How to balance people and technology for AI success

Insights

• Organizations that delay AI adoption risk not only competitive disadvantage but also potential market irrelevance.
• However, jumping onto the AI bandwagon without a clear, people-informed vision guiding initiatives isn’t the answer.
• When organizations prioritize a tech-first or tech-only approach and overlook the critical human and organizational change management aspects of AI, it can lead to adoption failure, workforce resistance, and ultimately, unrealized value from the AI investments.
• To ensure success in AI endeavors, organizations must place people and organizational needs at the heart of their AI roadmap.

The AI revolution isn't waiting for anyone. Some organizations have raced ahead and woven AI into their businesses to transform both products and processes, operating models, and business models. And others want to get on the bandwagon.

AI is increasingly embedded in the everyday experience, from restaurant recommendations to voice-activated driving directions. Organizations that delay AI adoption risk not only competitive disadvantage but also potential market irrelevance. However, AI implementations also come with their own challenges — such as lack of a cohesive vision of outcomes, uncoordinated efforts among different groups within an organization, getting employees up to speed on skills, fear of AI replacing humans, historically embedded work patterns and processes, and ethics and trust issues, not to mention lack of focus on people-centered change management. Organizations that prepare to solve these challenges before implementing AI will be the ones that succeed.

Complexities of AI deployment

Infosys’s Enterprise AI Readiness research found that only 2% of leaders are confident of being ready on all five dimensions identified in Infosys’s framework on company readiness for enterprise AI — strategy, governance, talent, data, and technology.

At the operational level, organizations often vacillate when faced with AI implementation decisions. They grapple with identifying promising use cases and then hesitate to experiment with AI in their operations and product development. Infosys’s Data+AI Radar research highlights the importance of companies defining the problem they aim to address with AI — essentially, identifying the use case — before deployment to ensure meaningful outcomes and satisfaction from their AI initiatives. Without a systematic approach to resource allocation and progress tracking, their AI initiatives can falter.

Organizations can also stumble without a people-informed vision to guide their AI initiatives. When teams operate in isolation rather than collaborating, they fragment their AI efforts and sometimes invest in lower-priority areas that fail to deliver enterprise-wide value.

The workforce presents its own set of people-related challenges. Organizations struggle to equip their employees with AI skills and create environments that inspire continuous learning. Albert Hahm, a partner in the AI and automation practice at Infosys Consulting, says: “For decades we have helped clients manage the three major levers of change – that is, people, process, and technology – where historically, technology has been the most difficult lever solve to unlock value. In this latest round of the AI revolution, people are now the bottleneck to gaining full value; as AI is an amplifier of our people’s knowledge, capabilities, innovation, and values.”

When some organizations attempt to upskill their workforce for AI, they encounter unexpected complexities despite significant investments. While integrating AI and automation into operations, they can face dual challenges: The need to train large numbers of employees in AI-related skills, coupled with the rapid evolution of the technology itself making training quickly obsolete. That can require an upskilling pivot to combine traditional training with ongoing, real-time support to help employees effectively use both new and evolving AI tools.

Legacy work patterns and processes often clash with AI-driven approaches, which creates friction. Employees can have trust issues — questioning AI's role in decision-making, the reliability and ethics of AI outputs, and fearing job displacement.

Companies that tend to focus on the tech-first or tech-only approach risk neglecting the critical human and organizational change management dimensions of AI, creating potential problems of adoption failure, workforce resistance, and unrealized value. For example, IBM's Watson for Oncology was designed to assist doctors in diagnosing and treating cancer, but it encountered significant challenges. While the technology showed potential, one of its many shortcomings was an unintuitive user interface. This issue stemmed from limited engagement with end-users during development. Ultimately, a combination of all the issues in it led to the program being scrapped in 2023.

The prevailing priority

Research validated by Stanford University identified eight essential cultural traits that predict AI success: adaptability, customer focus, collaboration, attention to detail, results orientation, integrity, transparency, and people-centricity. Organizations that aligned these cultural elements with their strategy saw impressive results — an average revenue growth of 44.5% over three years, while companies lacking this alignment experienced wildly varying outcomes from 75% losses to 1,000% gains.

Organizations that integrate human-centered strategies into AI adoption are also known to demonstrate more successful technology implementation and enhanced ROI. As the BCG 2024 AI Adoption Study shows, organizations that are AI leaders focus 10% of their resources into algorithms, 20% into technology and data, and 70% into people and processes.

To successfully navigate the AI transformation journey, the people-centric elements that organizations need to factor in include leadership alignment and vision, a comprehensive AI governance framework, skills and capability development, a cultural evolution, and trust.

The way forward

A successful AI roadmap puts people and organizational needs at its center, focusing on key implementation priorities:

• An antidote to a tech-first or tech-only approach is for organizations to establish strategy and vision early. Many organizations start with a proof of concept (PoC), but a technology-only PoC that is not tied to a business vision or quantifiable business benefits will lack the direction required to achieve business value. Gartner proposes strategy as the first of seven AI roadmap workstreams and a key guide for AI goals and prioritization. Thus, organizations must establish a foundational strategic AI vision, which is both compelling and focused on core business functions, then adjust and adapt it iteratively over time. A compelling AI vision unifies disparate initiatives and aligns teams around shared goals. This accelerates innovation and maximizes return on AI investments.
• To align leaders to the AI opportunities within the scope of the vision, one practical approach is to involve leaders in hands-on exploration sessions with AI technologies aligned with the core vision, then collaborative sessions to prioritize AI use cases. Leaders across divisions must unify their understanding of the agreed vision for AI's strategic role and clearly articulate its impact on business outcomes. Consistent messaging across all organizational layers is key. For instance, a trend in organizations leading in AI is to establish AI leadership councils that help to align AI with organizational vision and strategy.
• Establishing AI governance will entail actions such as creating a dedicated AI Center of Excellence, developing clear policies and procedures, and implementing metrics to both measure AI adoption progress and serve as feedback loops for continuous improvement. Microsoft's Office of Responsible AI (ORA) provides a notable example. It established a dedicated AI governance structure, created the AI, Ethics, and Effects in Engineering and Research (AETHER) Committee, developed and published clear AI principles and guidelines, and implemented responsible AI standards throughout Microsoft.
  
  Governance will also help structure AI decision-making with clear accountability and responsibility matrices. For example, Google recently updated its AI Principles Governance Framework, which covers management of AI development and deployment throughout the organization. It includes a review process for AI projects, an external advisory council for AI ethics, a public commitment to seven core AI principles and equally important, cancellation of AI contracts that do not align with ethical guidelines.

  Establishing AI governance will entail creating a dedicated AI Center of Excellence, developing clear policies, and implementing metrics to both measure AI adoption progress and allow feedback loops for continuous improvement.

  
• To maintain workforce competencies that align with emerging technologies, companies need to not only upskill and reskill employees in both AI technologies and the new or modified roles they demand but do so dynamically to match AI’s fast pace of change. Organizations can use skills intelligence platforms that map and measure employee skills in real time. This helps them guide employees toward opportunities that match their talents, spot critical skill gaps before they impact performance, and power smarter recruiting and team-building through AI.
  
  Organizations must build agile learning programs that adapt at technology's pace, use interactive digital adoption platforms that guide users through new processes, enable live learning environments where employees practice with real-world systems, and implement strong mentorship networks that drive continuous growth. Digital adoption platforms can help here: These provide contextual guidance and interactive walkthroughs directly within software applications, and can be dynamically updated. Walmart, for example, learned from its initial challenges, and developed an adaptive learning platform that updates training modules in real time as AI capabilities evolve. By investing in its employees' growth, Walmart ensured that staff felt valued and equipped to handle new technologies.
• Building trust in AI begins with transparent communication about its capabilities and limitations. Organizations need to regularly communicate on AI initiatives and how they align with the company’s goals and employees’ roles. They should involve employees in the design, training and evolution of AI solutions, creating a culture of shared ownership and understanding. This collaborative foundation helps teams view AI as a trusted partner rather than a mysterious force. The Infosys Consulting AI&A group, for example, launched multiple AI Champions programs in which AI creators and early adopters mentor colleagues and demonstrate practical AI applications in workshops and webinars. It is yielding higher employee trust, confidence and enthusiasm for AI, by helping employees feel more involved, better prepared, and less apprehensive about the changes AI brings.

  Building trust in AI begins with transparent communication about its capabilities and limitations.

• Cultural transformation is a key driver of successful AI adoption and revenue growth. Cultural development isn't just a nice-to-have, but a critical foundation for successful AI transformation. Organizations can create cultural change support for AI by identifying and empowering change champions in each department. That entails recognizing employees who facilitate AI-related changes and potentially building AI-related goals into performance measures.

Success hinges on integrating a people strategy into AI implementations from the start, involving employees in AI development, addressing ethical concerns head-on, leveraging culture in support of AI, and empowering change champions throughout the organization. By putting people at the center of AI transformation and balancing people-focused strategies with technology-centered approaches, organizations can turn the technological potential of AI into lasting business value.