
Mosanya Advisory works with boards, CEOs and leadership teams whose organisations must change faster than their current model allows, with AI as the accelerant.We help take stock of the disruptions, shape the ambition, define the strategy, the operating model and the adequate technology, and ensure the change holds through managed adoption.Strategy, execution and adoption, addressed in one integrated approach.
Founder & Principal
Deep disruption is the structural rewiring AI demands of complex organisations — not surface deployment or tactical efficiency, but the reshaping of strategy, operating model, and capability stack around what comes next.
The journey from intent to impact gets throttled across three challenging areas: strategy & transformation, business development, and venture building. Strategies crafted with both ambition and realism still get shelved without disciplined transformation and adoption. Building new revenue lines requires its own discipline across business models, partnerships, and channels. New ventures designed inside the gravity of the mothership rarely escape it.
AI compounds the difficulty across all three. The cost is everywhere: initiatives that stall before they compound, budgets that buy activity rather than outcomes, market windows that close while the organisation is still aligning. And in complex, volatile markets where the existing playbook only takes you partway, these challenges are sharpened, not softened.
Every engagement combines three ingredients: strategic framing, operating execution, and active adoption. They appear sequentially in transformation programmes, in parallel as a toolkit in business development, and iteratively across the build-validate loop of new ventures. The mix differs by practice; the ingredients are the same.
Skip an ingredient and the work does not stick: framing that ignores execution stalls at the plan, building without adoption produces capability that no one uses.
Three practices, each chosen by design to go beyond the playbook. Each carries its own challenge, its own demands, and a different mix of the three ingredients behind every engagement.
Vision exercises, AI strategy formulation, competitive assessments, strategic planning and cascading, transformation programme design, operating-model redesign, capability build-out, and the disciplined execution that turns intent into adopted reality.
Business modeling, go-to-market design, enterprise and government partnerships, alliance structuring, and the relationship-building that volatile markets reward.
New venture incubation, product and technology innovation, R&D-to-commercial bridges, and AI-native business model design. Work for organisations ready to do more than incremental.
Engagements range from a half-day briefing to a multi-quarter transformation role. Always anchored at executive level (board, C-suite, steering committee) and designed to cascade down through the operating layers that make AI land.
If the questions below resonate with the ones you are wrestling with, we should talk.
How do we hold the ambition of our strategy through execution, when scale and complexity resist every meaningful change?
How do we move from scattered AI pilots to a coherent enterprise strategy that lands?
How do we modernise the group without losing the founder logic and long-horizon discipline that built it?
How do we apply AI cross-portfolio without losing the long-horizon ethos that built the group?
How do we design and deliver large-scale public programmes that reach citizens and stick beyond the political cycle?
How do we use AI to improve public-service delivery at scale, with the governance the role demands?
How do we balance national mandate with commercial discipline, and outperform private peers in our sector?
How do we align AI investments with national-priority mandates while outperforming private peers commercially?
How do we redesign programmes and research to stay relevant to an economy reshaping faster than the curriculum cycle?
How do we rebuild curricula and research strategy for an AI-shaped economy?
How do we close the gap between discovery and commercial application before others do?
How do we turn AI capability into national strategic advantage rather than published papers?
Computer scientist by training, with an MSc in Electrical Engineering and Computer Sciences and a dissertation in artificial intelligence and computer vision. An early career in mission-critical, deep-tech system development, then co-founder of a venture-backed AI startup in San Francisco and Belgium, turning research-grade speech recognition into production-grade systems.
At McKinsey & Company, in the Strategy & Corporate Finance practice: M&A, large-scale transformation programmes and operational excellence for multinationals, family-owned conglomerates and public-sector organisations across Europe, MENA and Asia.
Seventeen years in the Gulf in C-suite roles at LG Electronics and Royal Philips across the Middle East and Africa: regional strategy, business-model and go-to-market reinvention, new business building, a healthcare consulting practice, and advisory work on national AI programmes.
Today an advisor on strategy, transformation, and adoption in the age of AI, and an AI investor as Founder & Managing Partner of Nexan Ventures. Based in Dubai.
ugo-mosanya.com →Around the core practice stands a curated network of senior advisers, operators and specialists across a wide range of sectors, functions and geographies. They bring their expertise into an engagement whenever it is needed.
The challenges above will not solve themselves, and the window for action keeps narrowing as AI moves faster than most operating cadences. Pick the entry point that fits and let's begin the conversation.