The 8 most common mistakes when using ChatGPT in companies

The adoption of generative Artificial Intelligence (AI) in the business world is increasing rapidly, but this process is not without its errors and challenges. This article discusses the most common mistakes and provides recommendations for avoiding them.

These are the 8 most common mistakes when using generative AI in companies

1. Misunderstandings about the role of generative AI : Many companies see generative AI as a substitute for human skills, rather than as a support tool. It is important to highlight the importance of considering AI as a complement that enhances human capabilities, especially in routine coding tasks, improving productivity and creativity.
2. Lack of preparation and clear policies : Less than a quarter of companies have established policies for the use of generative AI by employees. A lack of policies and adequate preparation can lead to the hasty adoption of these technologies, increasing the risks of inaccuracies and poor decisions.
3. Focus on cost reduction rather than innovation : The companies with the best AI performers (the "AI high performers") focus less on reducing costs and more on creating new businesses and revenue streams through AI. This innovative approach contrasts with that of companies that see AI primarily as a tool to reduce expenses.
4. Strategic and operational challenges : Companies with high AI performance face challenges related to model and tool management, such as monitoring the performance of models in production and training them. On the other hand, companies that are less advanced in AI struggle with more fundamental elements, such as defining a clear vision for AI and finding the necessary resources.
5. Changes in talent needs and job roles : Generative AI adoption is driving Significant changes in job roles . Companies are hiring more data engineers and machine learning specialists. In addition, new needs, such as prompt engineering, are emerging to support the adoption of generative AI.
6. Organizational Change Readiness and Training : The adoption of AI is expected to lead to substantial change in the workforce, with an increased focus on training and role redesign rather than reducing the size of the workforce.
7. Limited impact on overall AI adoption : Despite the rapid advancement of generative AI tools, they have not driven a significant increase in overall AI adoption. Adoption remains limited in scope, primarily focused on product and service development and service operations.
8. Expectations of increased investment in AI : Most organizations plan to increase their investment in AI in the coming years, anticipating significant returns in business areas where AI is used.

Strategy first, and then people and technology is the key to not making mistakes when using generative AI in companies

The implementation of generative Artificial Intelligence (AI) in companies requires a well-defined strategy that prioritizes the Business Strategy and then consider people and technology. To maximize the potential of generative AI, companies must focus on three key areas: deploying generative AI tools for productivity gains, reshaping processes and functions to improve efficiency and effectiveness, and inventing new customer experiences, services, and business models.

These actions are not mere small-scale experiments, but require an organization-wide commitment to integrate generative AI into all areas, including budgets, processes, roles, and culture. It is important to follow the principles of the Responsible AI and take into account productivity outcomes, cost-benefit trade-offs, requirements for success, risks of unintended consequences, and implications for operating models.

It's important to anticipate the impact on the workforce, as individual tasks and responsibilities will change as generative AI is integrated into business units. Companies must be prepared to create new roles, reallocate budgets, and reflect the use of generative AI in performance reviews .

Business and functional leaders should guide the implementation of generative AI, defining a clear vision for the use of AI in the enterprise, setting boundaries, and guiding a series of pilots across multiple parts of the organization to identify what works, and come up with a systematic plan to scale the most effective pilots.

It's crucial for companies to focus on a few high-value, transformative projects, as the benefits of generative AI across hundreds of individual use cases aren't always visible to leaders. However, the impact is usually clear when the approach is applied deploy, remodel, and invent fewer high-value projects . Once the most promising opportunities are scaled, the impact typically reaches between 5% and 10% of total revenue.

To capture the value of generative AI at scale, companies must maintain a dedicated focus on people through workforce planning, change management, and extensive training. It is important to assign 10% of the effort in AI to algorithms, 20% to the underlying data and technology, and 70% to people and processes.

For a successful implementation of generative AI in companies, it is essential to prioritize a well-defined strategy, followed by a focus on people and technology. This involves comprehensive organizational engagement, anticipation and adaptation to changes in the workforce, and a focus on high-value, transformative projects.