According to IDC, the Middle East region will see the fastest AI Spending growth rate worldwide over the coming years reaching 30 percent CAGR growth till 2026. In this context, the emergence of Gen AI has garnered the Middle East attention, and regional business leaders are understandably eager to tap into the power of this new technology.
Gen AI is unique in its pre-training of foundation models, simplifying AI development and shifting the focus to domain expertise, where thriving businesses excel. This evolving landscape allows every company to leverage its data and business knowledge to create powerful and distinctive Gen AI solutions. The realm of innovation and differentiation has expanded, leading many of our Middle East clients to be ambitious to embrace the potential of Gen AI.
As Middle East businesses embrace Gen AI, evaluating their enterprise architecture becomes a crucial first step. Scaling up Gen AI requires careful considerations of security, responsibility, cost-effectiveness, and delivering tangible value to the business. To ensure readiness for Gen AI implementation, business leaders should ask themselves seven key questions.
1. Which foundation model should we use?
The landscape of Gen AI models and vendors is expanding rapidly. Pure-play vendors like OpenAI and Cohere offer next-generation models as a service, built on extensive research and trained with publicly available data. Additionally, open-source models accessible through platforms like Hugging Face have matured significantly. Cloud hyper-scalers are also entering the field by collaborating with pure-plays, adopting open-source models, and offering comprehensive services.
While all options are increasingly viable, cost-effective, and accessible, carefully considering their fit with the organisation’s needs and requirements is crucial.
2. How should I make these models accessible to us?
There are two main options when it comes to deploying Gen AI models. The first is to have complete control by deploying the models on your own cloud infrastructure or private infrastructure. The second option is to opt for speed and simplicity using a managed cloud service from an external vendor.
Each option has advantages, but choosing complete control comes with additional complexities. These include managing the infrastructure for the models, implementing version control, developing skills and talent, and creating full-stack services for easier adoption. Specialised infrastructure choices like Nvidia GPUs or integrated appliances optimised for model computation workloads should also be considered.
3. How will we adapt the models to our own data for consumption?
To maximise the business value of Gen AI, it is crucial to leverage proprietary data to enhance accuracy and performance. There are three approaches – buy, build, or boost.
The first option is to purchase a fully pre-trained model and utilise in-context learning techniques to obtain responses with your own data. The second option involves taking a mostly pre-trained model and enhancing it by incorporating fine-tuned data. Lastly, organisations can develop their own models from scratch or further pre-train existing open-source models using their infrastructure and data.
4. What’s the overall enterprise readiness?
It is crucial to consider integration and interoperability frameworks for seamless full-stack solutions to ensure that foundation models meet the enterprise’s security, reliability, and responsibility requirements.
Building trust in AI within the organisation is also essential, requiring careful consideration of the implications of AI, especially for sensitive business functions. While Gen AI vendors offer some built-in capabilities, developing controls and mitigation techniques is advisable. In addition, reviewing AI governance standards and operating models can safeguard enterprise security.
5. What about the environmental impact?
With an increased focus on the sustainability agenda in the Middle East, it’s important to be aware of the environmental implications depending on whether you choose to buy, boost, or build foundation models. Foundation models, even when pre-trained, can have substantial energy requirements during adaptation and fine-tuning. This becomes particularly significant when pre-training or building your own model.
6. How can we industrialise Gen AI app development?
After deploying a foundation model, companies must consider new frameworks for accelerated application development. Vector databases or domain knowledge graphs that capture business data are crucial in developing valuable GenAI applications. Additionally, prompt engineering and industrialisation enable the creation of efficient templates aligned with specific business domains.
An orchestration framework is crucial in coordinating multiple components and services for application enablement. Although emerging frameworks and services exist, they are still evolving. Human-in-the-loop workflow components are vital for diverse usage scenarios.
Industrialising feedback from domain experts significantly accelerates progress, and solutions like Scale.AI leverage reinforcement learning with human feedback to streamline data labeling and modeling edge cases.
7. What do we need to operate Gen AI at scale?
To ensure smooth operability in Gen AI applications deployment, organisations should adapt Machine Learning Operations (MLOps) frameworks to incorporate Language Model Lifecycle Operations (LLMOps) and Gen AIOps considerations, aligning with changes in development, deployment, testing, model management, monitoring, prompt management, and data/knowledge management.
The MLOps approach must evolve to cover the entire application lifecycle for foundation models. As Gen AI advances towards AutoGPT, AI-driven operations will automate tasks like productionising, monitoring, and calibrating models to meet the business’s service level agreements (SLAs).
Balancing scale and individual impact
Addressing these architecture questions will enable Middle Eastern organisations to scale Gen AI efficiently and effectively, driving successful adoption across the enterprise. However, managing the impact on individuals is equally important, requiring flexibility and responsiveness from business and technology leaders. This adaptability is crucial to fully harness the value of the evolving capabilities surrounding foundation models in the dynamic field of Gen AI.
