Four Critical AI Priorities to Adopt for Success in 2024

Ryan den Rooijen
Ryan den Rooijen

Reflecting on my post a year ago, it is clear that while the economic climate did grow more challenging, we also saw an unprecedented maturation of Generative AI technologies. In 2023, we witnessed Microsoft’s $10 billion investment in OpenAI, Amazon’s $4 billion partnership with Anthropic, and Google's step-change development of their latest LLM, Gemini. Additionally, the global focus on AI regulation intensified, with the European Union introducing their AI Act and the United States implementing an Executive Order detailing extensive federal requirements for AI use​​. Yet, how many organisation saw the value of GenAI?

Looking forward to 2024, digital transformation will need to remain a top priority for businesses. In particular, there are four areas they should consider if they wish to stay ahead of their competitors in terms of AI value creation.

Recognising Future Skills Requirements

The outsize impact of AI on the workforce, particularly in white-collar professions, needs to be acknowledged. Knowledge workers such as creatives, lawyers, and finance experts are poised to see significant changes in their roles due to the mass adoption of AI technologies​​. Everything from content creation to insight generation and decision making processes will likely have to change.

Therefore, organisations must start proactively addressing these shifts by focusing on future skills requirements. This means retooling job descriptions and competency matrices to recognise that many tasks and traits might soon be entirely automated. Instead, adaptability will become even more central. Tailored training programs and partnerships with educational institutions will be key in preparing the workforce for the evolving job landscape.

Productionising GenAI Pilots

The transition from AI pilot projects to full-scale production will be a critical step for businesses in 2024. The era of experimentation with generative AI (GenAI) is morphing into one focused more on integrating these technologies into operations. This shift requires a strategic approach, aligning GenAI applications with business objectives to create tangible value and a competitive advantage.

In other words, GenAI is cool, but how is it impacting your bottom line? Effective integration of GenAI enables one to streamline processes, enhance customer experiences, and drive innovation. In this, organisations must navigate the complexities of deployment, including tuning, scaling, and managing models in production. Cross-functional collaboration becomes essential as one looks to harness the many datasets across the business for GenAI use cases.

2023 contains all my GenAI pilots. 2024 has my prod architecture. Photo by Kajetan Sumila.

Becoming Serious About Security

When a handful of data scientists are playing with dummy data, AI security can seem a bit of an unnecessary investment. But as models are increasingly trained with real-world data, or deployed in production, security needs to be prioritised. This entails protection of data and models against potential vulnerabilities and threats, as well as ensuring ethical AI practices, and regulatory compliance.

Particularly, as GenAI features are introduced into public applications, it is necessary potential risks associated with these types of services are mitigated. For example, there have been cases of models divulging private training data through malicious prompts. Proactive risk management and regular security assessments will be key in safeguarding AI-driven systems both against disinformation and misuse. Security cannot be an afterthought.

Embracing Data Quality

In the same vein, data quality is crucial for the effective performance of AI systems. Organisations must intensify their focus on ensuring the accuracy, relevance, and integrity of their data. This means shifting from simply collecting large volumes of data to actively managing it, through investing in both data management capabilities and the associated processes. After all, AI models' output quality often comes down to the input data's quality.

Fostering a culture that values data quality across the organisation will empower more informed decision-making and enhance AI-driven outcomes. Increasing awareness of consumer privacy and the rise of related regulations, such as those addressing automated decision-making, underscore the importance of responsible data handling​​. Data needs to be treated with respect.

As we step into 2024, these four priorities will shape the trajectory of organisations as they seek to operationalise AI capabilties. Emphasising skill development, integrating GenAI into operations, prioritising security, and focusing on data quality are essential steps to successfully navigating the AI opportunities. This year is set to be a transformative period for AI in business, and the gap between the winners and the losers is only set to grow.

Happy New Year! It is going to be a busy one.

– Ryan

Cover photo by Milad Fakurian.

Artificial Intelligence

Ryan den Rooijen

Chief Strategy Officer of Appsbroker CTS, the leading Google-dedicated consultancy. Formerly Chief Ecom & Data Officer.