From AI Hype to Lasting Value – An Interview with Christophe Zoghbi
Christophe Zoghbi is the founder and Chief Executive Officer (CEO) of ZAKA, an artificial intelligence (AI) education and consulting company helping organizations and professionals across the Middle East and North Africa (MENA) region turn artificial intelligence into practical capability.
In this interview, he explains how leaders can identify the right AI opportunities, choose between automation and agents, build culturally relevant solutions, and create adoption that delivers measurable and lasting value.
Christophe Zoghbi, Founder & CEO of ZAKA
What first drew you to building ZAKA around practical AI adoption rather than technology for its own sake?
My background is technical, but the more I worked with organizations and professionals, the clearer it became that the biggest gap was rarely access to technology. It was the ability to understand it, apply it, and connect it to a real need.
I saw talented people across the MENA region who wanted to participate in AI but lacked practical pathways and opportunities to build practical experience. I also saw companies attracted to AI because it was new, without a clear view of what problem they were trying to solve.
That shaped ZAKA from the beginning. We did not want to teach AI as an abstract subject or build technology simply to demonstrate that it could be built. We wanted people to leave a program able to do something they could not do before, and companies to invest in solutions that improved an actual outcome.
I believe technology matters when it expands human capability, improves decisions, or removes friction. Practical adoption is where AI moves from an impressive idea to meaningful progress.
When you assess a company, what tells you that AI is actually the right answer?
AI is the right answer when the nature of the problem genuinely benefits from capabilities such as interpretation, prediction, generation, or decision support.
We first look for a clear business outcome and a workflow where people are spending significant time processing information, handling variation, or making repeatable judgments. Then we examine whether the required data exists, whether the output can be evaluated, and whether the value justifies the complexity and risk.
Equally important, we try to disprove the need for AI. If a rule, form, integration, process redesign, or conventional automation can solve the problem reliably, that is often the better choice.
Many companies arrive with a solution already in mind, such as a chatbot or an agent. Our responsibility is to step back and understand the underlying need. Sometimes AI is central to the answer, sometimes it is only one component, and sometimes it is unnecessary.
A good assessment does not maximize the amount of AI used. It identifies the simplest responsible solution that can produce measurable value.
Why do you examine strategy, workflows, data, and governance before discussing AI solutions?
Because an AI solution does not operate in isolation.
Strategy tells us which outcomes matter and whether the initiative deserves attention. Workflows reveal how work actually happens, including the handoffs, delays, exceptions, and informal practices that may not appear in a process document. Data determines what the system can know and how confidently it can act.
Governance is where most leaders expect friction, but done well, it's the opposite, it's what lets an organization move faster with confidence. Clear guardrails mean teams don't have to relitigate the same risk questions on every project, who's accountable, what data can be used, which decisions need human review, and how performance gets monitored are settled once, up front, so execution doesn't stall later.
Organizations that treat governance as a brake usually end up slower because every new initiative reopens the same unresolved questions. Organizations that build it in early move faster because the guardrails are already there when the next opportunity shows up.
If we skip these layers, we risk creating an impressive prototype that cannot be trusted, adopted, integrated, or scaled. We may also automate a broken process and make the problem move faster.
We use our MAP Framework at ZAKA, which begins by Mapping the organization and its workflows, then Advising on suitable solutions, and finally Prioritizing opportunities into a realistic roadmap. This sequence keeps the conversation grounded in business reality.
The AI comes after the understanding because the quality of the solution depends on the quality of the questions asked before it is designed.
What will it take for MENA to become a builder of AI capability rather than only a consumer of global tools?
The region needs to develop capability at several levels at the same time. We need more people who can use AI confidently, more specialists who can build and adapt systems, more leaders who can make informed investment decisions, and stronger governance that enables innovation responsibly.
Education is part of this, but it must extend beyond awareness. Learners need practical projects, local use cases, mentorship, and routes into meaningful work. Organizations also need to move from isolated experiments toward owning their AI roadmaps, data foundations, operating models, and intellectual property.
At a regional level, collaboration matters. Universities, governments, companies, startups, and communities should not work as separate islands. They can share challenges, create test environments, support Arabic research, and open opportunities for local talent to solve real problems.
MENA does not need to reproduce every layer of the global AI stack to become a builder. It needs the confidence and competence to shape technology around its own priorities, develop solutions for its markets, and contribute original knowledge and products to the wider world.
Why does culturally relevant AI matter for Arabic-speaking and MENA markets?
Language is the visible layer, but the real risk is trust. A system can technically support Arabic and still misread how people actually communicate in a workplace, classroom, or customer interaction. When that happens, leaders lose confidence in the tool faster than they gained it.
I've seen organizations adopt a solution that worked well in a pilot, then watch adoption stall because it didn't reflect how their people actually think, decide, or phrase things.
This is a governance and adoption issue as much as a technical one. If a solution is built for another market and simply translated, the errors it produces won't be random, they'll cluster around the assumptions that don't transfer, and some groups will be affected more than others. That's a real business risk, not just a quality issue.
Getting this right means involving people who understand the environment the system will operate in, testing against real regional data, and designing for local regulations and institutional norms from the start, not patching a global product after the fact.
It isn't about isolating the region from global technology. It's about making sure the technology actually earns the trust it needs to be adopted.
When should a company use an AI agent, and when is simpler automation the better choice?
A company should consider an AI agent when the work involves a goal rather than a single instruction, requires several steps, and depends on interpreting changing information along the way.
For example, an agent may retrieve information from different systems, compare options, prepare an output, and request approval before taking a final action. It is most useful when the path cannot be fully predetermined, but the objective, tools, permissions, and evaluation criteria can be clearly defined.
Simpler automation is better when the process is stable and rules-based, when this happens, move that file, send this notification, update that field. Traditional automation is usually cheaper, faster, easier to test, and more predictable.
Companies should not add autonomy merely because agents are receiving attention. The more freedom a system has, the more important controls, observability, exception handling, and human oversight become.
My rule is to use the minimum level of intelligence and autonomy required. Begin with a reliable workflow, introduce AI where interpretation is needed, and only use an agent when multi-step reasoning and tool use create enough additional value to justify the risk.
What is one workflow leaders should examine first if they want measurable value from AI?
I would start with a high-volume internal workflow that employees already find repetitive and frustrating, especially one involving documents, requests, or information retrieval.
A good example is the journey from receiving a request to preparing the first useful response. In many organizations, an employee must search several folders or systems, review previous cases, extract key details, draft a response, and route it for approval. That creates visible measures, turnaround time, hours spent, rework, response quality, and employee satisfaction.
It is also usually safer than beginning with a fully autonomous customer-facing process. The specific workflow matters less than the selection criteria. It should occur often enough to matter, have a clear owner, contain a manageable level of risk, and produce an output that can be evaluated.
Leaders should document the current baseline before introducing AI, then run a focused pilot and compare results. This prevents vague claims about productivity.
The best first use case is not necessarily the most exciting one. It is the one that can prove value, teach the organization how adoption works, and build confidence for what comes next.
What principle helps you stay grounded when the pace and hype around AI keep accelerating?
I keep returning to one question, "What has meaningfully improved for the person or organization using this?"
The AI field moves so quickly that it is easy to confuse novelty with progress. A new model, feature, or agent can be impressive, but that does not automatically make it valuable. I try to separate what is newly possible from what is currently useful, reliable, and responsible.
Staying close to learners, teams, and business problems helps because reality gives immediate feedback. Did people adopt the tool after the demonstration? Did it save time without creating more review work? Did it improve a decision, expand access, or help someone develop a real capability?
I also accept that being grounded does not mean being slow. We should experiment actively, but with clear hypotheses, limited exposure, and honest evaluation. Not every experiment must become a deployment.
Curiosity should push us to explore, while evidence determines what we keep. That balance allows us to remain ambitious about AI without letting the speed of the market dictate decisions that should be driven by purpose.
What should leaders get right now if they want AI adoption to create lasting value?
Leaders should treat AI adoption as an organizational transformation, not a software purchase.
The first priority is clarity, which business outcomes matter, which workflows could improve, and how success will be measured. The second is people. Employees need more than tool access, they need role-relevant training, permission to experiment, clear expectations, and confidence that AI is being introduced to strengthen their work rather than simply reduce headcount.
The third is a foundation for responsible scale, including data access, security, governance, ownership, and a process for monitoring quality.
Leaders should then build a portfolio of initiatives instead of betting everything on one large project. Start with a few high-value use cases, learn quickly, document what works, and expand based on evidence.
Most importantly, leadership must remain engaged after the pilot. Lasting value comes from changing how decisions are made and how work is designed.
Companies that succeed will not be those that adopted the most tools. They will be those that built the capability to identify, implement, govern, and continuously improve the right AI solutions.
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