Stop experimenting with AI. Start building measurable business value.
Discover exactly where AI can increase revenue, reduce operating costs and improve decision-making — before you invest in another tool, agent or development project.
Private workshops from $1,500.
AI is easy to try. It is much harder to implement correctly.
Business leaders are being told that AI will transform their companies. As a result, many organisations are already experimenting with ChatGPT, automation platforms, AI agents and new software. But access to AI tools does not automatically create business value.
A successful AI project begins with the right business opportunity — not with the latest tool.
Your most valuable AI advantage is already inside your business.
Public AI models understand general knowledge. They do not automatically understand your organisation. Your competitive advantage lives in the data, systems and expertise you already own.
Internal assets
- Customer history
- Pricing rules
- Business processes
- Internal software
- CRM and ERP data
- Documents and reports
- Proprietary methods
- Operational experience
- Employee knowledge
- Supplier information
- Product and service data
- Historical performance
- Existing APIs and databases
External signals
- Market movements
- Customer behaviour
- Pricing signals
- Competitor activity
- Industry trends
- Economic indicators
- Public datasets
- Commercial intent signals
Business outcomes
- Identify revenue opportunities earlier
- Make faster and better-informed decisions
- Personalise customer interactions
- Improve forecasting
- Reduce repetitive analysis
- Automate selected workflows
- Detect anomalies and risks
- Make existing software more intelligent
- Give teams immediate access to relevant knowledge
The objective is not to add AI to your business. The objective is to make your business perform better using the data, systems and expertise you already own.
Why unguided AI projects produce poor results
No business case
The project begins with 'we need AI' instead of a specific financial or operational objective.
Wrong use case
The company automates a visible task rather than the task with the strongest business impact.
Weak data
The AI cannot produce reliable results because the required information is incomplete, fragmented or inaccessible.
No integration plan
The solution operates separately from the CRM, ERP, database, documents or workflows it needs.
Undefined agent responsibility
The agent has broad instructions but no clear scope, escalation rules or measurable objective.
No adoption strategy
Employees do not trust, understand or consistently use the new solution.
Technology before economics
Development begins before operating costs, usage volume and expected returns are understood.
Vanity KPIs
The project measures prompts, messages or generated content instead of revenue, cost, speed, quality and conversion.
This workshop prevents those mistakes by aligning business goals, processes, data, people, technology and measurement before implementation.
What your leadership team will achieve in three focused hours
1. Understand what AI can realistically improve
Learn the practical differences between generative AI, AI agents, workflow automation, analytics and traditional software. Understand where AI adds value, where simple automation is sufficient and where human validation remains essential.
2. Map revenue, cost and productivity opportunities
Identify processes where AI may:
- Increase sales
- Improve lead qualification
- Accelerate proposals and follow-up
- Increase customer retention
- Detect upselling and cross-selling opportunities
- Reduce administrative effort
- Improve reporting
- Accelerate document processing
- Support employees
- Shorten decision cycles
- Improve customer service
- Strengthen existing products or services
3. Connect opportunities to your data and systems
For each use case, identify:
- Required internal data
- Relevant documents
- Available APIs
- Software integrations
- External market data
- Access permissions
- Human validation requirements
- Data-quality limitations
4. Prioritise the best use cases
Evaluate each opportunity according to:
- Revenue potential
- Cost reduction
- Time saved
- Strategic importance
- Implementation effort
- Data readiness
- Adoption difficulty
- Operational risk
- Time to measurable value
5. Define initial KPIs and the first pilot
Select the first two or three opportunities and define:
- Expected outcome
- Success metrics
- Responsible stakeholders
- Required data
- Implementation sequence
- Pilot boundaries
- Next actions
You will not leave with fifty disconnected AI ideas. You will leave with clear priorities and a practical direction.
Not a passive presentation. A structured business working session.
The workshop combines concise guidance with interactive exercises. Participants work through real or anonymised business situations to:
- Improve a recurring process
- Identify where an AI agent would create value
- Determine what the agent should and should not do
- Analyse the data required for a reliable result
- Explore how internal information can be combined with market signals
- Define the output expected from the system
- Identify human approval points
- Select measurable KPIs
- Evaluate implementation feasibility
The objective is not to turn executives into AI engineers. The objective is to help them make better AI investment and implementation decisions.
Three hours designed for busy decision-makers
The agenda can be adapted to the organisation's priorities and participant profiles.
Small improvements can create significant annual value.
AI does not need to replace entire departments to create a positive return. Consider a simple productivity example.
If 10 managers recover only 30 minutes per working day, the organisation recovers approximately 1,100 working hours per year.
If 15 participants recover the same amount, that becomes 1,650 working hours per year.
Productivity value calculator
Revenue opportunity calculator
This calculator provides an illustrative productivity estimate. Actual results depend on the selected use case, implementation quality, adoption and business conditions.
Every AI project should have a financial definition of success.
AI should not be measured only by the number of prompts, users or messages generated. Depending on the selected use case, meaningful KPIs may include:
Revenue KPIs
- Lead conversion rate
- Revenue per opportunity
- Average deal value
- Sales-cycle duration
- Customer-retention rate
- Upselling revenue
- Number of qualified opportunities
- Proposal response time
Cost and productivity KPIs
- Hours saved per month
- Cost per processed transaction
- Administrative time
- Average handling time
- Report preparation time
- Document-processing time
- Error and rework rate
- Percentage of work automated
Operational KPIs
- Response time
- Resolution time
- Forecast accuracy
- Data-access time
- Employee onboarding time
- Escalation rate
- Human-review requirement
- AI-assisted task success rate
If the success of an AI project cannot be measured, the project is not ready to begin.
Cloud AI, private AI or a hybrid approach?
The technology choice matters, but it should follow the business use case — not lead it.
Public cloud AI
May be appropriate when rapid deployment, access to leading models and flexible consumption are priorities, and the organisation is comfortable processing approved information under the provider's terms.
Private or self-hosted AI
May be appropriate when data control, confidentiality, sovereignty, predictable high-volume usage or deeper infrastructure ownership are priorities.
Hybrid AI
May use public cloud models for general, non-sensitive tasks and private environments for confidential business data, internal knowledge or controlled workflows.
The workshop briefly evaluates the most appropriate direction for each prioritised use case.
What your organisation receives
- Pre-workshop executive questionnaire
- Private three-hour workshop
- Up to 12 participants
- Concise business-focused AI guidance
- Interactive practical exercises
- Process and opportunity mapping
- AI use-case worksheets
- Revenue and cost opportunity identification
- Data, software and API requirement mapping
- Initial KPI definitions
- Impact and feasibility scoring
- Cloud, private or hybrid guidance where relevant
- Prioritised list of opportunities
- Recommended first pilot
- Concise executive summary
- 45-minute management follow-up meeting
You do not leave with general AI knowledge. You leave with a practical direction for your organisation.
Led by Yassine Arfane
Yassine Arfane brings more than 15 years of experience across enterprise technology, information systems, infrastructure, cloud environments, software architecture, SaaS platforms, business automation and AI-powered applications. His background combines strategic understanding with real implementation experience.
"The objective is not to recommend AI everywhere. It is to identify where AI can realistically improve performance, reduce operating costs and support growth."
Choose the right starting point
Executive AI Opportunity Briefing
For senior leaders who need a focused understanding of the opportunity, risks and possible priorities before involving a wider management team.
- Executive AI overview
- Business-goal discussion
- Initial opportunity areas
- Implementation considerations
- Recommended next step
The $600 fee can be credited toward a larger workshop booked within 30 days.
AI Business Opportunity & Implementation Workshop
The core private workshop for your leadership team.
- Business-focused AI guidance
- Interactive exercises
- Process and use-case discovery
- Data, software and API mapping
- Revenue and cost opportunity analysis
- Prioritisation
- Initial KPI definitions
- Recommended first pilot
- Executive summary
- Follow-up meeting
AI Transformation Workshop + 90-Day Roadmap
For organisations preparing to move from experimentation to structured implementation.
- Everything in the standard workshop
- Two or three executive interviews
- Deeper business-process review
- Review of current systems and data
- Detailed use-case ranking
- Architecture recommendations
- Public, private or hybrid AI guidance
- Initial governance recommendations
- Prioritised 90-day roadmap
- Executive presentation
Custom implementation, integrations, AI applications, agents and infrastructure are scoped and priced separately.
The 30-minute value guarantee
Attend the first 30 minutes of the workshop. If you believe the session is not relevant, practical or valuable for your organisation, tell us before the first 30 minutes are completed. We will stop the workshop and refund 100% of the workshop fee. No complicated procedure. No obligation to continue.
The guarantee applies when the request is made before the completion of the first 30 minutes and before proceeding with the extended interactive discovery session.
What happens after the workshop?
There is no obligation to purchase implementation services. Your organisation can use the findings internally, work with another provider or continue with VYANIS. Where appropriate, VYANIS can separately support:
Each implementation project is assessed, scoped and priced separately based on the selected use case, systems, data, integrations and expected outcomes.
Frequently asked questions
Request the workshop programme
Tell us about your organisation. We reply within one business day.
Do not invest in another AI tool before you know what should be built.
Your organisation does not need more disconnected experiments. It needs clarity on:
- What should be improved
- Where the financial value exists
- Which data should be used
- Which systems should be connected
- What an AI agent should do
- How success will be measured
- Which project should be implemented first