Over the last couple of years, we have seen organisations explore Artificial Intelligence across several industries, including financial services, telecommunications, government, education and other industries. I recently had a chance to sat down with technology leaders from Red Hat and Kenyan systems integrator Copy Cat who say the biggest challenge with AI is everything that has to come before and around it, and not just about access to AI itself,
Data, infrastructure, governance, security, skills and business processes all determine whether an AI initiative will be a beneficial business tool or just another technology experiment. If a business is investing in AI, it has to see the benefits of the invetment.
Andrew Hindshaw, Senior Partner Account Manager, Sub-Saharan Africa at Red Hat, said, “AI is becoming the cloud conversation of five years ago.” He added that the focus should be on identifying practical use cases for AI and not just blind implementation..
Nadeem Noordin, Director of Copy Cat Group, on his part added that the shift starts with a basic principle of considering the outcome before considering the technology.
One of the challenges organisations face is the temptation to take an existing business process and simply introduce AI into it. On this, Noordin says, “Don’t take an existing process and introduce AI into it. Re-engineer the entire process.”
This matters as AI can automate or accelerate an existing inefficient process. Noordin and Andrew added that AI will not magically make the underlying process better. With this in mind, technology partners should be ready to tell customers when AI is not the right answer at that particular moment.
The question for businesses should therefore be “What problem are we trying to solve and is AI the right way to solve it?” and not “Do we need AI?”
When a business rushes to adopt AI, this may draw attention to some of the less glamorous foundations of enterprise technology.
Organisations need to have good data, security controls, governance frameworks and clarity around where their data is stored and processed. Besides this, there is a need to understand the regulatory and data protection requirements within their jurisdiction.
These issues get serious when employees start using publicly available AI tools with company information. Even if the organisation has a good AI strategy on paper, it may still struggle to control how sensitive information is shared with external AI platforms. In this scenario, AI can easily expose weaknesses that may have been there before the technology was adopted.
Even if a business may have been able to manage Legacy systems, fragmented data, outdated processes and skills gaps for years without issues, it may find it harder to ignore those weaknesses the moment it tries to build AI capabilities on top of those foundations. This makes it clear that AI readiness is more about having a deep understanding of the organisation itself and not just about the technology.
On partnerships, Red Hat says its model is strongly channel-focused. Its partners play a critical role in translating technology into customer outcomes.
This is appealing to businesses as they have someone who can help connect the different pieces and not just have another vendor in their tech stack.
Most AI projects for businesses include infrastructure, cloud environments, data, security, governance, applications and business processes. A customer may not want to manage all those relationships independently, and this is why systems integrators and technology partners come in handy.
Partners should then understand the needs of the customer and not just deploy individual products.
Copy Cat aims to bring together technologies and expertise from different parts of the ecosystem. It does this while staying focused on the needs of the customers.
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