Ahmed Tayeh, the Partner Sales Manager for Middle East and Turkey at Cloudera, says Generative AI can analyse vast datasets, uncover hidden patterns and generate insights that humans could miss
Tools such as ChatGPT have raised awareness and fuelled conversations about Artificial Intelligence and its potential business benefits. But so far, we have only seen relatively simple benefits from Generative AI. Typical use cases in business include chatbots, content creation, writing basic code and others.
Once companies are able to include more mission-critical data in a secure and trusted way, we will see the possibilities for wider business benefits from Generative AI, including drug discovery, personalized medical treatment plans or anomaly detection in sensitive sectors.
Why, according to you, should companies leverage generative AI?
Generative AI can analyse vast datasets, uncover hidden patterns and generate insights that humans could miss, hence enhancing the decision-making processes. It can automate repetitive tasks, such as content generation, data entry, etc., saving time and reducing operational costs. Companies can also leverage Gen AI for:
- Personalization: Gen AI can create personalized recommendations, content, and user experiences, enhancing customer satisfaction and engagement.
- Innovation: By generating new ideas, designs, or solutions, Gen AI fuels innovation. It’s a valuable tool for product development, creative industries, and research.
- Scalability: AI models can handle large volumes of data and tasks at scale, ensuring process consistency and reliability.
- Competitive Advantage: Companies that embrace Gen AI can gain a competitive edge by staying ahead in data-driven decision-making and customer-centric services.
- Efficiency: It can optimize resource allocation, supply chain management, and workforce planning, leading to cost savings and improved productivity.
- Risk Mitigation: Gen AI can quickly identify anomalies and potential threats in sectors like cybersecurity and fraud detection, enhancing security measures.
Generative AI offers a multitude of benefits, from improved insights and automation to innovation and competitive advantage, making it a valuable asset for forward-thinking companies.
What are the challenges companies face in terms of adopting and using Gen AI, and how can they be overcome?
When it comes to the adoption of Generative AI, challenges include data privacy concerns, costs, contextual limitations and regulatory compliance. Data privacy is a critical concern for every company as individuals and organizations alike grapple with the challenges of safeguarding personal, customer, and company data. Another significant challenge faced by LLM models is their lack of contextual understanding of specific enterprise questions.
Open-source models enable enterprises to host their AI solutions in-house within their enterprise without spending a fortune on research, infrastructure, and development. This also means that the interactions with this model are kept in-house, thus eliminating the privacy concerns associated with public cloud-based solutions.
Are companies aware of regional and global policies surrounding the use of Gen AI?
Yes, companies are increasingly aware of regional and global policies surrounding the use of Generative AI. Regulations such as GDPR in Europe and CCPA in California have raised awareness about data privacy and AI ethics. As AI continues to evolve, businesses recognize the importance of compliance with these policies to avoid legal and reputational risks. It is important to note that multinational organizations must ensure compliance with the local and regional directives elsewhere. This means that one company has several data privacy and sovereignty issues to consider.
The increase in availability and use of Generative AI has been fueling the economy, especially within the Middle East. A recent report highlighted that GCC countries, particularly Saudi Arabia and the UAE, stand to benefit significantly from the growth of Gen AI. The region could see substantial economic growth, with an estimated $23.5 billion annually by 2030.
This awareness of the economic potential reinforces the importance of complying with regulations like GDPR and CCPA, ensuring responsible and profitable regional AI deployment. Staying informed about and adhering to regulations around Gen AI is a priority for companies using Gen AI to ensure responsible and ethical AI deployment.
How can companies use their resources on using Gen AI to create a competitive advantage?
Companies can strategically allocate resources to harness Generative AI for a competitive advantage. According to a Cloudera survey, more than three-fourths (76%) of organizations in the Middle East store data in a hybrid environment, meaning they utilize both on-premises/private cloud and the public cloud. But two-thirds (66%) of respondents agree that having data sitting across different cloud and on-premises environments makes it complex to extract value from all the data in their organization. At a time when most enterprises are looking for benefits from Artificial Intelligence, they need to stay ahead of the curve by using modern data architectures.
What factors do companies need to consider before adopting Gen AI, such as having a centralised data strategy?
Data, as a strategic asset, demands its own strategy, such as an Enterprise Data Strategy. Early cloud adopters have realized that without an Enterprise Data Strategy, a cloud strategy alone can hamper the management, access, security, and governance of their data. In fact, early cloud adopters found that the move to the public cloud created data and analytics silos that were difficult to manage and far more expensive to run. With modern data architectures, organizations can derive more value from their data and simultaneously improve cloud costs.
How can companies experiment with Gen AI to predict the future of strategic workforce planning?
Businesses can build their own AI application powered by an open source LLM of their choice, with their data all hosted internally in the enterprise, empowering all their developers and lines of business – not just data scientists and ML teams – and truly democratizing AI.
The biggest problem is mostly not technology itself, but data strategy and organizational change. Organizational change is necessary in most circumstances to launch a data-driven culture and essentially a new paradigm for working. But this can create problems. Therefore, it’s vital to implement a change management process that encourages data-driven decision-making. To be successful, it also needs to appeal to employees at all levels to work towards this goal.