“Companies see Generative AI as potentially most transformative technology in a generation” according to Wavestone’s 2024 Data & AI Executive Survey. Impressively, as of today, 92% of Fortune 500 companies are using Open AI products, the GenAI front-runner (Financial Times Article, February 9, 2024), and 70% of US organizations are in exploration mode with GenAI (Gartner Poll, May 3, 2023). However, turning this exploration into meaningful transformation requires a strategic approach that prioritizes business value creation over following trends out of GenAI FOMO.

To navigate the challenges and maximize the transformative impact of GenAI, companies need to adopt a comprehensive and actionable business plan. This can be very difficult, and it is expected that many companies’ Generative AI initiatives will fail.

How can you see meaningful business impact from GenAI? What are the keys to a successful GenAI rollout within your organization? What main challenges and risks should be on your radar for GenAI projects?

5 Must-Haves for a Successful Generative AI 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:

1. Establish your AI readiness

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).

2. Foster a data-oriented culture

“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).

3. Build truly customized use cases

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.

4. Scale for mass implementation

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).

5. Protect yourself against all types of risks

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).

Additional note — Partner and capitalize on external resources as necessary

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).


Like any other technology, GenAI must be used the right way, in the right context, and with the right people. Through specific and relevant use cases, the actual execution needs to follow a comprehensive strategy that focuses on readiness, data, customization, scaling, and risk prevention—in other words, on long-term value. Currently, AI is not replacing humans, but it’s AI-enabled humans, tools, companies that are replacing the others. After all, at its core, whether GenAI is involved or not, businesses are of humans, by humans and for humans.

Wavestone can support you in your GenAI journey with strong capabilities for all these Must-Haves:

(1) Digital & IT Strategy: A dedicated practice at Wavestone can provide you with a 360-degree AI assessment.

(2) Data: Our comprehensive data capabilities can support you in all GenAI projects to deliver the quality data expertise and AI governance framework needed.

(3) Business Value Focus: From brainstorming GenAI ideas and developing use cases to assigning business value and transitioning to scaled projects, Wavestone is here to assist you throughout your entire journey (e.g., we are currently supporting clients on MS 365 Copilot as a Microsoft Strategic implementation partner).

(4) Cybersecurity Risk Mitigation: Our Global Cybersecurity practice can support you from the strategy to the implementations of your AI projects (cyber in AI and AI in cyber). Among other topics, we have deep expertise in evaluating and mitigating Language Model risks.

(5) Sourcing Optimization: Our Sourcing subject-matter experts can help you on very specific issues, including your transition from outsourcing to GenAI by finding the right partners and collaborators.

Wavestone supports data transformationsfor today’s leading enterprises.

Learn more