AI readiness may matter more than technology as organisations face information challenge

AI readiness has emerged as a more critical factor than adopting the latest artificial intelligence tools, with experts arguing that organisations must first improve how they manage information before expecting meaningful gains from AI. While businesses worldwide are racing to deploy platforms such as ChatGPT and Claude, many overlook whether their own systems are prepared to support AI-driven decision-making.

Rather than being a purely technological challenge, AI implementation increasingly exposes weaknesses in organisational data, fragmented knowledge and outdated management practices. Without reliable information, even the most advanced AI systems struggle to deliver accurate recommendations or business value.

Many companies continue to ask which AI model to adopt and how quickly they can implement it. However, industry experts suggest a more important question is whether organisations have built the information infrastructure required for AI to function effectively.

Data quality determines AI success

AI systems rely on large volumes of structured and reliable information to support business decisions. In manufacturing, for example, AI can recommend workforce allocation, production scheduling and maintenance planning by analysing employee skills, equipment performance, inventory levels and customer demand.

Similarly, in customer service operations, AI can route calls by evaluating customer history, agent expertise, service quality, workload and the likelihood of customer retention. These capabilities, however, depend entirely on accurate and accessible organisational data.

If customer records are incomplete, employee skills remain undocumented or operational systems operate in isolation, AI has little reliable information to optimise.

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Knowledge remains locked inside organisations

A major obstacle for many businesses is that valuable knowledge often exists outside formal systems. Critical information is frequently scattered across enterprise software, spreadsheets, emails and messaging platforms such as WhatsApp, while the most important operational expertise often resides only in the experience of senior managers.

Production managers, for instance, may know which technician performs best during equipment failures or which supervisor consistently delivers under pressure. Unless this knowledge is documented and shared across the organisation, AI cannot use it to improve operational decisions.

Experts argue that AI cannot analyse information that has never been recorded or integrate knowledge that remains trapped in individual experience.

Management, not technology, is the real challenge

Businesses that achieve the greatest success with AI are often those that have already invested in strong management systems, documented processes and integrated information platforms.

Organisations with disciplined data governance allow AI to access reliable operational information, while businesses relying on informal practices and fragmented records often struggle to realise similar benefits despite using the same technology.

This challenge is particularly significant for developing economies such as Pakistan, where experienced managers have historically compensated for weak information systems through personal knowledge and informal decision-making.

Building stronger foundations

Experts believe organisations should focus less on acquiring new AI tools and more on strengthening their internal information management. Key questions include where organisational knowledge is stored, whether operational data is consistently updated, and how much critical information depends solely on individual employees.

As artificial intelligence becomes increasingly integrated into business operations, organisations that invest in structured knowledge, reliable data and connected management systems are expected to gain the greatest long-term competitive advantage.

Ultimately, experts argue that successful AI adoption depends not only on technological innovation but also on an organisation’s ability to manage, organise and share what it already knows.