Responsable – IA et numérisation. Technologies réseau
Job Description
At MTN BENIN, we believe that understanding our people’s needs and aspirations is key to
creating experiences that delight you at work, every day. We are committed to fostering an
environment where every member of our Y’ello Family is heard, understood, and
empowered to live an inspired life.
Our values keep us grounded and moving in the right direction. Most importantly, they keep
us honest. It is not something we claim to be. It is in our DNA.
As an organization, we consider it our mission to create an exciting and rewarding place to
work, where our people can be themselves, thrive in positivity and ignite their full potential. A
workplace that boosts creativity and innovation, improves productivity, and ultimately drives
meaningful results. A workplace that is built on relationships and achieving a purpose that is
bigger than us. This is what we want you to experience with us!
Our commitments go beyond an organizational promise. It is in our leadership and
managerial ethos to meaningfully partner with our employees, customers and stakeholders
with a vision to realize our shared goals.
We are delighted that you are considering us as your career partner to make a mark in the
world. We look forward to your application!
The Manager AI & Digitalization partners with the entire business to accelerate and scale
MTN’s AI transformation. The role leads the identification, prioritization and industrialization
of high‑value AI and digitalization initiatives that improve customer experience, productivity,
operational excellence, risk management and revenue growth. Working through influence
across functions, the role embeds AI into business processes, products and customer
journeys, while ensuring delivery discipline, governance and responsible AI practices so that
solutions are deployed safely, securely and compliantly in a regulated telecommunications
environment.
● Drive MTN’s AI acceleration and infusion agenda by embedding AI-enabled ways of
working across operations and customer journeys to unlock growth and operational
renewal.
● Partner with business leaders to discover, shape and prioritize AI use cases with
clear value hypotheses, measurable KPIs and defined adoption plans.
● Enable repeatable, scalable AI delivery by defining standards, playbooks and
reusable patterns across data, architecture, security and engineering.
● Collaborate with data, platform, architecture and security teams to industrialize AI
solutions (MLOps/DevSecOps, monitoring, model lifecycle management) in line with
policy and regulatory requirements.
● Build organizational AI fluency and adoption through enablement (training,
champions network, communications and change management) to sustain benefits
realization.
Responsibilities
Under the supervision of the CTIO and Senior Manager, drive the following enterprise AI
acceleration and digitalization outcomes:
● AI Strategy & Roadmap Execution: Translate MTN’s AI ambition into an executable
roadmap and operating cadence; align priorities with business strategy and
Technology strategy; ensure clear ownership, milestones and investment rationale.
● Use‑Case Discovery & Business Co‑Creation: Run discovery/ideation workshops;
define problem statements, value hypotheses, success KPIs, data readiness and
process impacts; ensure business sponsorship and adoption commitments.
● Responsible AI, Risk & Compliance: Embed responsible AI principles,
privacy-by-design, security controls and model risk practices; coordinate required
approvals (security, risk, legal, procurement) and maintain audit-ready evidence.
● Delivery Acceleration & Industrialization: Establish repeatable delivery patterns (data
pipelines, model deployment, monitoring, retraining); partner with platform/cloud
teams on environments; ensure production readiness and lifecycle management.
● Value Realization & Performance Management: Define benefits cases; track
expected vs realized value; publish executive dashboards; drive corrective actions to
maximize ROI and business outcomes.
● Adoption, Change & Capability Building: Drive adoption through training, playbooks
and communities of practice; support business teams to integrate AI into daily
workflows; measure adoption and user satisfaction.
Enterprise AI Platform & Data Enablement
● Partner with Data & Analytics, Architecture and Security to define the required
AI/data capabilities (data products, feature stores where applicable, model registry,
monitoring, access controls).
● Promote reuse and standardization (reference architectures, templates,
prompt/model patterns) to reduce time-to-value and improve quality.
● Ensure solutions are designed for scalability, resilience and cost efficiency, with clear
SLOs and ongoing performance optimization
Stakeholder Management & Executive Reporting: Build strong relationships with
business leaders and delivery teams; facilitate decision-making through clear
reporting of progress, value, risks and dependencies; and ensure alignment with
MTN Group standards and local regulatory expectations.
Key Performance Areas: Supporting Activities (Guidelines, not limited
to):
1. AI Portfolio & Roadmap
Delivery:
Execute and govern an enterprise AI
portfolio, ensuring prioritisation,
delivery cadence, risk management
and benefits tracking across all
business functions.
●
Maintain a prioritised AI use-case backlog
with agreed scoring criteria (value,
feasibility, risk, readiness).
● Run monthly/quarterly portfolio
governance with CTIO and business
sponsors; track milestones, dependencies,
decisions and risks.
● Publish executive dashboards on delivery
status, adoption and realised value.
● Define and track benefits cases per
initiative; drive corrective actions to close
value gaps.
● Ensure each use case has a named
business owner, agreed KPIs and a
go-live/adoption plan.
●
2. Responsible AI, Risk &
Compliance: Ensure AI solutions are
designed and operated safely,
securely and compliantly, with
appropriate controls, approvals and
audit readiness.
●
Maintain a Responsible AI checklist and
artefacts per solution (data privacy, bias,
explainability where applicable, human
oversight).
● Coordinate governance approvals with
Security, Risk, Legal and Procurement;
ensure vendor and model onboarding
follows policy.
● Ensure data access, retention and
processing comply with local regulations
and MTN policies; maintain audit evidence
and reporting.
● Monitor AI solution performance and risk
indicators post go-live; trigger
review/retraining/controls updates as
needed.
3. AI Delivery & Industrialisation:
Establish repeatable delivery
practices to move AI solutions from
prototype to production reliably and
efficiently.
Define delivery standards and ensure
AI solutions meet production
readiness, monitoring, support and
lifecycle requirements (including
MLOps/DevSecOps where
applicable).
●
Define reference architectures and
patterns for analytics/ML/genAI use cases,
aligned to enterprise architecture and
security.
● Coordinate data readiness (quality, access,
lineage) and environment provisioning with
Data, Cloud and Platform teams.
● Establish model/prompt versioning, testing,
monitoring and retraining/refresh practices;
define support and incident processes.
● Track cycle time from idea to production
and continuously improve delivery
throughput and quality.
●
4. Adoption, Change & Capability
Building:
Drive AI adoption across the
organisation through structured
change management, enablement,
communications and measurement of
usage and impact.
●
Maintain AI champions network across
departments and run regular enablement
sessions (use-case clinics, office hours).
● Develop playbooks and guidance for safe
and effective AI use (including prompt
guidance where relevant) and track
adherence.
● Support business owners to redesign
processes and operating models to embed
AI (roles, controls, decision rights).
● Measure adoption and user experience
(active users, frequency, satisfaction) and
drive continuous improvement actions.
● Develop an annual AI capability plan
(training pathways, awareness, role-based
learning) in collaboration with HR/L&D.
Qualifications
Education:
● Bachelor’s degree in computer science, Data Science, Engineering, Information
Systems, Statistics or equivalent
Knowledge & Attributes
● Good understanding of the telecommunications industry and key value drivers
(customer, network/operations, digital channels, fintech/mobile money where
applicable)
● Strong stakeholder management and ability to influence across business and
Technology leadership
● Practical understanding of AI/analytics concepts and how to translate
business problems into data/AI solutions
● Strong commercial mindset with ability to shape AI initiatives
that drive growth, efficiency and customer experience
● Experience improving reporting/insights through automation
and analytics; strong focus on measurable outcomes and
decision support
Experience:
● 5+ years’ experience in digital transformation, data/analytics, automation or
technology delivery roles with business partnership responsibilities
● 3+ years’ experience leading cross-functional initiatives (portfolio/programme
delivery, product ownership, or transformation), with measurable business
impact
Professional competencies
● AI product/portfolio management: ability to prioritize use cases, define benefits, KPIs
and adoption measures
● Business analysis and process redesign skills; ability to translate operational
pain points into AI-enabled improvements
● Programme/project delivery: strong planning, dependency
management and stakeholder communication
● Change leadership: ability to drive adoption, communications
and training across diverse teams
● Governance, risk and controls mindset; ability to operate in
regulated environments with strong audit discipline
● Excellent interpersonal, facilitation and communication skills;
able to influence without formal authority
● Leadership and coaching; ability to build communities of
practice and develop AI fluency
● Strong analytical and reporting skills; able to create
executive-ready insights and performance tracking
● High integrity and disciplined execution; delivers outcomes
through structured governance and follow-through
Technical competencies
● Data & analytics foundations: data modelling, data quality, BI/visualization and KPI
design
● AI/ML and/or GenAI delivery understanding (model lifecycle, evaluation,
monitoring, prompt patterns) and awareness of MLOps practices
● Cloud and integration awareness: APIs, data pipelines, access
controls and secure deployment patterns in enterprise environments
Skills & Physical Competencies:
● Professional approach with a can do attitude
● Innovative, takes initiative, is result oriented and develops self consistently.
● Leadership, customer centricity, collaborative, ability to coach &
develop direct reports
● Trustworthy, integrity and ethical in dealings
● Good written and verbal communication, and commitment to
the organization
● Analytical thinking and problem-solving abilities.
● Global thinker, Improves processes.
● Listens well
● Conflict management
● Excellent negotiation skills.
● Manages underperformance
● Good time management
Continuous Risk & Compliance Management & Reporting
● Perform and encourage continuous and effective risk management practice within
your activities.
● Promote risk-based decision taken. Ensure effective escalation of key risks,
compliance breach and non-ethical issues.
● Demonstrate and encourage ethical behaviors. Promote Business
continuity best practices and compliance with applicable regulations
and internal PPPs (Policy, Process and Procedures).
Must live the MTN Values of
● Can Do with Integrity
● Lead with care
● Collaborate with Agility
● Serve with respect
● Act with Inclusion
