Transformation: The Key to Winning the AI Race
Organizations Need to Address Why Their Teams Reject AI
In the third year of the generative AI-inspired boom, adoption conversations, and hype continue to evolve. Yet AI’s promise frustrates, remaining out of reach for many organizations.
While an overwhelming 98% of business leaders express eagerness to adopt AI and have conducted initial experiments, still only 17% can demonstrate concrete operational adoption that has created benefits in their profit and loss statements, according to Accenture (see above chart). This gap between ambition and results hints at a crucial truth: the challenge extends far beyond the actual AI technology.
While most tend to focus on building a strong roadmap to integrate meaningful use cases, data governance, and general organizational AI maturity, increasingly workplace transformation has become a core aspect of adoption.
A True Challenge: Organizational Transformation
(Source: Wharton)
Successful AI implementation demands a fundamental transformation of how organizations operate. Many companies approach AI adoption as a purely technical challenge, but this perspective misses the broader organizational changes required for success. Shunned as change management, workplace transformation is no longer avoidable for companies.
A recent Wharton business article dives into the many elements of workplace transformation to foster AI adoption. It illustrated the need for employee upskilling, improving attitudes toward the technology set, executive sponsorship, and providing the necessary time allocations and reward infrastructure to succeed. It all adds up to building a culture of AI adoption.
The one area so many companies fall down on is true executive sponsorship. Most leaders allocate an experimental budget, appoint a single advocate to implement AI, and then expect to see results. In reality, an executive needs to dive deeper into the project to ensure success. They must engage in classic executive sponsorship as the cornerstone of successful project success, including, yes, AI transformation.
Good project management requires executive leadership for an AI project goes far beyond merely approving budgets or signing off on initiatives. True executive sponsorship includes:
Active engagement in removing organizational barriers
Regular communication about AI initiatives' importance and progress
Visible participation in AI-driven processes
Protection of AI initiatives during budget reviews and resource allocations
Personal investment in understanding AI's capabilities and limitations
It also requires executives to take the time to educate themselves about how AI works from a strategic perspective so they can internalize its impacts on teams and workflows, and understand the core elements of building an enterprise AI implementation. Without understanding AI, it is challenging to implement it. While some of this can be done in the process, executives would be well served getting in front of the project to help shape it from the onset.
Building a Culture of AI Adoption
Employee resistance to AI often stems from fear, misunderstanding, and concerns about job security. Unfortunately, most organizations aren’t addressing those fears. In fact, in the face of haphazard DOGE cuts in the name of efficiency and recent Silicon Valley layoffs, some employee fears are getting realized in the national media narrative.
Given the impact that resistance to new technology adoption can have on an organization, employees, and their mutual success, it’s best to work together to achieve adoption. Employees benefit through upskilling and adding highly valuable AI skills. And employers get their much-needed workplace transformation to operationalize AI.
Comprehensive Training Programs and Incentives
Successful organizations address many of the workforce challenges through training programs that go beyond basic tool usage. AI is not just a new software package. It literally requires management, human judgment, data hygiene, and also a constant agile attitude to address and embrace new evolutions.
Good training programs include:
Understanding AI's role in enhancing human capabilities
Practical examples of AI-human collaboration
Ethics and responsible AI usage
Data literacy and basic AI concepts
Regular updates on new AI capabilities and use cases
An understanding of operations and the processes that drive workplace success is a critical aspect of AI as well, particularly more complicated agentic AI. This often requires documentation and cross-collaboration, both banes of the daily workload. Efforts to embrace thoroughness and relationship-building need more than an acknowledgment; they need to be incentivized. Incentives also work to provide individuals with additional rewards for success.
Organizations need carefully designed incentive mechanisms that encourage AI adoption without creating unintended consequences. Effective approaches include:
Recognition programs for innovative AI applications
Performance metrics that incorporate AI utilization
Career advancement opportunities tied to AI expertise
Team-based rewards to foster collaboration for successful AI implementations
Sharing of success stories and best practices
Provisioning the necessary time to train and adapt AI
As already noted, AI implementations rarely succeed in isolation. This is why executive sponsorship is absolutely necessary. While some cultures and individuals work well together, most are disincentivized to work together, and some have intradepartmental rivalries and politics.
Executives need to ensure their larger enterprise is functioning by working together and with their teams to resolve and overcome the natural silos that emerge in enterprises. Often an executive’s participation and ability to work with other teams is crucial to breaking down these boundaries. And AI requires unprecedented levels of collaboration across departments, necessitating:
Clear communication channels between technical and business teams
Shared accountability for AI outcomes
Joint decision-making processes
Integrated data governance frameworks
Cross-functional AI working groups
Data Governance: The Foundation of Success
Data governance is often a jargon-filled endeavor that causes many to struggle.
Data governance is a well-discussed element of AI adoption. It is also one of the least addressed factors in organizational cultures. As a result, the AI movement has provided a true consequence for many brands. Some have seen garbage in and garbage out become a reality, and others simply cannot implement AI because their data is such a mess the cost of the cleanup makes the project prohibitive.
Humans create that mess, and they have little incentive to take the extra steps to ensure success. While proper data governance often includes the most important elements, it rarely addresses what it takes to get actual data users to practice strong practices. Notice the normal components of a data governance program:
Clear data ownership and responsibility structures
Quality control mechanisms for AI training data
Privacy and security protocols
Data access and sharing guidelines
Regular data audits and cleanup processes
Where are the education and training programs? How can other departments be incentivized to practice better data hygiene? This is a workplace transformation challenge, and the executive team needs to get behind it if they anticipate operationalizing AI in the near future..
CIOs know that data is critical too. In fact, data governance is the #1 priority for CIOs in 2025 (source). So expect to see this become a dominant topic inside organizations this year.
There’s a hidden challenge: marketing, sales, operations, and technology teams don’t speak the same language when it comes to discussing how to collect, handle, and use structured and unstructured data. That’s why we created a plain language data glossary—a simple, shareable guide to help teams shave better, more productive data conversations with their tech counterparts. Download your free copy.
Measuring Success: Beyond Traditional Metrics
Traditional measurements often fail to capture AI's full impact. More than anything, this seems to be one of the buggiest issues affecting the adoption cycle. Too ofte,n the conversation around AI has become one of time savings or error reduction, both of which put the human worker at risk of losing their job to AI systems.
While automations do achieve these results, humans and AI tools working together can produce greater outcomes. Smart organizations will see the bottom line can be improved by more than automating the old. They can strengthen their business and better achieve their strategic vision through innovative blends of creative human thinking and AI.
Organizations need new metrics that consider and address:
Productivity improvements
Decision-making quality
Innovation acceleration
Customer experience enhancement
Employee satisfaction and engagement
More than anything, organizations should understand what the ultimate barometers of success are. As such, they should tailor their AI metrics to achieve those most important outcomes, just as AI should ultimately serve their business mission.
Looking Ahead: The Path Forward
Winning the AI race isn't about having the most advanced technology. We are seeing lots of organizations procure and experiment with AI, even engage in pilots, yet fail to operationalize. It’s easy to blame the technology. Instead, organizations need to look in the mirror. AI success requires creating an organization capable of leveraging AI effectively.
A workplace transformation of this nature requires:
Long-term strategic thinking
Patient capital investment in the workforce
Continuous learning and adaptation
Strong change management capabilities
Unwavering executive commitment
Organizations that will succeed in the AI era aren't necessarily those with the biggest budgets or the most advanced technology. Success will come to those who successfully transform their organizational culture, processes, and people to embrace AI's potential fully.
Start now, move deliberately, and maintain focus on both the technological and human elements of change.