The All-In GenAI Gamble: Who Should Bet Big And Who Should Check?

The rapid evolution of Generative AI (GenAI) is transforming business operations and innovation. What started as a niche technology has now become a driving force across industries, from chatbots to content creation. 

With LLMs like GPT-4o and Llama, AI is expanding creative and analytical possibilities, propelling technology into the mainstream and fueling both excitement and skepticism. 

On one hand, companies like Nvidia have seen their stock prices soar, AI-related job postings on LinkedIn have increased 21-fold since November 2022, and AI startup funding surged from $22 Bn in 2022 to $36 Bn in 2023. 

On the other hand, according to AWS, only 6% of GenAI solutions are in production and  McKinsey notes that only 21% of those solutions are integrated across use cases

These mixed signals leave business leaders questioning whether to bet big on GenAI or adopt a more cautious approach.

Investment Dilemmas For Business Leaders

For business leaders, particularly in mid-to-large companies, deciding to invest in GenAI is fraught with uncertainty. They often question the necessity of GenAI when their existing machine learning and analytics solutions seem adequate. 

Moreover, the challenges of hiring AI engineers, building scalable teams, managing costs, and ensuring reliable insights add further complexity. A key concern is identifying which business use cases are truly suited for GenAI. 

Recommendations For Business Leaders 

• Business Metric – A recommended approach involves creating a small, internal GenAI task force composed of data scientists, engineers, and software professionals. This team can start by identifying a relevant business use case and conducting a proof of concept (POC). The success of this POC should be closely tied to measurable business metrics, and only if it demonstrates significant impact should the solution be scaled for production. 
• Data Quality – The success of a GenAI project heavily depends on the quality of enterprise data; thus, it’s crucial to streamline the organisation’s data strategy before embarking on the GenAI journey. 
• Governance & Monitoring – To navigate these challenges, business leaders should establish a robust GenAI governance and monitoring plan. This ensures that GenAI outputs are reliable, consistent, cost-effective, and compliant with the company’s information security standards. 

In essence, for business leaders, the strategy should be to ‘Bet and Check’, starting small, identifying a business metric as success criteria and scaling only when the value is clear.

Consulting Challenges

AI practitioners, especially in consulting, face their own set of challenges. While traditional data engineering, data science, and analytics projects still constitute a significant portion of their revenue, the rise of GenAI presents both a threat and an opportunity. 

The fear of missing out (FOMO) on the GenAI wave is palpable, yet there’s a risk of losing credibility if they invest too heavily without clear returns. Consulting firms are expected to be trusted advisors and must navigate the hype and reality of GenAI before their clients do.

The GenAI landscape is dominated by hyperscalers like Azure, Google Cloud, and AWS, each with its own set of challenges and maturity levels. Choosing the right partner is crucial but far from straightforward. 

Recommendations For Consulting Partners

• AI-First Culture – To succeed, consulting firms need to embrace an ‘AI-first’ culture, establish GenAI centers of excellence, and start with targeted POCs. 
• Market Differentiation – By identifying key business domains and use cases where GenAI can add tangible value, they can develop differentiated solutions that showcase their expertise and build trust with clients. The goal should be to solve real-world business problems, avoiding the temptation to create “shiny toys” that impress but lack substance.
• Strategic Alliances – Strategic partnerships with the right hyperscaler can significantly enhance a consulting firm’s ability to deliver impactful GenAI solutions. 

For consulting partners, the advice is to ‘bet big in pockets’, focusing on specific areas to build market differentiation and credibility.

Skill Evolution For Engineers

For data professionals—data scientists, engineers, and analysts—the pace of GenAI development is both exciting and intimidating. The rapid automation of tasks raises concerns about obsolescence, and the steep learning curve associated with GenAI skills adds pressure. 

These professionals must decide whether to invest in learning these new technologies or risk falling behind. However, while GenAI may require specific skills, the foundational principles of software engineering and programming remain essential. 

Recommendations For Engineers

• Core Competency – Data professionals should continue to hone their software engineering and programming skills, as these are still vital in the GenAI era.
• Expand Knowledge Horizon – Engineers should focus on expanding their expertise both ‘left and right’, meaning they should deepen their understanding of the business use cases they support and the insights their work generates. For instance, a data engineer should not only build data pipelines but also understand the business context of the data and how it is used to generate insights that add value. This holistic approach is critical in the GenAI world, where engineers who fail to broaden their skills risk becoming obsolete.
• Beginner’s Mindset – Adopting a beginner’s mindset, as embodied in the Japanese concept of Soshin, is crucial. This mindset fosters continuous learning and adaptability, ensuring that professionals remain relevant even as technology evolves. 

For data professionals, the clear recommendation is to ‘Go All-In’ on GenAI, embracing the opportunity to learn and grow with the industry’s advancements.

The question of whether to bet big or check on GenAI is one of the most pressing issues facing businesses today. In a world where GenAI could be the next big wave or just another hype cycle, informed decision-making is critical for success.

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