FEATURE: TRANSFORMING THE ENTERPRISE WITH AI linked to tangible business goals, such as reducing manual errors or improving decision-making, enthusiasm can fade. Successful AI adoption requires a clear roadmap, executive buy-in, and a willingness to experiment and iterate.
Amid the hype and rapid pace of change, executives feel pressured to adopt AI quickly to remain competitive, yet many are unclear on the best approach. Poor data quality, legacy systems resisting integration, and difficulty in managing organisational change can hinder progress.
“ Many underestimate the need for strong leadership and a clear AI vision to guide efforts. While some projects will fail, swift learning and ongoing investment are essential for long-term, transformative success,” says NTT DATA’ s Diraz.
A shortage of skilled talent presents an additional challenge. Unclear business objectives that are not aligned with organisational goals result in AI project failures, while communication gaps between IT and business units can lead to misaligned expectations and delays.
“ Ethical concerns, data privacy issues, and regulatory requirements add further complexity. Ultimately, insufficient executive sponsorship, weak change management practices, and a failure to integrate AI into core business processes underscore the need for a cohesive, strategic approach,” says Omnix’ s Gomaa.
AI adoption requires a high degree of agility. AI capabilities are forcing enterprises to rethink multiple aspects of their operational model.
AI tools are allowing us to reach new levels of productivity, but those tools are not perfect and need to be utilised in their proper context. One failure mode is lack of workforce training, which leads to ineffective use of the tools.
Another failure mode is an over-reliance on AI which is prone to hallucinations. This is a fundamental shortcoming of language models, and safeguards need to be in place to catch it. LQMs are not vulnerable to language-based hallucination.
“ AI opens up new kinds of security vulnerabilities that need to be addressed. Expertise in AI-driven cybersecurity, cryptographic agility, and AI governance is essential for enterprises levelling up in AI,” says SandboxAQ’ s Leichenauer.
Poorly defined goals and success metrics typically translate into lacklustre results, and the shortage of qualified professionals keeps full-scale adoption at bay. Most teams overestimate the effort required to integrate AI agents, robots, and humans into complex processes, leading to cost overruns and missed deadlines.
“ Without strong leadership support and open communication, AI initiatives can stall, as scepticism grows and essential stakeholders lose confidence in the transformative potential of the initiative,” says UiPath’ s El Zarka.
With increasing reliance on AI, companies worry about data misuse, algorithmic bias, and safeguarding sensitive information. The talent and skills gap presents a significant hurdle, as there is a shortage of qualified professionals in the region, even globally, with expertise in AI and machine learning.
“ These issues contribute to failures along the AI adoption roadmap, as organisations struggle to balance innovation with ethical, security, and talentrelated concerns,” reflects Zoho’ s Paramasivam. p
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