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Understanding AI challenges


Implementing generative AI: a strategic guide to overcoming the challenges

Published 18 May 2023 in Strategy • 8 min read

Generative Artificial Intelligence (AI), though a recent development, is already transforming industries.  


Systems like ChatGPT and their successors offer businesses exceptional opportunities to innovate and optimize operations. However, they also pose threats to established companies and will disrupt entire industries. Navigating the complexities of implementing these advanced technologies is crucial for securing every company’s future. While the challenges may seem daunting, they can be overcome with the right expertise and a strategic approach. 

The need for immediate governance 

In the short term, your primary concern should be that employees may inadvertently provide sensitive data and IP to generative AI algorithms without proper oversight. This information could be used by tool providers, and potentially lead to data breaches, unauthorized access, or even misuse of proprietary information. To address this issue, you must establish immediate policies and procedures governing the use of generative AI systems, including guidelines on the types of data that can be shared with these algorithms.  

Generative AI systems also may draw on the protected IP of others in ways that are not immediately apparent. This could expose your company to lawsuits, fines, and other unforeseen problems. Implementing governance and controls can help mitigate this risk by establishing clear guidelines on the acceptable use of external data sources, and incorporating processes to verify that AI-generated content does not infringe upon the IP rights of others. Consider engaging legal and IP experts to help navigate the complexities of copyright and patent law in the context of generative AI. 

Understanding the strategic challenges  

Beyond these immediate concerns, companies face a host of challenges in implementing generative AI, including legacy system integration, data privacy and security, scaling and integration, cost and resource allocation, talent acquisition and retention, bias and fairness, and ethical and legal concerns. By gaining a deeper understanding of these obstacles, you will be better prepared to develop a strategic approach for overcoming them.  

One of the biggest concerns when implementing generative AI is integration with legacy systems and data. These older systems are often difficult to change and integrate, making the adoption of new AI technologies seem like an insurmountable task. However, you must tackle this challenge head-on, as the rewards of successful integration far outweigh the costs. Begin by identifying the most critical legacy systems and data sources that need integration with the AI solution. Assess compatibility, bridge gaps, and address incompatibilities to create a seamless, unified system. 

Data privacy and security are paramount when working with generative AI models, as they often require large datasets to perform effectively. You must establish robust security measures to protect sensitive data during training and deployment, avoiding breaches or unauthorized access. Ensure that your organization is compliant with dataprotection regulations and proactively address any potential vulnerabilities.  

Scaling and integrating generative AI systems into existing workflows and infrastructures can be complex and time-consuming, especially for large organizations with legacy systems. You must develop a well-planned integration roadmap, considering both short-term and long-term goals. Allocate resources effectively, and seek external expertise if necessary, to ensure a smooth transition and minimize disruptions to your operations.  

Cost and resource allocation are crucial considerations when developing and implementing generative AI systems. You must balance the significant investments in hardware, software, and personnel against the potential benefits of adopting generative AI. Develop a thorough cost-benefit analysis to justify your investments and prioritize projects with the highest potential impact and return on investment. Consider leveraging partnerships, grants, or external funding to support your AI initiatives.  

Talent acquisition and retention are critical for successful generative AI implementation. You must invest in hiring and retaining skilled AI professionals, recognizing that the demand for AI expertise often outstrips the available talent pool. Develop strategies to attract top talent, provide ongoing training opportunities, and create a supportive work environment that encourages innovation and collaboration. 

Bias and fairness are vital considerations in generative AI implementation. AI models can inadvertently learn biases present in the training data, leading to biased outputs or recommendations. To prevent such issues, you must carefully curate training data, removing biases that could reinforce stereotypes or discrimination. Additionally, consider implementing fairness algorithms and regularly auditing your AI system’s outputs to ensure equitable results. 

As AI becomes more impactful, governments and regulatory bodies inevitably introduce new regulations to ensure responsible use. You must stay abreast of these changes and adapt your practices accordingly. Build strong relationships with regulators and industry bodies, actively participating in discussions to shape future policy, and demonstrate your commitment to responsible AI use. 

Finally, public perception and trust play a crucial role in the adoption and acceptance of generative AI technologies. You must address the public’s concerns about AI’s impact on society, jobs, and privacy to maintain trust and foster acceptance. Engage in transparent communication about your AI initiatives, showcase the benefits, and demonstrate your commitment to ethical AI practices. Additionally, be prepared to participate in public discourse, contribute to educational efforts, and collaborate with other stakeholders to create a shared understanding of AI’s potential and limitations. 

Organizing to meet the challenges  

To successfully overcome these challenges, focus on the following actions to help your organization proactively address potential issues and efficiently utilize resources. 

1. Develop a comprehensive AI strategy: begin by outlining your organization’s objectives, priorities, and resource allocations for generative AI implementation. This strategy should include a clear vision for how AI will drive value and innovation within the organization, as well as a roadmap for achieving specific milestones. Regularly review and update the strategy to ensure it remains aligned with evolving business goals and technological advancements. 

2. Establish strong partnerships: building relationships with external AI experts, technology providers, and relevant industry bodies will give your organization access to the knowledge, resources, and support needed for successful AI integration. Collaborate with these partners to identify best practices, leverage new technologies, and share insights on overcoming common challenges. These partnerships can also help your organization stay informed about emerging trends and regulatory changes. 

3. Build a culture of responsible AI use: emphasize the importance of ethical considerations, data privacy, and compliance with evolving regulations within your organization. Develop and enforce guidelines for AI development and deployment, ensuring all stakeholders understand their responsibilities in maintaining a responsible AI environment. Encourage open discussions about the ethical implications of AI, and promote a culture of accountability and transparency. 

4. Regularly evaluate and update your AI strategy: stay adaptable by continuously monitoring technological advancements, regulatory changes, and shifts in public perception. Adjust your AI strategy in response to these developments, to ensure your organization remains at the forefront of AI innovation and maintains a strong reputation for responsible AI use. 

5. Develop a robust data-governance framework: a comprehensive data-governance framework is crucial for ensuring data privacy, security, and compliance, as well as addressing bias and fairness concerns in AI outputs. Implement policies and procedures to manage data access, storage, and usage, and establish processes for auditing and monitoring AI systems to identify and mitigate potential biases. 

By actively addressing the challenges associated with generative AI implementation in these ways, you can position your organization to harness the transformative potential of this technology

6. Allocate resources effectively: balance the costs and benefits of AI investments by prioritizing projects with the highest potential impact and return on investment. Explore partnerships, grants, or external funding opportunities to support your AI initiatives and make informed decisions about allocating resources for AI development, integration, and ongoing maintenance. 

7. Invest in talent acquisition and retention strategies: build a skilled AI team capable of driving innovation and overcoming implementation challenges. Develop strategies to attract top talent, such as offering competitive compensation packages , providing ongoing training opportunities, and fostering a supportive work environment. Retain valuable employees by promoting career development and offering opportunities for growth and advancement within the organization. 

8. Foster a collaborative and innovative work environment: encourage cross-functional collaboration and the sharing of ideas to drive AI innovation. Establish processes and platforms for team members to communicate and work together effectively, and promote a culture of continuous improvement by recognizing and rewarding creative problem-solving and innovative thinking. 

9. Actively participate in shaping AI policy: share your organization’s experiences and insights, to contribute to the development of responsible AI regulations and industry standards. Engage with regulators, industry bodies, and other stakeholders to help shape policies that support AI innovation while addressing ethical concerns and societal impacts. 

10. Engage in transparent communication: build trust in your organization’s commitment to responsible AI use and showcase the benefits of generative AI technologies by communicating openly with the public, regulators, and other stakeholders. Share information about your AI initiatives, successes, and challenges, and demonstrate your organization’s dedication to ethical AI practices. 

By actively addressing the challenges associated with generative AI implementation in these ways, you can position your organization to harness the transformative potential of this technology. 

Recognizing and overcoming organizational barriers 

Finally, it is essential to understand that integrating generative AI into your organization is not a short-term IT project, but a long-term strategic initiative, requiring dedication, focus, and investment. To ensure success, it is imperative you align the implementation of generative AI with your company’s overall strategy, instead of treating it as just another tech program. Attempting to run it on the sidelines without proper integration is likely to result in failure. Securing the sponsorship of the CEO and executive committee is essential for obtaining the necessary resources and support, driving the successful adoption of generative AI technologies within your organization. The journey may be demanding, but the rewards of harnessing the power of generative AI are undoubtedly worth the effort. 


Michael Watkins - IMD Professor

Michael D. Watkins

Professor of Leadership and Organizational Change at IMD

Michael D Watkins is Professor of Leadership and Organizational Change at IMD, and author of The First 90 Days, Master Your Next Move, Predictable Surprises, and 12 other books on leadership and negotiation. His book, The Six Disciplines of Strategic Thinking, explores how executives can learn to think strategically and lead their organizations into the future. A Thinkers 50-ranked management influencer and recognized expert in his field, his work features in HBR Guides and HBR’s 10 Must Reads on leadership, teams, strategic initiatives, and new managers. Over the past 20 years, he has used his First 90 Days® methodology to help leaders make successful transitions, both in his teaching at IMD, INSEAD, and Harvard Business School, where he gained his PhD in decision sciences, as well as through his private consultancy practice Genesis Advisers. At IMD, he directs the First 90 Days open program for leaders taking on challenging new roles and co-directs the Transition to Business Leadership (TBL) executive program for future enterprise leaders.

Ralf Weissbeck

The former Group Chief Information Officer and a member of the Executive Committee at The Adecco Group.

Ralf Weissbeck is the former CIO of The Adecco Group. He co-led the recovery of the 2022 Akka Technologies ransomware attack and led the recovery of the 2017 Maersk ransomware attack that shut down 49,000 devices and 7000 servers and destroyed 1000 applications.


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