Artificial intelligence is often considered a technological challenge by companies. However, the real challenge lies in how humans view and adopt it. In other words, it’s a matter of organizational culture.
This is especially true in organizations where human decision-making is deeply entrenched when compared to machine decision-making. Would employees easily trust and use AI systems? Probably not. This would unquestionably hinder the desired outcomes from the investment. The issue can be further aggravated in companies with long-standing values and a strong sense of organizational inertia.
By failing to address organizational culture, even the most advanced technology will fail to achieve its full potential. Successfully navigating a transformation journey towards AI calls not only for the necessary material and technological resources. It takes leadership with the right mindset and ability to lead this shift while placing culture at the helm. Only then organizations will fully reap the intended benefits AI technologies can bring.
AI Leaders Describe Their Main Challenges
Adopting AI comes with its unique challenges. As several AI leaders in Silicon Valley, Seattle, and across hubs of AI talent shared with us, there are four central areas they grapple with the most in their organizations:
- Misplaced expectations of quick returns on investment
- Organization’s concerns over job displacement
- Difficulties in integrating AI into existing workflows and use case prioritization
- Underestimating the overall cost of implementing AI technologies
From our ongoing conversations with these executives, it has become increasingly clear that achieving a level of AI adoption that enables organizational efficiency and team synergy requires a comprehensive strategy. One that goes beyond just pure technological tools. It requires placing culture at the core, with a leader who not only understands the challenges but who can also address the cultural and social aspects of an organization to lead a successful implementation and acceptance.
Cautionary signals about AI's potential flaws reverberate across industries, emphasizing the need for prudent and intentionally human-centric leadership, as Ramesh Razdan, Global CTO/CIO at Bain & Company, explains: "AI is a groundbreaking technology, and it is crucial to approach it with a human-centric perspective. This entails ensuring that social and environmental concerns are central, and that we develop, deploy, and monitor these systems in a thoughtful, responsible, and ethical manner."
ADAPT: A Framework for Leading Cultural Change
Organizational culture refers to the shared values, beliefs, behaviors, and norms that shape the organization's identity.
Understanding the existing organizational culture is critical for identifying potential barriers to AI adoption and developing the right strategy to overcome them. For example, if an organization has a strong pre-existing culture that values human decision-making over machine decision-making, it may be tricky to get employees to trust and use AI technologies. AI startups may not face these internal hurdles, though they have plenty of other early-growth stage challenges. What’s important is to assess culture first to identify potential barriers, and then strategize the best messaging to address them internally.
As more organizations move towards the adoption of AI across all industries, several leaders we work with have shared with us how they are progressing in planning for, encouraging, and managing the cultural change needed to supplement their workforce with AI technology.
Drawing from the insights they shared, we propose a straightforward, actionable, and culture-centric framework for leaders and organizations embarking on their own journey of AI adoption. We call it ADAPT:
- Align the adoption of AI with business strategy. Humans become more engaged in their work when they feel connected to the broader business strategy. Find tangible examples of how values of innovation and experimentation will meet stakeholders’ needs and lead to a competitive edge in your industry. This involves creating an environment that encourages employees to experiment with new technologies, take calculated risks, and learn from their failures.
A culture of experimentation eliminates the fear of failure and is essential for fostering innovation and ensuring that employees are willing to embrace new technologies, including AI, to achieve business goals. As Sumit Gupta, Google’s Head of Product Management, Google Infrastructure, puts it: “Generative AI has inspired hobbyists to build AI based applications for fun! Give your team the space, time, and opportunity to bring this AI revolution to your business. Embrace the passion of your team and drive its energy towards business use cases that deliver clear outcomes for your company. This not only connects your team to your mission, but also encourages risk taking in a calculated way.”
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Develop a communication strategy. Crafting a communication strategy that effectively highlights the benefits of AI adoption is critical. Leaders should tap into internal communications channels to deliver a compelling narrative consistently. This serves multiple purposes, including enhanced transparency, addressing concerns from employees regarding job displacement, engage teams in a productive dialog, and emphasize the role of AI in augmenting human skills rather than replacing them. Additionally, we have observed that effective leaders outline the benefits of AI through these communications, such as increased efficiency, accuracy, and productivity, and emphasize how it can help employees focus on higher-value tasks.
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Advance experimentation. Hackathons can be an effective way to encourage employees to test AI technologies and demonstrate their potential. They also provide a platform for the workforce to showcase their skills, learn from one another, and foster a culture of collaboration and innovation. Organizations that are on the path of becoming engineering and innovation led foster experimentation and self-alignment through open hackathons and innovation challenges. These events enable employees to work collaboratively across different levels and siloes to solve real-world problems using AI technologies.
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Prioritize training. To manage a cultural transformation successfully, it is essential to prioritize training programs that identify required skills to upskill or reskill employees. These programs are designed to be accessible, interactive, and relevant to employees' roles, with regular opportunities for employees to refresh their skills and stay up to date with the latest AI technologies. Common examples include online learning platforms with on-demand AI-related courses, webinars, and training modules. These are supplemented by on-the-job training and coaching, where employees learn from experienced colleagues or AI experts.
For Jeff Sidell, Chief Technology Officer of Advarra, achieving a satisfactory level of effectiveness on AI doesn’t necessarily mean having a formal training. It starts with properly training people. As the leader puts it: “Not everyone has a degree in advanced statistics or data science, but I've found that anyone can understand the tradeoffs, risks and business value from AI if it's explained properly. I've seen too many businesses hire dozens (sometimes hundreds) of data scientists without a clear idea of what exactly they're trying to accomplish. A handful of data scientists and a few good data engineers can effectively produce AI-based solutions.”
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Tackle potential biases. Leaders who prioritize an inclusive and diverse workplace cannot overlook the role AI plays in advancing or hindering this culture. Because just like humans, AI technologies are susceptible to biases, too, underscoring the need to intentionally embed diversity and inclusion components into their development.
Mature business leaders have taken accountability for eliminating such biases. They recognize the importance of ensuring that AI technologies are developed and used in a fair, transparent, and accountable way. They ensure that AI technologies are designed and tested in a way that is free from bias and discrimination. Just as for many other leadership functions, implementing a well-balanced AI approach requires diverse teams of experts, including data scientists, ethicists, and social scientists, to collaborate on developing and testing AI technologies. It also requires ongoing monitoring and evaluation to ensure that AI technologies are not amplifying existing biases and discrimination in the workplace.
