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Quest Training

Data & AI Operating Model Design Training Course

Compares centralized, federated and hybrid models and shows how structures, decision rights, talent and governance combine to scale data and AI value.

DOPS · Data & AI Operating ModelsAll Levels#2560ClassroomEnglish , Arabic
Duration
5 Days
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Free substitution at any time · 30+ days' notice: full refund minus 10%. Cancellation policy

Sectors:Data, Analytics & Digital TransformationCategories:Data & AI Operating Models

Course Overview

This advanced practical course focuses on designing an effective operating model that enables organizations to manage, scale, and derive measurable value from data and artificial intelligence. It addresses how organizational structures, governance, processes, technology, talent, and decision rights should work together to support enterprise data and AI capabilities. Participants will examine operating model options, including centralized, federated, and hybrid approaches, and determine how responsibilities should be distributed across the Data and AI Office, technology teams, business units, and specialized functions. The programme emphasizes practical design rather than conceptual frameworks, with a strong focus on accountability, collaboration, scalability, and business alignment. The course also addresses AI and data governance, portfolio management, capability development, delivery models, funding, performance measurement, risk management, and organizational change. Participants will work with practical scenarios to identify operating model gaps and design structures capable of supporting analytics, AI, data products, automation, and digital transformation.

Learning Objectives

  • Analyse the strategic requirements of a Data and AI operating model.
  • Assess existing organizational structures, capabilities, processes, and decision rights.
  • Compare centralized, federated, and hybrid operating model approaches.
  • Define the roles and responsibilities of Data, AI, IT, and business functions.
  • Design effective governance structures and decision-making mechanisms.
  • Establish accountability for data products, AI solutions, platforms, and business outcomes.
  • Align operating processes with data management, analytics, and AI delivery requirements.
  • Develop appropriate talent, capability, sourcing, and collaboration models.
  • Establish funding and portfolio management approaches for Data and AI initiatives.
  • Define performance measures for operational effectiveness and business value.
  • Address risk, compliance, responsible AI, security, and change management requirements.
  • Develop a practical target operating model and implementation roadmap.

Who Should Attend

This course is designed for Chief Data Officers, Chief AI Officers, Chief Digital Officers, Data and AI Directors, Heads of Data, Heads of Analytics, Data Governance Leaders, AI Leaders, and senior technology and transformation executives. It is also suitable for enterprise architects, data architects, AI architects, digital transformation managers, IT leaders, business transformation professionals, portfolio managers, and senior business leaders involved in Data and AI strategy and implementation. The programme is particularly relevant to organizations establishing or transforming Data and AI functions, scaling enterprise AI capabilities, implementing data product models, or moving from individual initiatives toward an integrated operating model.

Learning Outcomes

  • Assess the current effectiveness and maturity of a Data and AI operating model.
  • Select an operating model structure appropriate to organizational needs.
  • Define organizational roles, responsibilities, and decision rights.
  • Establish effective collaboration between business, technology, data, and AI teams.
  • Design governance mechanisms for data and AI decision-making.
  • Define accountability for data products, AI solutions, platforms, and outcomes.
  • Develop operating processes for prioritizing and delivering Data and AI initiatives.
  • Identify the capabilities, skills, and talent structures required for sustainable operations.
  • Develop funding and investment mechanisms for Data and AI portfolios.
  • Establish performance indicators for capability, delivery, adoption, and business value.
  • Integrate risk, security, compliance, and responsible AI requirements into the operating model.
  • Produce a target operating model and phased implementation roadmap.

Course Outline

Day 1

Data & AI Operating Model Foundations and Current-State Assessment

  • Strategic role of the Data and AI operating model.
  • Relationship between business strategy and operating model design.
  • Core components of an effective operating model.
  • Current-state organizational and capability assessment.
  • Identifying structural, process, technology, and accountability gaps.
  • Centralized, federated, and hybrid operating models.
  • Evaluating operating model strengths, limitations, and scalability.
  • Defining design principles and target-state requirements.
  • Practical Application: Assess a current Data and AI organization and identify priority gaps requiring operating model redesign.
Day 2

Organizational Structure, Roles, Governance & Decision Rights

  • Designing Data and AI organizational structures.
  • Data and AI Office responsibilities and scope.
  • Business unit and functional responsibilities.
  • Data product and AI product ownership.
  • Data stewardship and technical accountability.
  • Governance committees and decision forums.
  • Defining decision rights and escalation mechanisms.
  • Cross-functional collaboration and accountability.
  • Managing centralized standards with decentralized execution.
  • Practical Application: Design an organizational structure, responsibility model, and decision-rights framework for a target Data and AI operating model.
Day 3

Data & AI Processes, Delivery Models & Capabilities

  • Operating processes across the Data and AI lifecycle.
  • Data product development and management.
  • AI use-case identification, prioritization, and delivery.
  • Analytics and data science delivery models.
  • Platform and shared-service capabilities.
  • Reusable data and AI services.
  • Talent, skills, workforce planning, and capability development.
  • Internal teams, external partners, and sourcing models.
  • Integrating Data and AI delivery with enterprise transformation.
  • Practical Application: Develop an integrated Data and AI delivery model covering capabilities, workflows, ownership, and service interactions.
Day 4

Governance, Investment, Performance & Risk Management

  • Data governance and AI governance within the operating model.
  • Responsible AI, security, privacy, and regulatory considerations.
  • Data and AI portfolio management.
  • Funding and investment allocation models.
  • Prioritizing initiatives based on value, risk, complexity, and feasibility.
  • Service performance and operational metrics.
  • Measuring adoption, delivery effectiveness, and business value.
  • Risk management and control mechanisms.
  • Executive reporting and performance management.
  • Practical Application: Build a Data and AI portfolio governance and performance framework linking investment decisions to measurable business outcomes.
Day 5

Target Operating Model Design & Implementation Roadmap

  • Designing the target Data and AI operating model.
  • Operating model transition strategies.
  • Prioritizing structural and capability changes.
  • Implementation phases and dependencies.
  • Change management and organizational adoption.
  • Funding and resource planning.
  • Operating model performance reviews and continuous improvement.
  • Scaling Data and AI capabilities across the enterprise.
  • Establishing long-term operating model sustainability.
  • Final Workshop: Develop and present a complete Data and AI target operating model covering organizational structure, governance, roles, processes, capabilities, delivery mechanisms, performance measures, investment priorities, and implementation roadmap.

Upcoming Dates

Selected date

London · 2 – 6 Nov 2026 · Classroom

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CityDatesPrice per ParticipantEarly Bird
LondonScheduled
2 – 6 Nov 2026€5,600—
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MadinahScheduled
9 – 13 Nov 2026€4,200—
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IstanbulScheduled
16 – 20 Nov 2026€4,700—
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MadridScheduled
16 – 20 Nov 2026€5,400—
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MakkahScheduled
23 – 27 Nov 2026€4,200€3,780until 2 Nov
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ManamaScheduled
30 Nov – 4 Dec 2026€4,700€4,230until 9 Nov
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MarbellaScheduled
30 Nov – 4 Dec 2026€5,400€4,860until 9 Nov
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JeddahScheduled
6 – 10 Dec 2026€4,200€3,780until 16 Nov
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MarrakeshScheduled
7 – 11 Dec 2026€4,300€3,870until 16 Nov
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MilanScheduled
14 – 18 Dec 2026€6,000€5,400until 23 Nov
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MunichScheduled
21 – 25 Dec 2026€5,400€4,860until 30 Nov
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MuscatScheduled
28 Dec 2026 – 1 Jan 2027€5,300€4,770until 7 Dec
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KigaliScheduled
28 Dec 2026 – 1 Jan 2027€5,100€4,590until 7 Dec
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OnlineScheduled
3 – 7 Jan 2027€1,700€1,530until 14 Dec
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RiyadhScheduled
11 – 15 Jan 2027€4,200€3,780until 21 Dec
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ParisScheduled
11 – 15 Jan 2027€5,700€5,130until 21 Dec
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RomeScheduled
18 – 22 Jan 2027€5,800€5,220until 28 Dec
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Kuala LumpurScheduled
18 – 22 Jan 2027€4,400€3,960until 28 Dec
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SalalahScheduled
25 – 29 Jan 2027€5,300€4,770until 4 Jan
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ViennaScheduled
1 – 5 Feb 2027€5,700€5,130until 11 Jan
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LisbonScheduled
1 – 5 Feb 2027€5,400€4,860until 11 Jan
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AmmanScheduled
7 – 11 Feb 2027€3,100€2,790until 18 Jan
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AmsterdamScheduled
15 – 19 Feb 2027€5,400€4,860until 25 Jan
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AbujaScheduled
22 – 26 Feb 2027€5,100€4,590until 1 Feb
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BarcelonaScheduled
22 – 26 Feb 2027€5,400€4,860until 1 Feb
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CairoScheduled
1 – 5 Mar 2027€3,300€2,970until 8 Feb
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BerlinScheduled
1 – 5 Mar 2027€5,800€5,220until 8 Feb
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CasablancaScheduled
8 – 12 Mar 2027€4,300€3,870until 15 Feb
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DammamScheduled
15 – 19 Mar 2027€4,200€3,780until 22 Feb
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AccraScheduled
15 – 19 Mar 2027€5,100€4,590until 22 Feb
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