and recognized that continued investment would be essential to unlocking long-term value. We were strongly aligned on the path forward from the beginning.”
This alignment became a defining characteristic of the integration, as leaders from both organizations invested the time and energy required to preserve Helly Hansen’s strengths while accelerating growth under new ownership.
Discipline, Governance, and Detail
To achieve the expected deal value, the companies needed disciplined execution. Kontoor and Helly Hansen established a structured integration program that combined rigorous planning, governance, and synergy management to keep leaders focused on performance and value creation.
Kontoor and Helly Hansen leaders consistently demonstrated a solutions-oriented mindset, viewing challenges as opportunities to strengthen execution and collaboration. Their ability to make timely adjustments while maintaining transparency and accountability and staying focused on the deal thesis helped sustain momentum and deliver strong results.
Kontoor’s steering committee helped resolve integration challenges as they arose. Kontoor and Helly Hansen had different systems, operating philosophies, and expectations for progress. As friction increased, leaders used the governance process to identify risks and escalate decisions. Integration leaders anticipated risks before they slowed momentum through bridging differences in communication styles and expectations.
A Longer Journey
As the integration foundation stabilized, Kontoor leadership focused more fully on growth initiatives, reinforcing Helly Hansen’s role as a strategic priority and key growth driver. The deal preserved Helly Hansen’s strengths while giving it more room to grow.
In Kontoor, Helly Hansen gained a strategic parent with a long-term interest in building its brand and investing in its growth. The balanced and disciplined approach to the integration helped generate more than $25 million in recurring synergies, putting the company on a path toward its targeted $40 million run-rate synergy goal by the end of 2027.
The deal demonstrates multiple ingredients for integration success:
Have a clear value thesis and communicate it consistently.
Set up strong governance with senior leaders who are visible, invested, and accountable.
Establish an integration management office with experienced leaders empowered to execute.
Maintain flexibility to adjust course as circumstances evolve.
Deliver on transaction value drivers by realizing and tracking synergies cross-functionally.
Invest time in culture- and relationship-building to establish trust across both organizations.
Communicate early, frequently, and transparently to build alignment, maintain momentum, and reduce uncertainty throughout the integration journey.
Kontoor viewed the deal closing not as a finish line but as the beginning of a longer journey. One Kontoor executive who helped build the deal model relocated to Norway, taking ownership of day-to-day execution. That presence helped sustain momentum after closing, supported by a lean steering committee empowered to make decisions quickly.
“Being present in Norway mattered,” said Kontoor’s Alkire. “It sent a clear message to the Helly Hansen team that this business was strategically important, that we were there to listen, and that we were committed to building the future together.”
Kontoor further reinforced that commitment with the appointment of Børre Hegbom, a Helly Hansen veteran of more than two decades, as brand president, reflecting the parent’s confidence in the new team and its commitment to preserving the company’s culture.
In early June 2026, Kontoor leaders went to Norway to celebrate the first anniversary of the deal with Helly Hansen employees at the company’s summer picnic, emphasizing that Kontoor viewed the acquisition as an ongoing investment in the combined company’s future.
Elizabeth Kaskeis the EY-Parthenon Global and Americas Mergers and Acquisitions Leader, Ernst & Young LLP.
Shelley Gupta is an Executive Director in the EY-Parthenon Transaction Strategy & Execution team, Ernst & Young LLP.
Learn how EY-Parthenon M&A advisory services can help organizations refine their growth strategies, perform deal sourcing, conduct diligence and valuation, and achieve greater synergies during M&A integration.
The views reflected in this article are the views of the author(s) and do not necessarily reflect the views of Ernst & Young LLP or other members of the global EY organization.
By Eugene Amigud, Chief Product & Innovation Officer, Infios
Traditionally, supply chain planning has been based on stable assumptions about demand, lead times, and costs. But despite heavy investment in AI forecasting, demand sensing, and predictive analytics, unplanned supply chain incidents still cost businesses $184 billion annually, according to the J.S. Held Global Risk Report 2025.
Many leaders assume the problem stems from flawed data and inaccurate models. But recent risk analyses show disruptions are the norm, not the exception.
The first major challenge to the forecasting framework was COVID-19, which wreaked havoc with the global supply chain for everything from flour to semiconductors. In 2025, the sudden implementation of tariffs by the U.S. upended the board yet again. In 2026, the closing of the Strait of Hormuz due to hostilities between the U.S. and Iran has disrupted the transport of multiple commodities, hurting many sectors in global trade, including energy, agriculture, and manufacturing.
In this unpredictable environment, even the most sophisticated forecasting models fall short. Forecasts depend on patterns. Geopolitical conflict, climate events, regulatory change, and cyber attacks have little historical precedent to model against, which is exactly what makes them so hard to forecast.
In addition, supply chains are now subject to several layers of risk at the same time. One disruption is enough to test a system, but these events often interact, creating a cascade of failures that is difficult, if not impossible, to recover from.
Most companies are running on technology infrastructure that was never designed for this degree of chaos. Their operating models were built for linear plans instead of nonlinear shocks. And even the most sophisticated forecasting model produces insights no one can act on fast enough: Decision-making is slow, execution is fragmented, and the gap between knowing that something changed and doing something about it is too wide.
Meanwhile, changes continue to unfold at all times, keeping teams stuck in reactive mode as they try to keep up. And as the burden of making decisions and synchronizing action falls on overwhelmed operators, risks and costs increase. Nearly 60% of supply chain leaders say manual workflows and interventions are the biggest source of inefficiency in their operations.
Their mistake is to treat these disruptions as surprises instead of assuming they will happen. That shift from prediction-led planning to execution-led adaptability is the real opportunity. This mindset moves resilience to the center of any solution, asking not “What will happen?” but “How fast can we respond when it does?”
A New Model for Performance
With traditional forecasting, by the time businesses catch up with major disruptions, inventory has already been delayed, orders have been rerouted, and customer commitments are at risk. Supply chain leaders are clear on the better strategy: 79% say the biggest advantage comes from how quickly and dynamically they can adjust their execution and plans. The key performance metric today is not forecast accuracy but time to adapt: measuring how long it takes your company to detect disruption, respond to it, and recover from it.
Managing this resilience means moving to a new operating model—intelligent supply chain execution—that supports:
Real-time data integration and shared operational context instead of static forecasts. Incorporating information about what is actually happening, instead of relying on point-in-time planning or historical patterns, creates a continuously updated planning environment that enables more-informed decisions.
AI decision making at the execution layer. Instead of focusing AI solely on forecasting or analytics, companies should embed it directly into operational workflows. Beyond the ability to anticipate disruptions, this enables intelligence that understands the full impact of sudden shocks across the supply chain, and the capability to take autonomous action across systems, with guidance and oversight from operational teams.
Cross-functional workflows that can adjust while conditions are in motion. Companies need technology that connects and coordinates execution across orders, warehousing, and transportation as one system. Businesses gain the ability to respond to disruptions holistically, eliminating the need for inefficient manual interventions and disconnected, ineffective problem-solving efforts.
Intelligent supply chain execution depends on a foundation of governance that holds control while increasing speed—with policy-driven frameworks and guardrails for AI autonomy.
From Forecasts to Data-Driven Action
When disruption is constant, forecasting, no matter how advanced, and traditional supply chains that rely on fragmented systems operating independently cannot keep up. Intelligent supply chain execution supports this shift by turning disconnected components into a coordinated system of action.
Forecasts will always be useful, but they are no longer enough on their own to keep an organization online. As disruption remains a permanent feature of the global economy, organizations need systems that can continuously sense, decide, act, and learn from each disruption, in real time.
Businesses that incorporate this model won’t just weather uncertainty; they’ll be poised to thrive in an increasingly unpredictable environment. And only by designing for constant surprise can they close that $184 billion gap.
Infios AI connects orders, warehousing, and transportation, turning fragmented signals into coordinated action—sensing disruption as it happens, deciding the right response and executing it in real time, and then learning from every outcome to improve the next one. That intelligence operates inside existing workflows, with the governance and guardrails leaders need to move from manual firefighting to continuous controlled execution.
Ready to move from prediction to action? Learn more about how Infios can build execution-led adaptability into your supply chain.
For the better part of two decades, the story of industrial disruption had a familiar shape. Nimble entrants ran circles around the incumbents, unburdened by legacy portfolios, tangled supply chains, or installed bases measured in decades. The advantage belonged to whoever carried the least.
AI is inverting the story. The same weight that slowed large manufacturers—the sprawling product histories, the regulatory scar tissue, the decades of hard-won engineering intellectual property (IP)—is precisely the raw material that makes AI valuable.
In discrete manufacturing, the winners of the AI decade will not be the startups with the cleanest slate. They will be the Goliaths who finally turn their bulk into leverage.
The Asset No One Can Buy
Here is the uncomfortable truth for every well-funded newcomer: The one thing they cannot acquire is history. A general AI model can learn everything on the public internet and still know nothing about how your product is designed, built, changed, and certified. That knowledge lives in the engineering systems established manufacturers have run for years—the requirements, designs, bills of materials, and change history that constitute the essential IP of a business, along with the rules governing who may see it and how it gets approved.
That is exactly the context AI needs to reason well: what it is working with, who is allowed to use it, and how similar problems were solved before. Strip that away and even a capable model is reduced to guessing.
Owning It Isn’t the Same Thing as Wielding It
There is no partial credit here: Either a manufacturer’s product data is connected, governed, and curated or it isn’t. And for most, it isn’t. When a manufacturer’s product data sits fragmented across systems that don’t talk, modeled inconsistently across functions, locked in PDFs and the memories of engineers who may be five years from retirement, that is not a company with a slightly weaker AI position. It is a company with no AI position at all, pointing retrieval-augmented generation at a pile of disconnected documents and calling the output intelligence.
Take a routine scenario: A supplier discontinues a component and a replacement has to be qualified.
In one company, an engineer spends weeks tracing where that part lives: which assemblies, which configurations, which customer variants, which open orders, and which regulatory filings reference it. In another, an AI agent reaching the live product record through an open standard like the Model Context Protocol returns the full ripple in minutes, flags the two variants where the substitution breaks a compliance requirement, and surfaces a part already qualified for the same purpose.
Same change, same models, same ambition. But one company absorbs the cost quietly; the other never pays it.
And that is one scenario among dozens that play out every week, such as:
A field failure comes in from a customer. One company reasons across service records, the as-built configuration, and the change history to find the root cause in an afternoon; the other convenes a task force and waits.
A new program goes out to bid. One company knows in hours which existing designs, parts, and lessons it can reuse; the other quotes as if starting from scratch and either overbids or wins work it will lose money on.
An auditor asks for the complete design and approval trail on a shipped product. One company produces it on demand; the other sends people digging through inboxes and shared drives for a week.
A design engineer starts a new part. One company’s AI surfaces the three interchangeable parts already in the catalog; the other creates a fourth and pays to carry it for its entire lifecycle.
None of these is a dramatic, board-level crisis. Each is a small, ordinary moment of product work. And that is the point. Here, the math tips toward the giant: the larger the portfolio, the deeper the installed base; the longer the history, the more of these moments occur every day and the more a connected foundation compounds across them. The same scale that was a liability for 20 years becomes the multiplier. On disconnected data, that scale only multiplies the drag. Those returns don’t come on a sliding scale. They belong entirely to the manufacturers whose data is ready to be reasoned over, and not at all to the ones whose data isn’t—a divide decided long before the first model is deployed.
A Strategic Decision, Not a Software Purchase
Closing the gap is less about buying new software than about making a decision: to own and govern product data as an enterprise asset, not a by-product of engineering. Three things unlock it:
A product data foundation, anchored in the bill of materials, so AI reasons over an authoritative connected model rather than fragments that disagree.
Governed AI, where the same access rules that protect the IP constrain the model to limit what each user may see, keeping the model secure and auditable rather than a novel way to leak secrets.
AI in the workflow, running within the procedures teams already use, from engineering change to configuration to manufacturing planning, rather than hovering above them as a chatbot.
The executives who win this decade won’t be those who ran the most AI experiments. They’ll be those who asked the harder question early: Are we turning decades of engineering IP into a structural advantage or letting it depreciate in systems that don’t connect, align, or scale?
For 20 years, size was a liability. AI is making size the ultimate advantage—but only for the manufacturers who build the foundation to wield it.
The advantage is already theirs. The only question is whether Goliath picks up the stone—before David convinces him he’s too big to fight.
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.
