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This article was originally published on CIO.com by Metis Strategy Partner Chris Davis, Managing Director Tony Qamar, and Senior Associate Grace Cozier.
CIOs are under pressure to deliver on AI’s promise. Yet history shows that many digital transformations have underdelivered — not because of strategy, process or technology, but because of people. Without engaged, enabled and aligned employees, even the most promising AI initiatives will stall. Models may be built, but they won’t be trusted or adopted. Workflows may be reengineered, but without buy-in, they’ll sit unused.
At Metis Strategy, our work alongside technology leaders, driving some of the world’s most complex transformations, has resoundingly taught us that success is always people first. The same lessons apply today as organizations pursue AI-powered transformations: those that succeed put people and culture at the center. Culture shapes every element of the operating model — what decisions are made, how decisions are made, how resources are prioritized and how quickly the company adapts.
Despite strong intentions and significant investments, many transformations falter because leaders misjudge where the true risks lie. A common failure pattern is over-indexing on technology or process while neglecting people. New AI platforms or workflows are often launched with great fanfare, but without engagement, enablement and alignment, employees struggle to adapt and value goes unrealized.
As the ADKAR framework highlights, sustainable change requires more than a solution — it requires awareness, desire, knowledge, ability and reinforcement at the individual level.
Another pitfall occurs when transformations become process exercises rather than engines of business value. When initiatives are framed as IT-only or designed around governance for its own sake, they lose relevance and fail to inspire the commitment needed for lasting change.
Transformations also stall when priorities are scattered or poorly communicated. Without enterprise-wide alignment, programs become optional, teams are spread too thin and execution suffers. In these cases, even well-designed AI strategies cannot deliver impact.
Finally, cultural resistance or a lack of trust can quietly derail transformations. Employees who do not see how AI initiatives align with their own incentives may disengage, breeding fatigue and undermining momentum. Without trust in data, models and leadership, adoption falters no matter how advanced the technology.
For CIOs, avoiding these patterns is essential. The organizations thriving in the AI era are those that design transformations with people and culture at the center from the very beginning. Through our client work, we’ve identified four cultural elements that consistently shape transformation outcomes — and four actions leaders can take today to strengthen them.
Successful transformations align operating models with customer needs. Too often, companies define themselves as “sales-led” or “product-led” and allow that orientation to drive key decisions. While this can create enterprise clarity, it risks sidelining customers or alienating parts of the organization.
In contrast, enterprises that adopt a true customer-first approach build resilience and agility across functions. Their operating models align incentives, prioritize experiences and ensure teams move in step with market needs. As explored in Achieving product success: A five-step framework for customer-centric design, an article written by Metis Strategy’s own Andre Lopes de Carvalho, organizations that embed customer centricity into their design and delivery processes create more value and are better equipped to sustain transformation.
Revisit your mission, values and incentive structures. Ensure they reinforce a culture where the customer is the anchor for decision-making at every level and for every department.
Digital and AI transformations are demanding. Employees must adapt to new ways of working, acquire new skills and deliver against rising expectations — all while managing day-to-day responsibilities. Without meaningful investment in people, fatigue and disengagement set in, jeopardizing transformation success and putting millions of dollars of investment at risk.
Leading organizations counter this by embedding continuous learning into their cultures. Targeted learning and development (L&D) programs, aligned with transformation priorities, not only upskill employees but also strengthen commitment to shared goals. The best programs address both business outcomes and personal growth — answering “what’s in it for me” alongside company goals.
Provide protected time and resources for development. Tie L&D investments directly to transformation outcomes to ensure employees are motivated and equipped to deliver. Acknowledge that meaningful learning requires space — even during working hours.
Leadership sets direction, but decision-making structures determine speed. Transformations falter under traditional command-and-control models, where approvals and escalations slow progress. The fewer people responsible for making decisions, the slower progress will be made.
Our experience shows that pushing decisions closer to the problem accelerates execution, improves accountability and fosters innovation. Empowered teams make better, faster choices — particularly when leaders set clear guardrails and reinforce trust. A decision-making framework, for example, clarifies which choices can be made locally and which warrant escalation, enabling faster, distributed decision-making.
Reduce layers of approval and empower small, cross-functional teams with decision rights. Ensure leadership behaviors reinforce empowerment, not control.
No transformation follows a straight path. Markets shift, technologies evolve and customer expectations change. AI adoption in particular requires experimentation — testing models, validating outcomes and adapting quickly to advances. Thriving organizations build cultures of experimentation where teams are encouraged to test, learn and adapt at speed.
We’ve seen this approach succeed when companies create the time and structure for experimentation, whether through dedicated capacity, lightweight governance or innovation programs. Not all experiments can succeed, so rather than penalizing failures, promote a culture that encourages learning from them. The result is not just innovation but resilience — a workforce confident in navigating uncertainty. As Jamie Engstrom emphasized in Technology, talent and transformation at caterpillar, curiosity and collaboration ensure experimentation is both grounded in business value and disciplined execution.
Normalize test-and-learn practices across teams. Protect capacity for experimentation, ensure lessons are shared and reward adaptability as much as execution.
Every transformation involves new strategies, processes and technologies. What ultimately determines success or failure is whether leaders place people and, by extension, culture, at the center. People shape decision-making, innovation and execution; they are the connective tissue of the operating model. CIOs who recognize culture as a true differentiator are better positioned to navigate complexity, make effective decisions and deliver sustained impact.
Wolters Kluwer’s ongoing transformation from a traditional publishing business to a cloud-first, AI-driven enterprise illustrates how people and culture drive successful AI-powered transformations. When we spoke with CIO Mark Sherwood, he discussed how customer centricity is reinforced through business relationship managers (BRM) who bridge technology and business units, ensuring digital investments are aligned with customer and market needs.
Learning and development are emphasized as employees build new skills in cloud, AI and data governance, enabling them to adapt to rapidly evolving technologies. Distributed decision-making is strengthened by embedding BRMs to bring tech and business closer together, ensuring alignment and enabling smarter choices on priorities and resources. Agility is fueled by a test-and-learn mindset, with experimentation balanced by strong ethical AI practices and disciplined measurement.
By maintaining a strong culture and people-first approach, Wolters Kluwer has accelerated its evolution and positioned itself as a leader in delivering trusted, technology-enabled solutions across industries.
CIOs today face constant change — none more urgent than the rise of AI. Shifting customer expectations, new competitive threats and accelerating disruptions are magnified in the AI era. Anchoring transformation in people and culture is what turns volatility into opportunity and lasting advantage.
For CIOs, the mandate is clear: build operating models that not only work today but also adapt continuously for tomorrow. That requires embedding four cultural practices at the center of every digital and AI transformation:
When CIOs champion these actions, they move beyond technology deployment to transformation that is embraced, scaled and sustained. Put people first and digital and AI transformations become not only achievable, but sustainable.
Now a household name in personal finance, Intuit was founded in 1983 by Scott Cook. The company’s four decades of success — the company reported 2022 annual revenue of $12.7 billion and a portfolio of valued products including TurboTax, QuickBooks, Credit Karma, Mint, and Mailchimp — has been possible in part by the company’s dedication to innovation. Innovation is necessary for modern businesses to maintain a competitive advantage, meet evolving customer needs, and attract top talent. Prioritizing innovation can also improve the IT-business relationship by positioning the IT organization as a partner that is uniquely suited to evaluate ideas and pursue those most likely to succeed.
Two conversations on Peter High’s Technovation podcast with Intuit’s Chief Information Security and Fraud Prevention Officer, Atticus Tysen, and Chief Data Officer, Ashok Srivastava, show how Intuit has reinvented itself through IT-driven experimentation driven by a desire to solve real business problems and foster a strong IT-business partnership in the process. These interviews show how a formalized test-and-learn process, a set of practices formalizing the steps taken to ideate, conduct pilots, analyze results and scale valuable ideas across an enterprise, can be used to systematically scale innovation and deliver a range of benefits. Among them:
–Change the culture of the IT organization to encourage more frequent ideation and support team members in voicing ideas they might not have previously
–Empower the IT organization with data and agility that allows it to show up as a true business partner
-Increase the influence of the IT organization to build trust and credibility with the business
-Eliminate silos and encourage expansive thinking to ensure the creation of durable, enterprise-grade solutions
Intuit’s innovation journey highlights these improvements in action, as we’ll see below. We will also share Metis Strategy’s 10-step test-and-learn process that you can implement in your own organization.
“At Intuit, experimentation is everyone’s job,” said Brad Smith, Intuit’s former Executive Chairman. Building a culture of experimentation and innovation requires creating a safe space to allow risk-taking and encourage more people to bring ideas to the table. Intuit’s IT organization prides itself on its hypothesis-driven testing culture designed to pursue new ideas with clear business outcomes rather than rely on legacy solutions, bolstering the IT organization’s strategic value.
Solidifying a test-and-learn process positions IT organizations to play a more active role with business teams, understand customer needs, unlock innovation opportunities, and change the culture of IT from reactive order-takers who “just” keep the lights on to partners who help shape the future of the enterprise. At Intuit, the test-and-learn process is guided by the company’s two innovation competencies, Customer Driven Innovation and Design for Delight, which drive all solution development and ensure strategic focus throughout the ideation process. These defined principles, outlined and enforced by the Intuit labs, help narrow down and develop winning ideas by ensuring new solutions unlock value and solve real problems.
IT leaders must be change champions to ensure the successful adoption of a test-and-learn process and the subsequent shift in culture required to improve the IT-business partnership. Broad participation in the test-and-learn process happens when the process is accessible and engagement is encouraged across all roles and tenures.
Intuit’s technology leaders incentivize innovation by giving employees unstructured time for ideation and solutioning, which fosters their participation in the test-and-learn process and refines the company’s ideation muscle outside of day-to-day responsibilities. Other incentives used to ensure test-and-learn participation include the Scott Cook Innovation Awards, which recognize employee innovators; mentorship programs to guide new participants through the test-and-learn process; rotational development programs to upskill employees; and workshops to refine critical thinking skills.
By enabling test-and-learn experimentation, IT leaders can begin to change their organizations’ culture and empower the IT organization to become a true business partner. Intuit notes that the test-and-learn culture has enabled a durable competitive advantage that allows the company to differentiate itself from competitors while focusing on what matters most to its customers.
In a recent survey, 63% of CIOs reported that they struggle to communicate IT’s business value to business partners. Formalizing a test-and-learn process can improve that communication by giving IT leaders the data needed to tell their innovation story and tie new ideas to tangible business outcomes.
Test-and-learn experimentation produces data surrounding the feasibility and value of scaling an idea. Ideas that are ultimately chosen to scale are backed by data on their projected success and business impact. IT organizations can also provide their business counterparts with data on risk mitigation and projected costs based on the initial testing.
Tysen names data as the primary enabler for successful test-and-learn experimentation as it creates opportunities to take calculated risks. Tysen and Srivastava work together to break down data silos and democratize data so teams can more effectively derive and deliver insights.
Pairing test-and-learn with agile delivery methods can promote a culture that rewards failing fast, iterating, and delivering value in the shortest sustainable time. Intuit focuses on lessons learned from experiments rather than if one was a success or a failure.
Intuit’s innovation competency, Design for Delight, further showcases agile ways of working by prioritizing constant customer feedback, quick prototypes, and iterative solutions to ensure initiatives pursue maximum customer value.
Since experimentation often happens on top of day-to-day responsibilities, transparent and realistic expectations must be set to prevent under-delivery or delays and preserve working relationships. Tysen manages business expectations by ensuring that the IT organization outperforms traditional IT metrics as a prerequisite for experimentation. Operational excellence builds trust between IT and business partners and creates space for test-and-learn experimentation that builds that trust further via successful ROI-generating innovation initiatives.
The typical IT delivery muscle must be refined, and often rebranded, to position the IT organization as an innovator rather than an expensive bottleneck. Tysen says his organization builds credibility with the rest of the business by leveraging business metrics and KPIs, not just IT metrics, when evaluating ideas generated through the test-and-learn process. Applying business metrics to IT-generated ideas ensures that the IT organization and the business are being held to the same standard and can help ensure fair appraisal and understanding of each initiative’s value.
In a recent survey, fewer than 5% of CIOs reported that they spend time talking about business outcomes or measuring the business outcomes created by the technology they deploy. This is a significant oversight preventing buy-in and limiting the IT-business partnership. Tysen emphasizes the importance of listening to partners to ensure his organization accurately understands their problems so he knows what is needed to create relevant soultions. The consistent use of business metrics across Intuit also ensures the appropriate acknowledgment of IT’s test-and-learn successes.
Test-and-learn experimentation breaks down traditional business silos and seeks to prevent ad-hoc ideation, eliminate repetitive solutioning, and facilitate cross-functional collaboration. It also promotes enterprise thinking, a practice of monitoring cross-functional requirements, scalability considerations, and long-term needs such as reducing future rework and technical debt.
Srivastava notes that Intuit’s process for test-and-learn experimentation relies on conducting deliberate tests that solve specific and identified problems rather than needless, temporary solutions. Test-and-learn experimentation not only brings MVPs to life with speed but also facilitates deliberate and intentional conversations about long-term considerations and dependencies during the product creation process, ensuring that the final product meets as many consumer needs as possible.
When working with clients seeking to streamline and scale innovation, we use a 10-step test-and-learn process to govern the intake of ideas, manage stakeholder expectations, accurately reflect capacity, and capture data to inform a solution’s journey. This process helped a recent client identify and eliminate silos that hindered collaboration while elevating the IT organization to the status of a business partner rather than an order taker.
Implementing a test-and-learn experimentation process enables an organization to narrow an infinite number of ideas and pursue only those that will deliver the most value. The IT organization is uniquely positioned to facilitate this process and help the business identify winning ideas due to its digital testing capabilities and data collection methodologies.
Working closely with business partners can help teams across the organization avoid placing big bets on ideas that may drain their resources without delivering the needed value. Prioritizing resource allocation based on test data and iterating throughout the solution development process creates a virtuous cycle where the business will increase its speed to market for winning ideas while guaranteeing maximum customer satisfaction.
The benefits that come from implementing a test-and-learn process will not be realized overnight. A structured approach to change management and user adoption is needed to ensure an effective transition. It makes sense to start small. With support from internal change champions, consider piloting a beta test-and-learn process, secure quick wins, and use that momentum to facilitate a broader rollout.
Once adopted, an enterprise might face execution hurdles that prevent maximum value realization. For example, a company may not have the discipline needed to define the hypotheses that drive testing, resulting in the creation of tests that do not produce the needed information.
Alternatively, existing data collection and analysis capabilities may not be sufficient to derive conclusive test results. An enterprise may also suffer from “analysis paralysis,” which can create stagnation when a test fails and lacks ownership over revisions. To learn more about avoiding these experimentation pitfalls, see this article that outlines how business experimentation frameworks can help mature a test-and-learn culture across an enterprise.
