How to avoid the 7 most common pitfalls in generative AI development

Today’s busy enterprise CTO wears many hats - technology architect, gatekeeper of budgets and, now, strategic advisor on digital transformation for business growth. The latter role is now centered around the most revolutionary technology of the decade: generative AI. 

Market opportunities abound for generative AI as the next generation of consumers - the tech-native Generation Alpha, who have never known a world without smartphones and internet - nears the age of entering the workforce and making purchases.  

More businesses are also developing internal applications for generative AI, to automate repetitive tasks, enhance knowledge sharing and boost productivity

A late 2023 study found that 71% of companies are already using AI, with almost all deployments taking 12 months or less - and returns on investment realized within just 14 months on average.. 

For organizations at the beginning of the transformation journey, though, maximizing value from the technology requires foresight and careful consideration of the barriers to successful implementation - addressing strategic challenges on the horizon before they become roadblocks. 

• Talent acquisition and retention: Attracting and retaining the highest skilled, specialized developers, engineers and cybersecurity professionals is top priority for CTOs. 79% of CTOs plan to significantly increase headcount in the coming year.

But be wary: as many businesses demand a full return to office working for most employees, tech experts place a heavy premium on remote perks and flexible schedules (desired by 63% and 77% of IT professionals respectively). As a result, 69% of CTOs - up from 47% in 2023 - are looking to upskill and reskill existing employees rather than hiring externally. 

• Securing budget: Responsibility lies with CTO to justify the business case for every investment in digital transformation. Technology leaders who can act as a strategic advisor to the board and simplify the most technical concepts for any audience, will be able to justify investments with fewer barriers than other .
• Tackling technical debt: According to a 2024 Deloitte study of over 300 business leaders, 82% say their companies have missed cost reduction targets. The number of respondents who named “challenges with technology infrastructure to meet new internal business conditions” as a barrier to success rose from 31% in the last edition of the survey to 50% in 2024. Clearly, legacy systems still pose a challenge for many businesses - one of the most important issues to consider in a digital transformation programme centered around generative AI. 
• Cybersecurity threats & corporate risk: Rapid adoption of generative AI presents new security vulnerabilities. Integrating generative AI into critical systems poses the risk of data leaks, biased and discriminatory systems or compromised human decision-making through poor information security and opaque algorithm processes. 

With cybersecurity taking a substantial share of IT spend in 2023 - around 20% on average - CTOs need to work closely with cybersecurity teams from project inception to ensure vulnerabilities are minimized and critical systems are protected from risk. 

• Addressing ethical issues: CTOs also need to evaluate the potential biases, fairness, and transparency of AI-generated content and decision-making, particularly with the use of sensitive data, to ensure legal and ethical compliance with privacy regulations.
• Regulatory risk: 90% of CROs from multinational enterprises and organizations surveyed by the World Economic Forum in 2023 said efforts to regulate AI need to be accelerated.

More than half are also planning to conduct an audit of the AI already in use in their organizations to assess its safety, legality and “ethical soundness”, although some said senior management were unwilling to view AI as a business risk.

• Data Quality and Availability: Generative AI models rely heavily on high-quality, unbiased data sets for training.  It must be free from errors, inconsistencies, and biases is crucial for training AI models — and for generating outputs businesses can rely on for effective decision-making. Poor data quality can lead to incorrect, biased, or unreliable outputs that can disrupt business operations or cause ethical and legal issues. 

Most businesses however, don’t have the required volume of high-quality data, impacting on the effectiveness of generative AI applications.

Maximizing the impact of generative AI 

The new wave of AI presents a wealth of opportunities, but overcoming barriers to success requires careful planning. By addressing talent gaps, securing budgets, and mitigating technical debt, CTOs can lay the groundwork for higher productivity and bigger market impact. Prioritizing cybersecurity, ethical considerations, data quality, and regulatory compliance will ensure a smooth journey. 

With a thoughtful approach, generative AI can become a powerful engine for innovation and, ultimately, increased market share.