Building AI Programmes that Balance Speed and Responsibility
Most organisations begin with reputational and regulatory concerns, because those risks are easier to quantify. However, mature organisations eventually understand that ethical AI starts with protecting people, says Alexander Merkushev, Head of AI Projects at Yango Tech.
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Artificial intelligence is moving from experimentation to enterprise infrastructure.
Across the GCC, governments are accelerating national AI agendas while businesses race to embed AI into customer service, operations, hiring and decision-making. Yet as adoption gathers pace, organisations are discovering that deploying AI responsibly is becoming just as important as deploying it quickly.
For many enterprises, the challenge is how to build governance, trust and accountability into every stage of implementation. Without strong data foundations, clear operating models and human oversight, the benefits of AI can quickly be overshadowed by operational, regulatory and reputational risks.
“Regulation will keep evolving, but companies should not wait for every rule to be written before building safeguards into how AI is selected, tested, deployed, and monitored,” says Alexander Merkushev, Head of AI Projects at Yango Tech.
In this interview, he discusses why governance must evolve alongside innovation, how enterprises should approach ethical AI, why the GCC needs adaptable governance frameworks, and why organisations of the future will ultimately be defined by the judgment behind their AI decisions rather than the technology itself.
Excerpts from the interview;
The GCC has moved faster than most regions in embracing AI. Do you think innovation is currently moving faster than the rules designed to govern it?
Innovation is moving faster than regulation, but the GCC is showing that innovation and governance can advance together. Governments across the region are investing in AI while strengthening expectations around trust, security, and accountability.
In the UAE, the establishment of the Federal Authority for Artificial Intelligence and Data, alongside DIFC’s proposed AI-focused data protection amendments, shows that governance is becoming more structured as adoption accelerates.
The challenge is that enterprise adoption often advances before internal operating models are ready. Many companies want to use AI quickly, but they may not yet have the right data foundations, governance processes, employee training, or risk controls in place. That is where the gap appears.
This is also where Yango Tech’s AI Expertise service is focused.
Through a combined consulting and deployment model, we support organisations with strategy, governance, workforce readiness, and implementation planning, while helping them build production-ready systems that can integrate directly into operational environments.
Regulation will keep evolving, but companies should not wait for every rule to be written before building safeguards into how AI is selected, tested, deployed, and monitored.
When organisations talk about ethical AI, are they usually talking about protecting people, protecting reputations, or protecting themselves from future regulatory risk?
Most organisations begin with reputational and regulatory concerns, because those risks are easier to quantify. A public mistake, a compliance breach, or loss of customer trust can damage a company very quickly. However, mature organisations eventually understand that ethical AI starts with protecting people.
AI systems are increasingly involved in decisions that affect customers, employees, citizens, and business partners.
That means the discussion has to move beyond surface-level risk management. Companies need to ask if the system is explainable, the data is reliable, human review is required, and the outcome is fair and appropriate for the context.
In our work with enterprises, we see that responsible AI becomes practical when it is built into implementation. Governance cannot sit apart from the technology as a policy document; it must define how use cases are approved, how models are monitored, when human judgment is required, and where automation should stop.
The GCC has ambitious national AI agendas, but each country also has its own cultural, social, and regulatory priorities. Can there ever be a standard for ethical AI in the region?
A single ethical AI standard across the GCC is possible at the level of principles, but implementation will always need local context.
The region can align around transparency, safety, accountability, data protection, human oversight, and measurable value. These principles are already widely understood across advanced AI markets.
The difference comes in how each country applies them. A UAE public sector use case will have different governance expectations from a financial services deployment in Saudi Arabia or a retail automation project in another GCC market.
Cultural expectations, sector regulation, data residency requirements, and public trust all influence how responsible AI should be designed.
This is why companies need frameworks that are consistent but adaptable. The core standard should be stable, while the operating model should reflect the country, sector, and use case.
In practice, ethical AI is achieved by combining strong governance with local understanding and practical deployment experience, rather than copying a global template.
In your experience, what concerns business leaders more today: the risk of falling behind in AI adoption or the risk of deploying AI irresponsibly? And why?
Business leaders are under pressure from both sides, but the fear of falling behind is often stronger at the beginning. AI is now linked directly to productivity, customer experience, cost efficiency, and competitive position.
Across the GCC, 84% of organisations have adopted AI in at least one business function, but only 31% have scaled AI across operations, and 11% are considered AI value realisers.
At the same time, leaders know that irresponsible deployment can create serious operational, legal, and reputational damage. This is especially true in regulated sectors such as finance, healthcare, logistics, and public services, where AI systems may influence sensitive processes.
The most capable organisations are building AI programmes that treat speed and responsibility as connected priorities, with clear use cases, strong data foundations, governance, employee readiness, and measurable business outcomes.
That is the only way to scale AI with confidence rather than urgency alone.
Five years from now, what do you think will define an organisation: the technology it deploys, the policies it follows, or the decisions it chooses not to automate?
Making predictions five years ahead in AI is always risky. Five years ago, few people imagined generative AI would become a daily tool for hundreds of millions of people, and even a year ago, the industry conversation looked very different. The pace of change is so fast that specific forecasts are likely to be wrong.
However, one principle remains consistent across technology revolutions: the organisations that create lasting value are those that make new technology part of their core business, not those that treat it as a layer on top of existing processes.
Five years from now, I believe the strongest organisations will be defined by judgment. AI tools will become increasingly accessible, but the real differentiator will be how leaders decide where AI should drive efficiency, where it should augment people, and where human accountability must remain central.
Our experience shows the potential impact when AI is deployed strategically: up to 95% first-contact resolution in customer support, up to 40% lower support costs, up to $100,000 in monthly operational savings, up to 50% faster hiring cycles, up to 35% better debt recovery performance, and up to 80% faster document processing.
Ultimately, success will depend not on how much AI an organisation adopts, but on how effectively it integrates AI into the way it creates value.
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