AI Adoption and Implementation
AI Adoption and Implementation
What makes this programme different
Move from AI experimentation to practical adoption. We provide AI readiness assessment, workflow discovery, team training, reusable assets and implementation support.
- Business-led, not tool-led – We begin with your organisational objectives, workflows and people rather than recommending technology for its own sake.
- More than prompting – The programme addresses skills, platform capabilities, business systems, workflows, governance and sustainable adoption.
- Built around real work – Participants apply AI to relevant tasks, documents, decisions and processes within their roles.
- Practical implementation support – Training is supported by workflow discovery, reusable assets, implementation guidance and adoption planning.
- Adapted to your technology environment – The programme can be aligned to Microsoft 365 Copilot, ChatGPT, Claude, Gemini, Google Workspace and other approved business platforms.
- Designed for sustainable capability – Teams receive frameworks, prompt libraries, workflow templates, playbooks and practical resources they can continue using after the engagement.
Participants don’t just learn what to do — they learn how to think.
Overview
The World-Class Account Manager Programme is a live, facilitated capability upgrade for account managers, client success professionals, and digital marketers who want to lead client relationships strategically — not reactively.
This programme is designed for agency-sponsored delegates and ambitious professionals who want to step up their commercial thinking, interpret data with confidence, and become trusted advisors to their clients.
This is not entry-level training.
It is a practical, decision-focused programme grounded in real agency scenarios and real client complexity.
Delivered live online over three weeks, participants apply the learning directly to live or anonymised client accounts.
This programme is ideal for:
- Account managers (junior to senior) in digital or marketing agencies
- Client success managers and account leads
- Digital marketers moving into client-facing or strategic roles
- Agency teams needing to standardise account management capability
- Professionals committed to career growth and leadership progression in 2026
It is especially valuable if you:
- Feel confident managing clients but struggle with strategic decision-making
- Report on performance but don’t always know what to recommend next
- Avoid difficult commercial or scope conversations
- Want to move from “safe” execution to confident client leadership
- Are setting clear professional development goals for the new year
Curriculum Overview - Effective AI adoption is not achieved through a once-off prompting workshop. It requires several interconnected layers of capability.
Layer 1 – Prompting and critical thinking
Participants learn how to communicate effectively with AI, provide relevant context, structure requests, assess outputs and improve results through iteration.
This layer includes:
- Prompt structure and reasoning
- Context and instruction design
- Role and objective definition
- Output and format requirements
- Verification and quality control
- Human judgement and accountability
Layer 2 – Using the full capabilities of AI platforms
Employees move beyond basic chat interactions and learn to use the broader capabilities available within approved AI tools.
Depending on the selected platforms, this may include:
- Personalisation and preferences
- Projects and workspaces
- Reusable prompts and instructions
- Research and document analysis
- Notebooks and knowledge collections
- Collaborative AI assets
- Purpose-built assistants or agents
- Shared team resources
Layer 3 – Using native AI across business platforms
Employees learn how AI capabilities embedded within existing business systems can support their work.
This may include appropriate use across:
- Microsoft 365
- Google Workspace
- CRM platforms
- Project management systems
- Marketing platforms
- Reporting and analytics tools
- Knowledge management environments
- Customer service systems
Layer 4 – Connecting AI across the wider workflow
The final layer examines how AI capabilities can work together across the organisation’s broader technology stack.
The focus is on improving complete workflows rather than using isolated tools for disconnected tasks.
Examples may include:
- Research to analysis to reporting
- Meeting preparation to documentation to follow-up
- Data review to insight development to decision support
- Customer feedback to categorisation to recommendations
- Content briefing to production to review and distribution
- Workflow documentation to process improvement planning
Programme structure
The final programme is customised, but a typical AI adoption and implementation engagement includes the following phases.
Phase 1 – AI readiness and requirements assessment
We assess the organisation’s current AI environment, priorities and adoption maturity.
This may include:
- Leadership and stakeholder discussions
- Current platform and licence review
- Employee skills and confidence assessment
- Existing AI usage
- Governance and data considerations
- Departmental requirements
- Adoption barriers
- Business priorities and desired outcomes
Phase 2 – Technology, access and governance alignment
Before practical training begins, we establish what employees can access and how the approved platforms should be used.
This may include:
- AI platform and licence confirmation
- Employee access checks
- Feature availability
- Internal policies and guidelines
- Confidentiality and data requirements
- IT and security considerations
- Approved use cases
- Escalation and support requirements
This phase reduces the risk of delivering training on tools or features participants cannot access.
Phase 3 – Workflow discovery and prioritisation
We identify where AI can create practical value within relevant departments and roles.
Workflows are evaluated according to:
- Business value
- Frequency
- Time and effort required
- Data sensitivity
- Human judgement requirements
- Implementation difficulty
- Risk
- Potential for reuse or scale
The output is a prioritised set of opportunities rather than a long list of disconnected ideas.
Phase 4 – Role-specific training and capability building
Training is designed around participant roles, approved platforms and prioritised workflows.
Depending on the organisation, topics may include:
- Prompting and instruction design
- Research and synthesis
- Document creation and analysis
- Reporting and decision support
- Meeting and communication workflows
- Data interpretation
- Marketing and customer workflows
- Project and operational support
- Reusable AI resources
- Verification and responsible use
Phase 5 – Reusable asset development
Participants and teams create assets that support consistent application after training.
These may include:
- Shared prompt libraries
- Workflow templates
- Standard instructions
- Team Projects or Notebooks
- AI playbooks
- Verification checklists
- Role-specific guides
- Use-case libraries
- Implementation checklists
- Responsible-use guidance
Phase 6 – Implementation support
Teams receive structured support as they begin applying AI within real work.
This may include:
- Guided implementation sessions
- Workflow clinics
- Troubleshooting
- Output review
- Prompt and process refinement
- Departmental working sessions
- Manager support
- Progress check-ins
- Additional coaching or training
Phase 7 – Adoption and impact review
We assess progress, identify barriers and recommend the next phase.
The review may consider:
- Employee confidence
- Frequency and quality of use
- Workflow adoption
- Reusable asset usage
- Time or effort saved
- Implementation barriers
- Additional skills required
- Future use cases
- Platform or governance gaps
What your organisation will walk away with
Depending on the agreed scope, the programme may provide:
- A clearer organisational AI adoption strategy
- An AI readiness assessment
- A prioritised workflow opportunity map
- Improved employee AI confidence and capability
- Role-specific AI use cases
- Shared prompt and workflow resources
- A practical AI playbook
- Departmental implementation plans
- Guidance for responsible and appropriate AI use
- Better alignment between leadership, employees and IT
- Reduced fragmented or duplicated AI activity
- A phased roadmap for continued adoption
- A framework for monitoring progress and value
How this benefits organisations
The AI Adoption and Implementation Programme helps organisations to:
- Move beyond isolated AI experimentation
- Improve the return on existing AI and software investments
- Build consistent capability across teams
- Identify relevant, practical AI opportunities
- Reduce unnecessary platform subscriptions
- Improve productivity and knowledge work
- Standardise successful practices
- Address adoption barriers before they become entrenched
- Improve employee confidence without removing human accountability
- Create reusable organisational knowledge and assets
- Connect training to real workflows and business outcomes
- Establish a foundation for responsible, scalable AI use
How the engagement works
This programme is customised because every organisation has different tools, priorities, risks and levels of AI maturity.
Step 1: Initial requirements discussion
We discuss your objectives, audience, existing platforms, challenges and desired outcomes.
Step 2: Recommended scope
The Training Group recommends the most appropriate combination of assessment, workflow discovery, training, asset development and implementation support.
Step 3: Tailored proposal
You receive a written proposal outlining the scope, deliverables, delivery format, timeline and investment.
Step 4: Readiness and setup
We complete the necessary stakeholder, platform, access and participant preparation before delivery.
Step 5: Delivery and implementation
The agreed programme is delivered online, in person or through a blended approach.
Step 6: Progress review
We evaluate adoption progress, implementation barriers and recommended next steps.
Investment
The investment depends on:
- Number of participants and departments
- Current AI maturity
- Platforms included
- Required assessments
- Number and duration of training sessions
- Workflow discovery requirements
- Reusable assets and documentation
- Implementation support
- Online or in-person delivery
- Location and travel requirements
About the strategist and facilitator
Irma Karsten is an AI strategist, digital marketing strategist, corporate trainer and curriculum designer with more than 25 years of experience in strategy, implementation and capability development.
As the founder of The Training Group, Irma helps organisations translate AI capabilities into practical skills, prioritised workflows and repeatable ways of working.
Her approach combines AI adoption, process thinking, learning experience design and real-world implementation. She works with leadership teams, corporate departments, agencies, training providers and professional teams in South Africa and internationally.
Irma’s AI adoption framework extends beyond prompting. It considers the full capabilities of AI platforms, native AI within existing business systems, connected workflows, reusable team assets, responsible use and the organisational conditions required for sustainable adoption.
