Trust and Transparency Drive AI Adoption in the Middle East, Says NCST’s Shereen Faisal
Artificial intelligence (AI) is increasingly becoming a central topic in boardrooms throughout the Middle East. However, many organizations continue to face challenges in its adoption. Shereen Faisal, Project Manager and AI Data Scientist at the Nasser Centre for Science and Technology (NCST), highlights that the reluctance surrounding AI is not primarily due to the technology itself. Instead, it is rooted in concerns about trust, transparency, data protection, and the effects on established workflows. These factors often overshadow discussions about the technology’s potential.
The Trust Barrier
Faisal emphasizes that trust is essential for successful AI implementation. Organizations need assurance that AI systems will yield reliable outcomes, operate securely, and comply with regulations. For end users, the stakes are personal; they are concerned about data privacy and the preservation of human support in decision-making processes. Confidence in AI increases when it demonstrates practical, measurable benefits transparently.
According to Faisal, “Trust is built through experience, good governance, and consistent results.” She underscores the necessity of fostering a culture of accountability and transparency within organizations.
Building Transparency
To foster trust, organizations must engage in honest communication regarding AI’s capabilities and limitations. A common mistake is to present AI as a cure-all, failing to clarify the specific contexts in which it excels or falters. Faisal advocates for clear explanations about what an AI system is designed to achieve, how it supports decision-making, and where human judgment remains crucial.
Transparency should also extend to the development and governance of AI systems, covering aspects such as data quality, security, fairness, and ongoing monitoring. Faisal asserts that building trust is a continuous process, not a one-time effort. When transparency becomes embedded in an organization’s culture, confidence in AI is likely to follow.
The Role of Pilot Projects
Faisal highlights the importance of targeted pilot projects in overcoming resistance to AI adoption. These initiatives enable organizations to assess not only the technology but also its integration into existing workflows. By creating a low-pressure environment for experimentation, organizations can refine processes and success metrics while keeping the scope manageable.
Successful pilot projects can cultivate internal advocates. When employees and business leaders observe tangible improvements, they often become champions for broader AI adoption. Each successful initiative enhances internal knowledge and nurtures a culture more receptive to AI-driven transformation.
Setting Realistic Expectations
Business leaders play a pivotal role in shaping perceptions of AI. Faisal advises that AI should be viewed as a journey of continuous improvement rather than a one-off solution. Unrealistic expectations can arise when AI is marketed as an instant fix, while in reality, value is typically realized incrementally.
Leaders should establish clear, measurable objectives linked to specific business challenges, tracking outcomes such as improved decision quality, reduced processing time, or enhanced customer experience. Celebrating incremental progress is crucial, as smaller improvements can accumulate to create significant value and enhance organizational confidence.
“Above all, leaders should foster a culture of learning, treating insights and occasional setbacks as part of the innovation process,” Faisal emphasizes.
Focusing on People
For organizations aiming to scale AI initiatives effectively, the focus must remain on people. Faisal stresses that successful adoption depends on helping individuals understand how AI enhances their experiences and addresses real problems. Effective communication is vital; organizations should clarify why AI is being introduced, the value it will deliver, and the areas where human involvement remains essential.
Engaging users early in the process ensures that solutions are tailored to genuine needs, fostering a sense of ownership. Adoption should be seen as an ongoing process supported by training, feedback, and refinement. Ultimately, success should be measured by user acceptance and business impact, rather than solely on technical performance.
As reported by cyberwarriorsmiddleeast.com, organizations navigating the complexities of AI adoption must prioritize building trust through transparency, focused pilot projects, and a commitment to people-centric strategies.
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Published on 2026-07-07 05:38:00 • By FAME Delivered News Desk