Scaling Up Your GenAI Project: From Concept to Reality

Generative AI: Ambitions vs. Reality

Many companies are eager to harness the power of Generative AI (GenAI). However, translating these ambitions into actionable outcomes is a hurdle that many organizations struggle to overcome.

According to recent research from Google Cloud, published in August, 47% of 2,508 companies with revenues over $10 million successfully transitioned from conceptualizing GenAI to actual implementation within a six-month period. Remarkably, 34% of these firms achieved this milestone within three months. Furthermore, more than half of the participants indicated a noticeable increase in annual revenue, ranging from 6% to 10%.

Yet, a significant portion of businesses finds themselves mired in what Google terms “pilot purgatory.” In the UK, 38% of respondents admitted they had not yet moved GenAI into production, highlighting challenges associated with scaling deployments.

Aligning Strategies with Business Outcomes

To move beyond pilot purgatory, organizations need to align their AI strategies with tangible business outcomes. But what does this alignment entail?

For starters, securing executive buy-in is paramount. Dell Technologies, in partnership with AI chipmaker Nvidia, established its Dell AI Factory earlier this year to aid companies in integrating GenAI applications into their workflows. “Aligning GenAI strategies with business goals goes beyond mere fascination with technological hype,” explains Steve Young, UK senior vice president and managing director at Dell Technologies. “It requires a nuanced understanding of how these technologies can genuinely enhance business performance.”

A compelling case for this alignment can also be seen with Expedia. In summer 2024, the travel tech company unveiled its AI assistant, Romie, on EG Labs—a platform where users can experiment with innovative products. Although Romie is still in its initial testing phase, the company is proactively gathering user feedback to refine features like AI search, as noted by Shiyi Pickrell, Expedia’s senior vice president of data and AI.

Building a Strong Testing Culture

For GenAI projects to scale effectively, a robust test-and-learn culture is essential. Leaders must foster an environment where experimentation and learning from failures are normalized. Without a clear understanding of what they want from GenAI, companies risk misdirection.

Acquiring full support from C-suite executives is crucial. Despite the buzz surrounding GenAI, many decision-makers remain hesitant, either because of entrenched traditional views or doubts about ROI. “To gain the confidence of reluctant executives, it’s vital to demonstrate how GenAI can address real business challenges,” advises Kristof Symons, CEO International at Orange Business.

In March 2024, Orange Business launched two GenAI products tailored for French enterprises, furthering the conversation about executive commitment. “When leadership commits to AI initiatives, it sends a unified message: this is vital for our collective success,” Symons adds.

Prioritizing Data Quality

As companies rush to implement GenAI, many overlook the critical aspect of data quality and management. This oversight often results in pilot projects falling short.

“Data quality is king,” states Symons. For GenAI algorithms to function reliably, they must rely on accurate, relevant data. Thus, assembling a knowledgeable team skilled in AI, machine learning, and data science is essential. Without such expertise, organizations may struggle to convert their concepts into successful implementations.

Furthermore, data privacy and security concerns cannot be ignored. The implications of mishandled data can lead to legal ramifications. Therefore, a comprehensive risk mitigation strategy is crucial to ensure compliance with regulations while integrating GenAI solutions into existing systems seamlessly.

Training and Empowerment

A successful GenAI rollout requires the support of all employees, not just those in technical roles. Leaders must “demystify GenAI as a tool for everyone,” emphasizes Symons.

This democratization means ensuring that the technology is accessible to all employees. When only select individuals have access, it can foster resentment and resistance among others. Therefore, promoting AI equity within the organization is paramount to mitigate disparities.

Both Symons and Young underline the importance of offering education and training for those less familiar with GenAI. Empowering the workforce through knowledge will enhance their understanding of the technology’s benefits, which can ultimately contribute to the success of pilot projects. However, there may still be resistance along the way.

Five Steps to Generative AI Success

To keep GenAI projects on track and avoid pilot purgatory, organizations must follow several steps:

1. **Define Clear Objectives:** Understanding the specific goals associated with GenAI implementation is essential. Pursuing the technology without concrete targets can lead to wasted resources.

2. **Engage C-Suite Executives:** Addressing concerns about costs and returns can help overcome executive hesitance. Present compelling evidence showing how competitors have benefited from adopting GenAI.

3. **Focus on Data Management:** Robust data management and sound data practices are critical for successful implementation. Assemble qualified teams to ensure the effective scaling of pilot projects.

4. **Provide Comprehensive Training:** Employees need to feel assured that GenAI will complement their roles, not replace them. Offering training opportunities can bolster their confidence in utilizing this technology.

5. **Foster a Culture of Experimentation:** Embrace the risks inherent in experimental technologies like GenAI. A culture of constant learning will help organizations adapt to both business challenges and technological advancements.

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