Primary challenges when implementing generative AI (And how to address them)

The research, guided by Pierre Marchand, Cognizant’s Chief Data Strategist, emphasizes the critical alignment of business and IT strategies. Utilizing frameworks like objectives and key results (OKRs), organizations can bridge the gap between strategic business objectives and IT initiatives.

Organizations face several primary challenges when implementing generative AI. Ensuring data quality and availability is crucial, as Gen AI systems require large volumes of high-quality data. This involves implementing robust data governance frameworks, using data cleaning and preprocessing techniques, and establishing clear data management policies.

Bridging the gap

Navigating generative AI integration with existing processes and IT infrastructure can be complex and resource intensive. Here, thorough planning and collaboration between IT and business units are crucial, as well as the use of middleware and API integrations to facilitate interoperability and seamless data flow.

Ethics and compliance

Ethical and compliance issues are also significant, requiring the establishment of an ethical AI board to oversee AI initiatives and ensure alignment with ethical standards, along with regular audits for compliance with legal and regulatory requirements.

Skills and expertise

To manage and develop gen AI solutions, employees need to be invested in by getting them trained and upskilled. We advise collaborating with external experts, hiring specialized talent, and encouraging continuous learning and professional development.

Cultural resistance and change management

Employees may resist adopting Gen AI due to fears of job displacement or reluctance to change established workflows. Developing a comprehensive change management strategy that includes clear communication about the benefits of Gen AI, involving employees in the implementation process, and providing support and training can help ease the transition.

Cost and resource allocation

Implementing Gen AI can be costly. Organizations can address this by starting with small, high-impact pilot projects to demonstrate value and build a business case for further investment, using the value of investments (VOI) framework to prioritize projects that offer the greatest strategic and financial benefits.

Scalability and maintenance

Scaling Gen AI solutions from pilot projects to enterprise-wide deployment can be challenging, as can ongoing maintenance and updates. Planning for scalability from the outset, using modular and flexible architectures, and establishing processes for regular maintenance, monitoring, and updating of AI systems are essential.

Bias and fairness

Finally, bias and fairness are critical issues, as Gen AI models can perpetuate or amplify biases present in the training data, leading to unfair or discriminatory outcomes. Implementing bias detection and mitigation strategies throughout the AI development lifecycle, using diverse and representative datasets, and regularly auditing AI outputs for fairness and equity are necessary to address these challenges.

The game of value

To learn more, read our comprehensive research report, "The game of value: In the AI-enabled organization."