The success of enterprise AI will not be determined by models or algorithms—it will be defined by how effectively organisations turn ambition into architecture. In regions like the Middle East, where national strategies have fast-tracked AI as a pillar of economic transformation, the spotlight has now shifted to execution: how to operationalise AI at scale, align it with measurable value, and embed it into real workflows.
This shift exposes a hard truth: many enterprises are pursuing AI without a clear understanding of where to begin, how to measure success, or what outcomes to target. Strategy often outpaces structure— and without disciplined use case identification, AI risks becoming just another siloed initiative.
“Business leaders want AI, but they don’t know how to start,” says Walid Gomaa, CEO at Omnix International. “Even if they start, they don’t know how to create a proper ROI to get money and get funding… unless you have a customer who’s ready to fund without ROI, then the implementation of AI is going to be a challenge.”
This ambiguity around return on investment is one of the main reasons AI initiatives stall, Gomaa explains. Organisations may launch pilot projects based on industry hype or vendor promises, only to realise later that business value was never clearly defined. “Sometimes, they say AI didn’t help. But the reality is, maybe the wrong use case was implemented—or there was no proper ROI model to begin with.”
Bridging this disconnect requires building a business-driven foundation—starting not with a proof of concept, but with a clear monetisation strategy. It calls for solutions that shift the focus from technical experimentation to commercially grounded execution. Services like Omnix’s AI Monetisation offering have emerged to address this need.
“This service is only about making sure that you break the gap between the business users and IT users,” says Gomaa. “The outcome is a clear strategy for AI, clear implementation of use cases, clear ROI—and it’s all coming from business users.”
By transforming fragmented efforts into enterprise-aligned roadmaps, the service enables organisations to approach AI not as a siloed project, but as a strategic capability— embedded with purpose, prioritised for value, and engineered for scale.
The need for an AI talent strategy
AI is reshaping how work gets done— but without the right skills, even the most advanced technology will fall short. Gomaa sees two layers to this challenge. On one hand, there’s a shortage of deep AI expertise, such as data scientists, machine learning engineers, and AI strategists. On the other, there’s a readiness gap across business functions.
“You need a data engineer, data scientist, strategist. They were not there before… now these guys are becoming very important to the implications,” he adds. “To find them and afford them is a question… because they are very few and very expensive.”
This cost barrier has led some organisations to delay their AI ambitions. But Gomaa argues that the solution isn’t simply hiring—it’s about enabling existing teams to work effectively alongside AI.
Whether it’s a doctor interpreting AI-assisted diagnostics or an engineer responding to a predictive maintenance alert, humans remain central to the process. Gomaa stresses that the future lies in human-machine collaboration, not replacement.
“AI is here to enhance the skills we already possess—it’s not about replacement. It’s about partnership. In this hybrid model, AI handles part of the work, while human expertise completes the picture. The true power of AI lies in collaboration, not substitution,” he says.
The unseen barrier to AI success
While talent and strategy are essential, AI adoption will fail without one critical ingredient: clean, consistent data.
“If the data is inaccurate, the whole thing will collapse. You have to have data management, data governance, and make sure that the data is accurate.”
Data inconsistencies are especially common in large organisations, where conflicting datasets reside across departments. This makes it difficult to determine which version is accurate—and feeding this ambiguity into an AI system only compounds the issue.
“In large organisations, you can find the same data coming from different sources, and they are all different. So which one of them is the real data?”
To address this, Gomaa advocates for building data governance frameworks early in the AI journey— before experimentation begins. Data cleansing, integration tools, and quality controls must be treated as foundational infrastructure, not afterthoughts.
Augmentation in action
Beyond the headlines about AI replacing jobs, Gomaa sees a more pragmatic and valuable application: intelligent augmentation. Tools like conversational agents and copilots demonstrate how AI can accelerate productivity while keeping human judgement in the loop.
“You need to analyse the feedback [from AI] and take action on that… It’s all about making my work better, not replacing my work.”
Even in areas like predictive analytics, machines offer probabilities—but the decision ultimately rests with the human.
“Machines cannot take a decision. An engineer will take a decision based on the input, but now he’s adding more quantitative data to base his decision, rather than depending only on his 80/60.”
This is the model of the future: AI as an advisor, a companion in problem-solving—not a standalone authority.
The way forward
As enterprises move beyond experimentation, the focus is shifting from generic AI tools to purpose built, context-aware solutions. Off the-shelf AI tools can demonstrate potential, but domain-specific use cases are what ultimately unlock real business value.
“ChatGPT is a generic engine. But the real value is in understanding— how can I use the ChatGPT engine for real estate? How can I use it for manufacturing? Then this becomes the use case specifically for this industry,” says Gomaa.
This perspective will shape Omnix’s AI strategy going forward, with a focus on developing solutions tailored to industry-specific needs, compliant with regional regulations, and capable of running closer to the data—whether on laptops, desktops, or secure on-premises infrastructure.
Gomaa believes this direction is especially relevant in the Middle East, where national AI strategies and data sovereignty mandates are heavily influencing how and where AI can be deployed.
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