The Orchestration Imperative: Navigating Your AI Change Management Journey
Exponential improvements in generative and agentic AI have sparked a corporate investment frenzy. Facing intense pressure from their investors, boards, and C-suites, companies are rushing to implement AI solutions to modernize operations, drastically reduce costs, and build capabilities that drive compounding growth.
Virtually all are coming to a stark realization: Scaling AI is not a technology problem—it is a massive change management challenge.
Building leadership alignment around the right system of change for your organization’s unique culture is the only way to turn scattered, frustrating workflows into a synchronized engine for growth.
The Burning Platform
This dynamic recently played out at our client, a 3,500-person consumer entertainment company that is part of a multi-divisional global conglomerate. This organization is renowned for an empathetic culture that underlies a multi-decade record of consistent growth. It is mission-driven, passionate, deeply collaborative, technology-savvy, and highly customer-obsessed. Because of its digital-native roots, the company is accustomed to embracing technological change.
Yet, despite these profound cultural strengths, its ambitious internal AI program quickly stalled, stoking deep anxiety among employees and slowing progress toward its strategic and operational goals.
In facing this roadblock, the company has company – lots of it. Few technologies in history have fomented the kind of broad-scale enthusiasm as generative and agentic AI. Yet, despite the billions of dollars pouring into these systems and tools, enterprise outcomes are radically diverging. A recent MIT study highlighted that roughly 95% of generative AI pilots fail to deliver any measurable P&L impact. Furthermore, Gartner predicts that 40% of agentic projects will fail by 2027—not because the technology lacks capability, but because organizations are blindly automating broken processes instead of fundamentally redesigning their operations.
This seemingly universal lack of progress illustrates a fundamental corporate misunderstanding that has played out in past technology adoption scenarios, such as the Enterprise Resource Planning and Customer Relationship Management waves of the 1980s through the early 2000s – sets of over-hyped, over-promised, breathtakingly expensive technology implementations that eventually created value, but only after years of missteps, pain, and misallocated investment. The learnings from these misadventures should be fresh as companies take up AI implementation now.
Many firms view AI programs strictly as a software deployment. But in our experience, AI’s greatest power lies in its ability to empower individuals and reduce the complexity required for teams to orchestrate complex, multi-functional workflows.
The False Start
Few enterprise functions are as ripe for beneficial change from AI as marketing. For more than a century, marketing has been organized around a linear assembly line. Someone writes the brief. Someone else writes the copy. Someone else designs the layout. Someone else plans the media. And finally, someone else measures it. In the words of the Northwestern University marketing professor Jim Lecinski, AI shifts the organization’s center of gravity entirely away from this manual “authorship” toward “orchestration,” where humans build, manage, and coach dynamic systems that adapt in real-time.
The necessary transition from manual authorship to AI orchestration across all digital experiences demands leadership alignment and sustained commitment on a multi-year change journey.
Our consumer-entertainment client initiated its AI-powered transformation with highly aggressive, top-down goals. The core objective was to automate 70 percent of repeatable marketing tasks. This mandate, championed by the CEO and his leadership, was designed to enable a massive reduction in the time spent on advertising and marketing content development and distribution, which would free up hundreds of thousands of hours in efficiency savings that could then be invested in geographic and product expansion.
However, while the embrace of AI aligned with the company’s growth ambitions, the execution strained the organization’s culture. This friction manifested in four distinct mismatches:
The Mission vs. the Mandate: The company set its KPIs entirely around cost reduction and strict time-saving milestones, which failed to reflect its behavioral bedrock, particularly its customer-centricity and its mission-driven culture. This top-down mandate triggered acute job anxiety across the marketing organization. It also stoked worries surrounding an impending, hard deadline for contractor reductions before the promise of the new AI tools was proven. Because leaders’ communications reinforced time savings over reimagining the work, the transformation felt like a direct threat to employees’ livelihoods rather than an empowerment tool.
Unshared OKRs: Even if employees wanted to adopt the tools, they were structurally disincentivized to do so. A “cascading gap” emerged in the company’s objectives and key results (OKRs). The marketing technology team was measured strictly on automation metrics and building the AI tools. Meanwhile, the frontline marketing teams were measured solely on campaign delivery and revenue generation. This misalignment undermined tool adoption. Because learning a new AI platform takes time, marketers quickly realized that engaging with the new software jeopardized their immediate campaign deadlines. Naturally, they reverted to the manual, legacy processes with which they were comfortable to assure they hit their revenue targets.
The “Problem of Plenty”: Beyond difficult-to-navigate interface designs, employees suffered from a wealth of apparent riches: The organization unleashed a flood of disparate AI tools—generative assistants, proprietary orchestration hubs, and third-party vendor platforms—simultaneously. Employees were overwhelmed by the sheer volume of available technologies. Without clear, role-specific guidance, they abandoned the tools the moment they hit a technical roadblock.
Better vs. Faster Perhaps most damaging was the clash with the organization’s foundational identity. The company possessed a deep-rooted commitment to “customer obsession”—a drive to deliver only the highest quality, “better” experiences. This created tension with executive mandates for “faster” and “cheaper” automation. When early versions of AI translation tools produced output that local market teams considered poor quality, marketers simply stopped using them, fearful of damaging the brand.
Recognizing the deepening disconnect between the technology being built and the marketers who were to use it, leadership made a crucial pivot. First, they moved the engineering and product teams directly under the marketing organization’s aegis to create better alignment. Second, they brought in an external change management partner – JourneySpark (we and our colleagues Karen Premo Napolitano and Zack Yoelson-Angeline) - to co-create a sustainable playbook that the teams could collectively own and follow.
Sparking Collective Energy
The structural integration of technology and marketing was a positive step toward reducing friction. However, top-down mandates had already triggered psychological resistance, an effect known in change management circles as Reactance—the inherent pushback that occurs when employees feel their freedom and agency are threatened. To sustain and accelerate momentum, change management needed to be highly collaborative and co-created directly with the teams using the AI tools.
The playbook they needed required more than laying out a technical roadmap and blasting out executive emails. It necessitated changing the actual daily work, evolving the organization’s approach to peer-led training, reinforcing new collaborative behavioral habits, and aligning incentives to close the cascading OKR gap.
To achieve this, we designed a multi-phased discovery and alignment program. We began with dozens of in-depth 1:1 interviews across every department in marketing—from creative and marketing operations to product and media procurement—to bypass superficial optimism and unearth the unvarnished truth regarding user anxiety and workflow bottlenecks.
Next, we facilitated targeted roundtable discussions to validate these systemic themes and hone in on the right solutions that fit within the existing culture. Finally, we brought cross-functional practitioners together for collaborative workshops to serve as a catalyst for alignment and co-creation, while continuing to boost the energy in the team.
By ensuring that the end-users had their “fingerprints” on the resulting strategy, we replaced fearful resistance with collective ownership. People embrace what they help create.
Building a Movement
Successfully scaling artificial intelligence requires a synchronized, multidimensional system of change that addresses strategy, metrics, and human psychology simultaneously. Based on the rigorous discovery process, we developed a tailored system of change for the client organized around six interconnected plays—what we call The Six Gears of Change.
We were very conscious that visualizing a change process as a series of gears risked communicating, if only subtly, that we were reducing human emotions and activity to a set of mechanical processes. But we believed that it would help teams and their leaders see the flywheel effect by which the different activities, meshing together, could create outcomes that would transcend the abilities of any single team or any discrete set of actions.
The six gears on which we focused the team’s co-creation efforts were:
Balance Top-Down & Citizen-Led Innovation: The first gear calibrates the sources of innovation. Organizations frequently oscillate between central mar-tech development and ungoverned “citizen-led” experimentation. To strike the right balance, this gear establishes specific teams to clarify the central AI roadmap while creating approved, safe “sandboxes” to harness grassroots, employee-led AI experimentation.
Crucially, to cure tool fatigue and rebuild broken trust among the marketing staff, this gear institutes a strict “Proof of Value” pilot gate. Before any AI tool becomes mandatory, it must first be piloted by a dedicated group of power users. Only after it has proven concrete time savings and operational stability in a controlled setting is it rolled out broadly.
Measure Value Realization: The second gear solves the gap in shared objectives. It launches a dedicated team to build actionable KPIs that measure true value realization against the broader strategic vision, moving away from the sole, anxiety-inducing metric of “hours saved.” To ensure that adoption actually happens, this gear mandates the cascading of shared OKRs across both the technology builders and the marketing executors. Marketing leaders are explicitly evaluated and rewarded for dedicating team bandwidth to AI training and workflow redesign, aligning incentives so that taking the time to learn a new tool is celebrated rather than penalized.
Sustain Leadership Collaboration: Transformation cannot happen if the executive team is not moving in sync. The third gear requires the leadership team to actively commit to modeling prioritized cultural behaviors. They must actively balance the tension between moving fast and maintaining high-quality brand standards, demonstrating to the frontline that “customer obsession” will not be sacrificed at the altar of automation.
Communicate the Steps on the Journey: Communication is the lubricant that allows the other gears to turn. The fourth gear shifts the organization away from top-down, “spray and pray” endpoint mandates. Instead, it establishes regular, snackable storytelling that clearly translates the overarching AI vision into immediate, digestible 3-month milestones. By providing hyper-transparent, interactive roadmap visibility, marketing leaders can accurately assess their operational risk and adjust their strategies based on real progress, not evanescent promises.
Reimagine the Work: To quote our friend, the serial marketing technology entrepreneur and founder Tom Chavez, “you cannot pave the cow path”: Automating a bad process only creates faster bottlenecks. The fifth gear deploys dedicated teams to fundamentally redefine job descriptions and workflows. This is the mechanical process of formally shifting marketers’ roles from manual content “authorship” to AI “orchestration.” This gear launches role-based upskilling. Generic technology training often fails. Instead, organizations must teach employees exactly how to delegate specific daily tasks to their new AI counterparts, converting profound job anxiety into immediate empowerment.
Make it Engaging: The final gear focuses entirely on the human element. It deploys a team specifically tasked with lowering AI anxiety through gamification, peer-to-peer awards, and celebratory milestones. This taps into the innate human drive to bond and learn. By establishing internal recognition programs (like an “AI in Action” showcase) and a talent marketplace where freed-up capacity leads to exciting new stretch projects rather than layoffs, this gear shifts the internal narrative from “job elimination” to “career expansion”.
Your Agentic Future
The enterprise’s transition to the Agentic Era is not merely a technical upgrade; it is a fundamental reorientation of the modern firm. The lessons learned from this 3,500-person consumer entertainment company provide universal takeaways for any organization navigating this unprecedented shift:
Redefine, Don’t Just Automate: Companies must meticulously map and redesign legacy processes before they apply AI. As industry data show, automating a broken process only magnifies current frictions.
Address Psychological Barriers Directly: Leaders must proactively address the fear of job loss, the anxiety of contractor cliffs, and role confusion. By tapping into employees’ pride in their work, leaders can reframe AI not as a robotic replacement, but as an orchestration enabler that helps them deliver exceptional experiences and creates more strategic, fulfilling, and creative jobs for humans who are freed from much of the routine manual labor that previously consumed them.
Amplify Energy Through Co-Creation: Cross-functional governance—bringing together engineering, marketing, legal, and product teams from Day 1—is absolutely non-negotiable. If tools are not built for actual end-user needs, they will die on the vine.
Implement Role-Based Upskilling: AI adoption requires behavioral habit-building. Organizations must teach employees exactly how their daily tasks will shift in the new agentic reality, converting fear into immediate, measurable empowerment.
The organizations that win the AI era will be those that invest as heavily in their human operating models as they do in their large language models. Your competitive advantage comes from building organizational capability now, turning the promise of artificial intelligence into lasting, synchronized performance.
Matthew Egol is the founder & CEO of JourneySpark Consulting, podcast host and author of The CX and Culture Connection: Creating a Growth Flywheel by Approaching Customer Experience and Culture Together. Previously, he was a partner in the Retail & Consumer Practice at the consulting firms Booz & Company and PwC, where he specialized in customer experience, culture, and digital transformation.
Randall Rothenberg served for 15 years as the CEO of the IAB, the global trade association for digital marketing, media, and advertising, where he led the industry in public policy, technical standards, marketing, and thought leadership. Earlier in his career, he was the CMO and head of thought leadership at Booz & Company; a reporter and editor at The New York Times; and the author of Where the Suckers Moon: The Life and Death of an Advertising Campaign (Alfred A. Knopf, 1994).