5 Must-Haves for a Successful GenAI Implementation

To capture GenAI’s full potential for your organization, you must start with value hypotheses that are specific to your organization and its goals, rather than just following the herd. Here are our 5 Must-Haves for driving your Gen AI success:

Start with a readiness assessment of your organization. Before deep diving into highly technical discussions, start by reflecting upon your strategic goals. Human and financial investments in GenAI should be aligned with overarching business goals and clear roles and responsibilities.

Most companies who hesitate to implement GenAI today mention the lack of talent that combines AI proficiency and specific domain expertise. Assessing your organization is critical to knowing where and how to implement GenAI.

This might be counter-intuitive but Generative AI is not the solution to everything: based on the problem you have, you might need Machine Learning, Deep Learning, Predictive AI, low-code solutions, or a Business Intelligence tool, etc⁽¹⁾.

“Garbage in, garbage out” is particularly true for Generative AI: no quality output is possible without quality data, and there is no good AI without good data. In terms of AI adoption efforts, one of the recurring blocking points is the quality of data.

Curiously, one silver lining of GenAI is that it is making companies more data oriented as they are now more aware of data’s importance. Investing in targeted, right-sized data quality and governance is a winning strategy in the short and long term. A well-defined Data Strategy—with clear processes, points of control, strong data privacy, and good practices—will amplify the value added to your decision-making processes⁽²⁾.

The added value of GenAI is expected to reach trillions of dollars annually—for an amount roughly equivalent to the GDP of France or the UK—with an impact across all industry sectors (McKinsey Report, June 13, 2023). To benefit from it, you must identify problems worth solving based on your specific context: it is essential to prioritize actual business needs, be collaborative and customized instead of one-size-fits-all approaches, and hold transparent and ethical standards.

Continuous communication has been identified as a key success factor for our clients. Engaging stakeholders effectively requires transparency about the objectives, processes, benefits, and risks. By inviting employees to provide feedback and personal inputs, you create a dynamic and collaborative environment, enabling truly relevant use cases to surface through a grassroots approach.

Achieving GenAI implementation at scale is crucial for realizing the most value from the selected use cases in the long run. However, only 5% of market leaders have implemented GenAI in production at scale. Contrary to the previous generation of AI (i.e., ML, DL, predictive AI) that impacted a minority, GenAI has the potential to impact most employees.

That is why scale can deliver powerful outcomes through improved productivity, increased quality, enhanced creativity, and so on. And the return on investment (ROI) will highly increase if you can roll out broadly and quickly after the first successful pilot. The market momentum lies here today.

Another key success factor from our experience is flexibility: the use cases must be regularly refined by challenging the ideas and adapting their approach over time. Thus, people need to stay engaged until this stage and change management strategies must actively support them⁽³⁾.

From a regulatory perspective, AI and GenAI projects are under high scrutiny today. With the exponential development of AI in recent years, NIST’s AI RMF (Risk Management Framework) became an industry benchmark in the US. In the EU, the AI Act marks a major milestone, and it applies not only to European companies but also US companies operating in the EU. Compliance here is non-negotiable.

Ultimately, escalating security risks and the advanced AI cyber-threats expose companies to greater vulnerabilities. Adopting a security by design approach enables early identification and planning—ideally to be incorporated in a complete cybersecurity strategy. Cyber audits, red team penetration testing, and vulnerability scans, are some examples to evaluate your security, identifying gaps, and ensuring up-to-date and secure systems for your GenAI implementations⁽⁴⁾.

Based on your context and needs, leveraging external structures can reveal highly beneficial for your company. In addition to your internal upskilling, simultaneously, external resources can bring value by bridging AI talent gaps by providing immediate and impactful support thanks to mature AI specialists that have a specific domain expertise. Likewise, for specific technical capabilities, AI-powered Analytics platforms can sometimes be more relevant and powerful than internal solutions⁽⁵⁾.