For the first time in business history, we are seeing something completely new: regular employees are using new AI technology faster than the companies they work for. This is not just another technology that needs a quick fix. Instead, it completely changes how companies adopt new technology. In the past, new technology moved from top leaders down to workers. Now, it moves from everyday workers up to leadership. The center of change has shifted from the boardroom to employee chat channels and personal accounts.
Only 9 percent of companies report being fully prepared culturally for AI integration—a figure that inspires approximately the same confidence as a paper umbrella in a hurricane.
“Individuals—human beings both in and outside of business—are adopting AI quicker than can be embraced at the enterprise level. As leaders, we’ve realized we’ve got a vulnerability here.”
—Toby Boudreaux, Global Vice President of Data Engineering at Publicis Sapient
So how does the C-suite lead change management when adoption speeds have already left organizational readiness in the dust?
For perhaps the first time in corporate history, CEOs find themselves in the uncomfortable position of leading a revolution they didn’t initiate and may barely understand. This peculiar reality demands a fundamental recalibration of executive function—you must not only learn the tools better than any other emerging technology, but also learn from your employees to create an overarching strategy.
The fantasy of AI delegation—that comfortable myth where you outsource understanding to the technically inclined—is as outdated as corner offices with landlines. This revolution demands first-person immersion from those at the top. Executives who treat AI like previous technologies—something to be understood through quarterly briefings and filtered summaries—will find themselves presiding over well-articulated strategies that fundamentally misunderstand the very thing they’re strategizing about.
“This is a seismic shift. If you don’t take the time to really understand what the implications of something are, you are not really in a position to guide an organization in adopting it.”
—Bilal Zaidi, Senior Director at Publicis Sapient
Random AI dabbling breeds nothing but digital confetti—colorful, briefly entertaining, ultimately meaningless. The challenge isn’t authorizing innovation but architecting it toward transformational outcomes: customer interactions measured in seconds rather than hours, product cycles collapsing from quarters to weeks. When experimentation lacks strategic anchoring, you create the organizational equivalent of a thousand independent science experiments without a unified theory—interesting perhaps, but ultimately incoherent.
That dizzy sensation you’re feeling? It’s unprecedented acceleration. Whatever five-year plans you’ve crafted are already out of date. The path forward isn’t perfect prediction—it’s architectural adaptability. The annual review cycle belongs in a museum alongside other corporate artifacts. Quarterly pivots, cross-functional teams with actual authority to change course and malleable success metrics that anticipate their own extinction—these are the governance structures that acknowledge AI’s fundamental unpredictability.
The most excruciating calculus awaits in talent distribution, where dedicating your brightest minds to future-focused initiatives means, at times, accepting degradation in current operations. This tension—between maintaining quarterly performance and investing in capabilities that render those very metrics obsolete—defines the contemporary CEO’s dilemma. The wisest approach isn’t always accumulating AI talent but, instead, AI leadership acumen: potentially recruiting executives who’ve already navigated the AI waters rather than hoping your entire existing leadership will spontaneously develop entirely new mental frameworks.
“Take it, embrace it and really aggressively transform your company, because if you don’t, you are not going to be around.”
—Vicki Zoll, Senior Director at Publicis Sapient
The COO’s arena is where AI can bring some of its most clear benefits—but also where people may resist change the most.
“There’s often resistance to AI-driven change. It’s very natural because AI drives fear, essentially fear of job loss or redundancies because there’s so much automation happening.”
—Bilal Zaidi, Senior Director at Publicis Sapient
The old idea that change management comes at the end of transformation is wrong. We often think people just need good instructions to accept change. The truth is that change management must be part of transformation from the beginning, like the tempo set for the entire orchestra. This means including the human side of change when redesigning processes:
When teams redesign work processes without considering how people will adapt, they make a serious mistake.
Companies often dream of quick transformation—replacing old systems with new AI solutions overnight. But this ignores how people actually handle change. Real change requires careful planning: 90-day cycles of gradual improvement rather than sudden overhauls, test teams with different perspectives and attitudes and ways to gather feedback about how people feel, not just about technical results. Success stories serve as proof that others have safely tried new ways of working. Each story helps reduce the fear of those considering similar changes, making new approaches seem less strange over time.
Operational leaders often have a strong attachment to traditional measurements—the familiar numbers presented in quarterly meetings. These old measurements can actually block real transformation. Traditional productivity measures show how well old processes work, not how new capabilities are developing. The real return on investment isn’t just about lower costs or higher revenue. It’s about whether your organization can respond to market changes in weeks instead of months, creating advantages that grow stronger over time.
A better approach measures:
The biggest challenge for operational leaders is creating service delivery that is neither all-human nor all-machine, but a partnership where each does what they do best. This means more than just reassigning tasks—it means rethinking work itself. Identify what machines do efficiently and what humans do with unique judgment, then design systems where these abilities strengthen each other. This partnership requires operational and technology leaders to work closely together. The COO and CIO become architects of evolution, making sure that data systems and operational processes develop together rather than separately.
Bottom line: When implementing AI in operations, focusing only on the technology while ignoring how people feel about the changes will create perfect systems that your teams will quietly refuse to use.
When you look at your organization’s data systems, it’s like exploring layers of history. You’ll find old mainframe computers running COBOL, middle-aged client-server systems and newer cloud systems. Each layer represents a different time period in your company’s technology history. These different layers contain both valuable information and difficult challenges.
While you’ve been building official security systems and rules, your employees may have started their own unofficial AI revolution. Your security team might be using free AI tools to detect threats. Your help desk might solve problems using AI assistants that aren’t officially approved. This isn’t just people bringing their own devices to work—it’s people bringing their own AI. This creates much bigger risks than just connecting personal phones to the company network.
“Many organizations face a digital archaeology challenge, with valuable data fragmented across generations of systems, from mainframes to cloud platforms.”
—Sheldon Monteiro, EVP and Chief Product Officer at Publicis Sapient
Traditional data governance with strict rules and perfect data requirements doesn’t work well in the AI era. Smart CIOs know that waiting for “perfect data” before starting with AI is like refusing to build a house until there’s no dust at the construction site. Instead of creating rules that mainly restrict, build frameworks that help people use data properly. Create different quality levels for different types of data with clear guidelines, use automated systems to find problems early and form teams where risk managers and innovators work together instead of against each other.
When a retail company put AI interfaces on top of their old inventory systems, they weren’t just fixing old technology. They were extending the life of their existing systems while preparing for future replacements. AI can serve both as a temporary solution and as the final goal. The most valuable AI projects aren’t always the exciting ones that get media attention. Often, they’re the ones that make old systems work better when they can’t be replaced immediately. Find where data moves between old and new systems, then use AI to help these different systems communicate better.
The help desk worker who used to reset passwords now designs conversations for AI chatbots that handle these tasks automatically. This isn’t just adding technology to existing jobs—it’s transforming roles. Technical teams move from doing repetitive tasks to recognizing patterns and handling special cases. This change requires looking at current service tasks not just for efficiency but for automation possibilities. Identify which patterns AI can handle reliably and retrain technical teams to supervise rather than do the work directly. This creates both faster solutions and more interesting human work focused on complex judgments.
One global manufacturing company successfully created a balanced AI model. They combined central platforms for company-wide capabilities with team-specific resources. They discovered that innovation works best not with complete freedom or strict control, but with a balance between these extremes. This balanced approach means creating shared platforms for common needs (like document processing or conversational interfaces), setting up safe testing environments with appropriate rules for team-specific needs and maintaining just enough consistency to enable teamwork without stopping experimentation.
“Legacy IT infrastructure is so massive and so embedded in large organizations over decades… you cannot just uproot it and throw it away. This has huge change management ramifications because you’re essentially changing everything about how the organization functions under the hood.”
—Bilal Zaidi, Senior Director at Publicis Sapient
The biggest change for CIOs may be in their mindset—moving from controlling infrastructure to orchestrating experiences across many different AI capabilities. Instead of approving every tool, you establish safe boundaries within which teams can experiment. Instead of managing each deployment, you monitor usage patterns to identify potential risks or opportunities. This transition means creating self-service AI resource catalogs with easy-to-use portals, simplifying approval processes to remove unnecessary barriers and implementing monitoring systems that can detect risks without creating restrictions that push innovation underground.
Bottom line: Your biggest job isn’t just adding AI to your current systems but rebuilding your technology to support both the AI tools you approve and the AI tools your employees are already using without permission.
As CTO, you’re witnessing a strange new reality: your developers now code alongside AI that generates solutions at speeds that make human-only programming seem quaint. Your QA teams build testing agents that find bugs in places humans never thought to look. This isn’t just a new tool in the toolkit—it’s a fundamental reimagining of how technical teams function.
“If we are not considering AI as a full-fledged, first-class citizen of the process, you are missing the point. Individual interactions with AI personas in the team will become more important.”
—Rakesh Ravuri, CTO and Engineering Leader at Publicis Sapient
The traditional Agile Manifesto told developers to value “individuals and interactions over processes and tools.” But in this new reality, we instead need an AI-Assisted mindset: “Individuals and AI interactions over rigid roles and ceremonies.” This isn’t about replacing humans with machines—it’s about creating partnerships where each contributes their strengths. Your development processes must now include AI as a team member, not just as a tool. Define clear responsibilities for what humans do and what AI does. Measure success by what you achieve together, not just by human productivity alone.
The Agile Manifesto prioritized “working software over comprehensive documentation.” The AI era demands “explainable, working software over comprehensive documentation.” When AI helps build your systems, people need to understand how decisions are made. To achieve this, you’ll need to create standards for AI transparency. Build testing frameworks that verify AI-generated code is understandable. Train your teams to document how AI makes decisions, just as they document their own code.
In the past, innovation happened in bursts—a hackathon here, a special project there. With AI, innovation becomes constant. The new standard shifts from “Responding to change over following a plan” to “Responding at pace over perpetuating legacy patterns.” This requires technical pipelines that quickly evaluate AI-generated solutions. You’ll need feedback systems that improve AI capabilities based on real-world performance. And you’ll need to measure innovation by how quickly you adapt, not by how well you stick to outdated plans.
Traditional development built standalone applications. AI creates value when different capabilities work together—document processing, conversation, decision support—all connected in one ecosystem. Finding the right adoption speed is crucial. There’s pressure to join the “AI arms race,” but moving too fast can cause chaos if your organization isn’t ready. Moving too slowly means falling behind. The best approach is creating a thoughtful roadmap with input from across your organization before implementation. To build these ecosystems, standardize how AI components connect to each other. Create shared knowledge bases. Set up governance that ensures consistency across all your AI systems.
AI is changing what it means to be a technical expert. Instead of specializing deeply in specific technologies, the focus shifts to orchestration, judgment and innovation. Successful organizations train developers to guide and evaluate AI contributions rather than write every line of code themselves. They create mentorship programs to help experienced engineers work with AI. And they update promotion criteria to reward effectiveness in human-AI teamwork.
Bottom line: The revolutionary moment for technical organizations isn’t when AI writes perfect code, but when CTOs redefine their development culture to treat algorithms as team members with distinct strengths, weaknesses and working styles that complement rather than replace their human counterparts.
As CFO, your relationship with AI differs fundamentally from your C-suite colleagues. While others rush to embrace the newest AI tools, you find yourself playing a more cautious role—not because you resist innovation, but because the financial data under your care demands thorough protection. This tension—between embracing transformative technology and protecting sensitive financial information—creates a unique set of challenges that other executives simply don’t face.
“The sensitivity of the data we touch when we think about contracts or financial dashboards means that we cannot just use any AI because, by definition, AI stores what they are sent with a view to learn and evolve, and could use the data we share to answer a request from any other random user of the AI engine.”
—Eric Celerier, CFO Commercial Success at Publicis Sapient
The predictability of hourly billing—that fundamental economic equation where time equals money—is rapidly becoming as outdated as paper ledgers. As Celerier notes, “In the past, we were mostly selling to our clients time and materials... The arrival of the AI tools are changing that world. The usage of AI tools delivers a lot of added value for our clients. The classic financial models have to evolve accordingly.” This shift demands creative financial thinking: outcome-based models. As an example, where clients would pay for results rather than hours, subscription access to AI-powered capabilities, or hybrid approaches that combine traditional services with AI enhancements at varying price points based on customization levels and human involvement. The question isn’t whether to change your pricing models, but how quickly you can evolve them before market forces make the decision for you.
The finance professional who can’t explain how AI creates value is like an accountant who can’t explain a balance sheet—technically skilled but strategically limited. This educational journey requires specialized AI literacy programs for finance teams focused not on technical implementation but on business implications—how these tools affect financial models, risk profiles and regulatory requirements. Creating simulations that demonstrate how AI capabilities translate into customer value enables more accurate pricing and forecasting, turning finance from cost controllers into value interpreters.
“There’s a need to understand... what AI tools do we have? What do they do? How do they work? What is our competitive advantage in this field of expertise? To provide adequate support, we need to understand better what we are selling. If you don’t understand the nature and the value of the services your company delivers to its clients, you just cannot define a fair and accurate pricing model.”
—Eric Celerier, CFO Commercial Success at Publicis Sapient
The most delicate financial equation is the following: On the one hand, we are aggressively trying to reap the cost and time savings benefits from AI and transfer those to our clients. But at the same time, we need to fund AI development inside the organization. Solving this equation requires sophisticated cost-accounting systems that separate AI development investments from client-billable work. Leverage financial models that measure both internal gains and client value creation, as well as reinvestment frameworks that direct some AI-driven profits back into capability development. This ultimately creates a virtuous cycle of continuous improvement rather than a one-and-done efficiency gain.
The shift from services to products demands entirely new commercial structures. This product mindset requires different commercial models based on usage or capability access, metering systems that track AI utilization for consumption-based pricing and contract templates that address previously unnecessary considerations like data rights, model improvements and ongoing support arrangements. The finance team that can’t create these frameworks rapidly will find themselves unable to capitalize on their organization’s AI investments.
Bottom Line: The greatest financial challenge isn’t calculating AI’s return on investment but redesigning your entire financial governance system to protect sensitive data while simultaneously enabling the very AI experiments that could transform your business model.
“We are now starting to have cases when we are selling our AI tools and even sometimes embedding our AI tools into the clients’ ecosystems, allowing the clients to use them. This requires a brand-new financial framework.”
—Eric Celerier, CFO Commercial Success at Publicis Sapient
The marketing executive looking backward for inspiration might as well be studying ancient hieroglyphics. The Don Draper archetype—intuitive, charismatic, spinning narratives from creative instinct—has given way to something less cinematically appealing but infinitely more powerful. This fundamental shift isn’t merely technological but strategic: from broad demographics to data-driven personalization. AI is now informing and assisting entire cross-channel customer journey orchestrations—browsing patterns, purchase histories, content development, support interactions, social engagement—creating the unprecedented ability to tailor experiences to individual preferences. The marketer who once knew audience segments now has insights at the individual level.
"CMOs are becoming CDOs (chief digital officers). The CMO of the past? You might think of 'Mad Men' and Don Draper. But the CMO of today is living in data insights and dashboards."
—John Ayers, Global Lead of Strategic Partnerships at Publicis Sapient
The modern marketing department often resembles a linguistic nightmare where specialized teams speak entirely different languages while theoretically pursuing the same goal. This fragmentation—where media, creative, physical (in real life) and digital teams operate in silos—leads to campaigns that make sense individually but fall apart collectively. It creates marketing that’s internally coherent but collectively disjointed. The solution isn’t another dashboard but a fundamental restructuring: unified customer data platforms that capture interactions across all channels, teams organized around audience segments rather than channels and metrics that measure complete customer journeys instead of isolated campaign metrics.
"The challenge with performance marketing today lies in the division of labor. Media teams focused on top-of-funnel brand awareness often speak a different language than their colleagues managing engagement across owned channels. To unlock the full potential of attribution, we need tighter collaboration across paid, earned, shared and owned experiences. That alignment is the true north star."
—John Ayers, Global Lead of Strategic Partnerships at Publicis Sapient
The creative process becomes neither fully automated nor stubbornly manual. Instead, it becomes something altogether new.
“There’s a whole lot of hype about the power of AI in creating marketing materials and creative content, and it can do a fantastic job in that regard. But without it being complemented by good old school human creativity, it could go off-brand or create legal risk.”
—Bilal Zaidi, Senior Director at Publicis Sapient
Successful organizations develop hybrid approaches: AI handles data analysis, content optimization and performance forecasting while humans make strategic decisions. This requires clear boundaries between AI and human responsibilities, oversight systems for AI-generated content and quick feedback loops that allow human intervention when necessary.
When marketing teams spend less time on repetitive tasks—manually scheduling posts, formatting reports, testing email variations—they gain something precious: creative capacity. AI automation isn’t about reducing headcount but redirecting human energy toward innovation and strategy. The transformation requires mapping current workflows to find automation opportunities, creating dedicated “innovation time” when teams can work on strategic initiatives, and measuring both efficiency improvements and innovation outcomes to demonstrate the value of this shifted focus.
As AI makes fake and real content increasingly indistinguishable, authenticity becomes marketing’s most valuable currency. When algorithms can generate endless variations of messages, the human touch becomes not just nice but necessary.
“Data is no longer the currency. Trust is. And with AI, brand authenticity will be the differentiator.”
—John Ayers, Global Lead of Strategic Partnerships at Publicis Sapient
Maintaining trust requires clear policies about AI-generated content, quality control processes that ensure brand consistency and customer experiences that balance AI efficiency with human connection at key moments that matter.
Transformation happens step by step, not overnight. As Zaidi notes: “Some kind of marketing content automated content generation, content review capabilities [are essential investments]... For marketing offices, especially in content creation and things like that, these should be like no-brainers to get as a first step.” The most successful AI adoptions begin with specific pain points that frustrate teams daily, such as juggling four different dashboards to analyze campaign performance. By addressing concrete problems with targeted solutions, showing measurable value and building momentum through early wins, marketing leaders create the foundation for broader transformation.
Bottom line: As AI makes perfect marketing execution available to everyone, the true competitive advantage shifts from tactical excellence to something machines cannot replicate: the courage to develop a distinctive brand voice that sometimes intentionally speaks to fewer people more deeply.
As Chief Experience Officer, you occupy a unique position—standing at the crossroads where all paths of the organization eventually meet. Your role isn’t defined by a single function but by a commitment to see connections others miss. The digital face you present to customers through AI systems isn’t just another technology implementation; it’s the new front door to your entire organization. That chatbot isn’t merely answering questions; it’s forming first impressions, setting expectations and shaping how people feel about your company. A clumsy, frustrating bot damages customer trust more effectively than any human agent ever could, while a thoughtful, efficient one creates the sense that your entire organization runs with the same smoothness.
"The role of this CXO is to connect the dots. You have to have a versatile skill set and mindset to understand different requirements, from business strategy to data strategy to marketing strategy and sales... but always keeping the customer in mind."
—Soulaf Khalifeh, Manager of Customer Experience and Innovation Consulting at Publicis Sapient
The greatest customer experiences don’t emerge from a single brilliant mind but from collective commitment to a shared vision.
“The CXO should own the customer’s north star vision—but that only works if every team and stakeholder feels a sense of co-ownership.”
—Bilal Zaidi, Senior Director at Publicis Sapient
This isn’t about imposing your vision but orchestrating its collective creation. When marketing, product, technology and service teams each see their fingerprints on the experiential vision, it transforms from a departmental mandate into an organizational commitment. This means workshops where diverse teams define the ideal customer experience together, clear principles that guide decisions across departments and visual tools—like AI-enhanced journey maps—that make abstract concepts tangible for everyone involved.
In the rush toward AI-enhanced experiences, the most essential truth is easily forgotten: Technology is implemented by humans and for humans, and its success depends entirely on human adoption. A CXO’s job is to connect teams, grasp their incentives and earn their buy-in. That takes time. The human-centered approach begins with genuine curiosity about how teams operate—their goals, their constraints, their hopes, their fears. It requires aligning incentives so that AI initiatives create value for everyone involved, not just customers. And it demands regular check-ins across teams to maintain unity as projects evolve from concept to reality.
The metrics that guided organizations in the pre-AI era are necessary but insufficient for measuring success in this new landscape. KPIs like time to market, employee productivity and ROI on creative work have always existed, but now they take on a new significance. AI isn’t just a cool feature; it must connect customer satisfaction with operational efficiency. Meaningful measurement requires balanced scorecards that capture both traditional business metrics and new AI-specific outcomes, dashboards that show the relationship between customer experience improvements and operational gains and flexible KPIs that evolve alongside AI capabilities. When metrics remain static while technology transforms, you measure yesterday’s success, not tomorrow’s.
“If AI handles 40% of your tasks, how do you use that time? The answer isn’t just efficiency—it’s rechanneling that energy into high-value, creative work.”
—Bilal Zaidi, Senior Director at Publicis Sapient
While other C-suite roles evolved gradually over decades, the Chief Digital Officer emerged like a sudden evolutionary adaptation—a corporate mutation perfectly suited for an environment where technology isn’t just a business function but the medium through which business itself happens. This role exists in the fertile chaos of perpetual reinvention. The CDO faces a unique paradox: tasked with enterprise-wide transformation while lacking direct authority over the specialized teams who must execute it. Rather than being a design flaw, this is a necessary tension that forces innovation through influence rather than mandate.
“Arguably one of the most important C-suite roles in an organization in terms of harnessing the power of AI and directing AI strategy is the Chief Digital Officer.”
—Bilal Zaidi, Senior Director at Publicis Sapient
Unlike the specialized domains of other C-suite roles, the CDO’s challenge lies in making AI accessible to everyone while preventing digital anarchy. This means creating self-service innovation platforms where non-technical teams can safely experiment with AI, developing tiered access models that match capabilities to expertise levels and establishing common standards that allow diverse solutions to connect rather than conflict. The organizations that succeed don’t reserve AI for data scientists or digital specialists. They’re the ones that make it an organizational utility, as accessible as spreadsheets were in the 1990s but infinitely more powerful. This democratization isn’t an afterthought, and instead the central mechanism through which digital transformation actually happens.
The greatest barrier to transformation is less technological and more psychological—the natural organizational resistance to abandoning familiar practices for uncertain new capabilities. While every C-suite member manages some aspect of change, the CDO uniquely focuses on cultivating digital courage: the collective willingness to experiment despite discomfort. This courage-building requires creating safe spaces for experimentation where failure carries minimal career risk; developing showcase opportunities where early successes receive organizational visibility; and establishing storytelling mechanisms that transform individual learning into collective wisdom. When digital experiments happen in isolation, their lessons remain trapped; when they become organizational stories, they create cultural momentum.
While other leaders focus on what AI can do today, the CDO cultivates organizational capacity to imagine what it might do tomorrow. This forward-looking orientation means systematically exposing teams to emerging technologies before they’re fully mature, creating forums where potential applications can be explored without immediate implementation pressure and developing future-state visions that inspire innovation beyond current constraints. The most successful digital leaders recognize that imagination precedes implementation—that the ability to envision new possibilities often matters more than the technical skill to realize them. This isn’t idle speculation but practical preparation for a landscape that transforms faster than traditional planning cycles can accommodate.
The CDO’s most essential function may be establishing systems that accelerate organizational learning about digital capabilities. Unlike traditional knowledge management focused on capturing established wisdom, digital learning systems must capture emerging insights about rapidly evolving technologies. This means creating mechanisms that document experiments across different business units, developing shared taxonomies that make disparate learning comparable and establishing regular forums where insights move across organizational boundaries. The company that learns about AI capabilities faster than competitors doesn’t just implement better—it develops entirely different strategic options.
While other C-suite roles focus on either digital or physical domains, the CDO uniquely orchestrates their convergence—the increasing integration of computational intelligence into physical spaces, products and experiences. This blended reality requires developing frameworks that guide the embedding of AI into physical environments, establishing experience principles that maintain coherence across digital and physical touchpoints and creating governance models that address the unique ethical questions that arise when algorithms shape physical reality. This integration isn’t merely a technical challenge but a philosophical one, requiring organizations to reimagine fundamental relationships between information, objects, spaces and human experience in ways that previous technological revolutions never demanded.
Bottom line: The CDO must operationalize AI access across the org—fast. That means launching self-serve platforms, creating governance guardrails and institutionalizing learning loops that turn individual pilots into enterprise-wide momentum.
The executive suite now faces a profound choice: attempt to control a revolution already in progress or become its most thoughtful enablers, creating frameworks that channel its energy rather than contain it. The C-suite’s value lies both in a decent understanding of AI capabilities (which will continuously evolve beyond any static comprehension) as well as in creating the organizational conditions where both humans and machines can continuously learn together.
What connects all successful AI transformations is humility—the recognition that no leader, regardless of title, fully comprehends the end state toward which we’re collectively evolving. The organizations that thrive won’t be those with the most advanced AI strategies on paper, but those that have reconstructed themselves, in difficult ways, to adapt continuously as AI capabilities expand in directions we cannot yet imagine.
The question isn’t whether your organization will transform—it’s whether that transformation will happen coherently, with intentional guidance from the C-suite, or haphazardly through a thousand unconnected adaptations.
The AI revolution won’t wait for your carefully orchestrated change management plan. It’s already happening, with or without your permission.