AI project advisory and strategic programmes

Scope, manage and deploy an AI project or a suite of agents

OLING provides AI project advisory to turn an idea into a manageable programme, from cross-functional initiatives to business agents tailored to microbusinesses and SMEs.

Discuss your AI project
AI project advisory, management and business agents
A scoped and phased AI programme Clear decisions across business teams, the IT department and partners Agents and use cases integrated within a sustainable framework
Scope of the service

When to engage AI project advisory support

AI project advisory is relevant when an organisation needs to move from scattered ideas or prototypes to an approved, funded, governed and deployable project. It covers the project life cycle, while the AI Act page addresses the compliance framework.

  • Assess the business problem, users, AI-assisted decisions and expected value before selecting a solution.
  • Review the data, integrations, security constraints and responsibilities required for the use case.
  • Compare implementation options, providers and the conditions for moving from prototype to production.
  • Organise acceptance testing, adoption, human oversight and post-deployment results measurement.
Objectives

Objectives agreed with your teams

  • A scoped and phased AI programme
  • Clear decisions across business teams, the IT department and partners
  • Agents and use cases integrated within a sustainable framework
  • Deliverables defined during project scoping.

AI project advisory, management and business agents

OLING supports management teams and project teams in scoping, managing and deploying AI projects, from strategic programmes to business agents integrated into existing processes.

The engagement aims to clarify objectives, assign responsibilities and organise a consistent deployment across business teams, the IT department, data and security teams, and partners.

What the engagement covers

  • AI project scoping and use case assessment.
  • Prioritisation, phasing and deployment planning.
  • Coordination between business teams, the IT department, data and security teams, and partners.
  • Project management, acceptance testing, production rollout and monitoring.
  • Design of business agents tailored to existing tools and processes.

Scope of our engagement

  • Scoping, requirements and functional and technical studies
  • Benchmarking of models, platforms, software vendors and integrators
  • Specifications, procurement documents, tender evaluation and contracting support
  • Detailed design, integrator management and interface oversight
  • Prototypes, pilots and representative test datasets
  • Functional acceptance, accuracy, robustness, security and performance tests
  • Production readiness, training and hypercare

Deliverables and decision criteria

Scoping pack, traceable requirements, selection matrix, specifications, acceptance plan and sign-off records. Production approval relies on agreed thresholds, classified defects, support arrangements and a reversibility plan.

Prerequisites, controls and limitations

A convincing pilot does not prove production readiness. We test incorrect answers, missing data, access rights, injection attempts and unauthorised actions. The client makes the launch decision against documented criteria.

Discuss your AI project

Our method: from business need to measured value

  1. Understand

    Analyse the organisation, activities, people, data and IT landscape. Observe real work and pain points before selecting a solution.

  2. Prioritise

    Compare business value, feasibility and risk. Establish a baseline, a business owner and success criteria for each use case.

  3. Design

    Define the target process, architecture, interfaces, responsibilities, human approvals and performance indicators.

  4. Experiment

    Test a pilot on representative situations, including failure cases. Compare quality, cost and usefulness with agreed criteria.

  5. Deploy

    Integrate with applications, run acceptance and rollback checks, train users and support managers.

  6. Measure

    Track benefits, errors, costs, adoption and incidents. Regularly reassess models, data and controls.

FAQ

How can AI agents be deployed reliably?

Constrain tools and permissions, test adversarial and failure scenarios, require appropriate human approvals and monitor actions. An agent must be able to stop and hand a case to a person.

Bringing the right expertise together

Our role

Manage the project without confusing project advisory with compliance

The advisory team manages business requirements, trade-offs, the backlog, stakeholder coordination, acceptance testing and deployment. AI Act, GDPR and cybersecurity obligations are incorporated as project requirements, then examined in greater depth with the relevant specialists.

Scope

Use cases, data and integration

  • Target process, pain points, volumes, frequency, users and the expected level of AI autonomy.
  • Data sources, quality, access rights, confidentiality, retention and dependencies on existing tools.
  • Functional architecture, interfaces, models or providers involved, and supervision arrangements.
  • Quality, cost, time frame, explainability, security and user acceptance criteria.
Project stages

Manage the AI project life cycle

  • Define the objectives, governance, risks and criteria used to decide whether to proceed.
  • Prioritise use cases and build a pilot phase using representative data, users and scenarios.
  • Manage the design, integrations, providers, business acceptance testing and required controls.
  • Prepare deployment, support, usage measurement and the decision to scale or stop.
Deliverables

AI project management deliverables

  • Scoping document, value-feasibility-risk matrix and launch decision.
  • Prioritised backlog, functional architecture, and data and integration requirements.
  • Governance, RACI matrix, schedule, budget, decision log and risk monitoring.
  • Acceptance-testing strategy, production readiness criteria and usage measurement dashboard.
Project context

Decisions to make before deployment

  • Select the use cases that justify a pilot and those that should remain out of scope.
  • Choose between purchasing, configuration, development, a specialised agent or conventional automation.
  • Define autonomy limits, human validation requirements and escalation conditions.
  • Set the quality, cost, time frame and adoption thresholds required for wider rollout.
Your teams

AI project stakeholders

  • Executive sponsors and business leaders for value, scope and trade-offs.
  • IT, data and security teams for data, integrations, environments and controls.
  • Pilot users for scenarios, acceptance testing and adoption.
  • Legal teams, the Data Protection Officer, procurement and compliance teams for applicable requirements and suppliers.
Practical questions

Short answers

When should an organisation involve OLING?

OLING is relevant when an initiative affects information systems, processes, compliance or several stakeholders and requires independent scoping before decisions are made.

What deliverables can be expected?

Assignments produce diagnostics, scoping notes, roadmaps, decision matrices, action plans, steering materials and evidence tailored to the context.

Related expertise

Complete the AI project scoping process

The AI project must remain aligned with compliance, data protection and operational automation.

Related resource

AI and AI Act compliance

To classify use cases and define obligations, controls and evidence.

View page Related resource

GDPR compliance

For personal data processing, data protection impact assessments and processor relationships.

View page Related resource

Automation and practical AI

To identify operational gains and align AI, tools and processes.

View page
Discuss your project

Assess the use case and launch decision

The initial discussion covers the business problem, users, data, integrations, risks and success criteria for the first phase.

Discuss your AI project