AI Strategy Workshop for Leadership
What is AI workshop by Euristiq
hours of your time
AI use cases
of charge
Who is this workshop for?
AI workshop for boards and directors
Typical pain points
- Unclear how AI impacts long-term strategy and risk profile
- Pressure from market, investors, and competitors to “do something with AI”
- Fear of both missing out and wasting money on the wrong initiatives
- No shared view on where AI creates real business value vs hype
- Need for governance, prioritization, and investment clarity
Outcomes
- Clear decision framework for AI at business and portfolio level
- Structured view on risks, opportunities, and strategic trade-offs
- Alignment across leadership on priorities and direction
- Clarity on where to invest, postpone, or avoid
- Business-focused outcomes: ROI logic, risk framing, competitive positioning
AI workshop for CEO
and executive leadership teams
Typical pain points
- No clear AI strategy or execution sequence
- Business model under pressure from market changes and competitors
- Too many options: build, buy, partner, or wait
- Misalignment between business goals, technology, and teams
- Need to balance growth, cost, risk, and execution reality
Outcomes
- Clear, business-first AI direction tied to strategy and outcomes
- Prioritized options: where AI fits and where it doesn’t
- Alignment across leadership on goals and next steps
- Practical decision support (not theory)
- Investment clarity without technical deep-dives
AI workshop for CPO, Head of Product,
Product Owners, Innovation Leads
Typical pain points
- Unclear which AI use cases actually matter for the product
- Many ideas but no prioritization or ROI logic
- Pressure to add AI features to stay competitive
- POCs exist but no path to scalable products
- Misalignment between business, user value, and technical constraints
Outcomes
- Identification of high-impact AI use cases for product or portfolio
- Realistic, business-aligned AI roadmap
- Clear prioritization: build, test, or skip
- Bridge from ideas/POCs to execution-ready initiatives
- Shared understanding across product, business, and tech
AI workshop for Operations,
Support, Finance, Legal, etc.
Typical pain points
- Slow, manual, or costly processes
- Pressure to improve efficiency, quality, and scalability
- Uncertainty about what AI can truly automate vs assist
- Risk of choosing the wrong tool or initiative
- Need to justify investment with business impact
Outcomes
- Workflow-level AI opportunities tied to business outcomes
- Clear distinction between automation, augmentation, and redesign
- Practical options for efficiency and scalability improvements
- Input for defensible business case and prioritization
- Realistic view of effort, impact, and next steps
AI workshop for CTO,
VP Engineering, Tech Leads
Typical pain points
- Unclear AI scope and priorities from business side
- Uncertainty around data, security, architecture, and scalability
- Risk of building wrong solutions or long-term tech debt
- Unclear build vs buy vs integrate decisions
- Team misalignment around AI direction
Outcomes
- Business-aligned technical direction and constraints
- Clarity on feasibility, architecture, and integration paths
- Build vs buy vs customize guidance
- Prioritized, realistic technical starting point
- Alignment between business expectations and engineering reality
AI workshop for Head of Data,
Analytics, BI, Data Platform Owners
Typical pain points
- Data quality, access, or governance challenges
- Pressure to do AI without knowing if data is ready
- Unclear whether to invest in data or AI first
- Risk of failed AI initiatives due to data issues
Outcomes
- Assessment of data readiness and constraints
- Clarity on what is possible now vs later
- Prioritized data vs AI roadmap
- Alignment between data strategy and business goals
- Reduced risk of premature AI initiatives
AI workshop for Change,
HR & Transformation Leader
Typical pain points
- Fear of low adoption of new AI systems
- Skills gaps and readiness issues
- Resistance to change in teams
- Unclear impact of AI on roles and workflows
Outcomes
- Understanding of organizational impact of AI
- Clear view of adoption and change needs
- Signals on skills and role evolution
- Alignment between tech and people strategy
AI workshop for Leaders
with an Existing AI or Data Product
Typical pain points
- POC or MVP that doesn’t scale or deliver value
- Unclear next-stage product direction
- Architecture or adoption issues
- Uncertainty on what to build next
Outcomes
- External structured product and tech perspective
- Clarity on next-stage priorities
- Roadmap from experiment to production solution
- Alignment between product, tech, and execution
Mobilize leadership
for AI transformation
Benefits of an AI workshop
Clear starting point for AI
“We don’t know where to start with AI.”
We help you identify and prioritize the workflows where AI can create real business impact first—so you stop guessing and start with what actually matters.
From ideas to a real strategy
“We have ideas, but no strategy.”
We turn scattered ideas into a sequenced, practical roadmap that shows what to do now, what to plan next, and what to ignore.
Alignment across teams
“Our teams aren’t aligned.”
We bring business, technology, and data stakeholders to the same page, so decisions are faster, clearer, and easier to execute.
Realistic view on data, governance, and risk
“We’re worried about potential risks.”
We assess your readiness and constraints early, so you avoid costly surprises and unrealistic plans.
Focus on business results, not experiments
“We need results, not experiments.”
We focus on feasible, value-driven use cases that can move from idea to production—not endless pilots that go nowhere.
Clear next steps toward execution
Instead of leaving with more questions, you leave with concrete options: what to build, what to buy, what to integrate, and what to do next to move forward with confidence.
Start with AI strategy workshop
AI strategy workshop
for industry leaders
Typical finance industry pain points AI can solve
- Strict regulatory and risk constraints
- High operational and compliance costs
- Customer expectations for faster, better digital services
- Complex legacy IT systems and fragmented data
Outcomes
- Business-aligned AI use cases in operations, risk, compliance, and customer experience
- Clarity on feasibility, data readiness, and governance
- Prioritized, risk-aware AI roadmap
- Clear next steps toward controlled implementation
Typical insurance industry pain points AI can solve
- Slow, manual claims and underwriting processes
- High operational costs and fraud risk
- Pressure to improve customer experience and speed
- Legacy core systems and complex data landscape
Outcomes
- Clear AI opportunities in claims, underwriting, fraud, and service
- Prioritization between automation and decision support
- Realistic view of integration with core systems
- Roadmap from quick wins to strategic initiatives
Typical healthcare industry pain points AI can solve
- Staff shortages and operational inefficiencies
- Heavy administrative workload and slow processes
- Data silos and compliance constraints
- Pressure to improve quality of care and patient experience without increasing costs
Outcomes
- Identification of AI use cases in operations, administration, and clinical support
- Clear boundaries: what is realistic, safe, and compliant
- Prioritized initiatives with risk and impact in mind
- Direction toward pilots and scalable solutions
Typica manufacturing industry pain points AI can solve
- Pressure to improve efficiency, quality, and throughput
- Legacy production systems and fragmented data
- High costs from downtime, defects, and manual processes
- Unclear where AI delivers the most value: planning, quality, maintenance, or supply chain
- Fear of expensive and risky technology initiatives
Outcomes
- Clear map of high-impact AI use cases (quality, maintenance, planning, operations)
- Prioritization between automation, optimization, and decision support
- Realistic view of data, integration, and system constraints
- Step-by-step direction toward pilot and implementation
Typical retail industry pain points AI can solve
- Margin pressure and intense competition
- Demand forecasting, pricing, and inventory challenges
- Need to improve personalization and customer experience
- Disconnected systems across online and offline channels
Outcomes
- Clear AI opportunities in demand, pricing, operations, and customer experience
- Prioritization of growth vs efficiency use cases
- Realistic view of data and system constraints
- Actionable roadmap toward pilots and rollout
Typical transportation and logistics industry pain points AI can solve
- Inefficient planning, routing, and capacity usage
- High operational costs and service-level pressure
- Poor visibility across the supply chain
- Legacy systems and fragmented data
Outcomes
- Clear AI opportunities in planning, routing, forecasting, and operations
- Prioritization of quick wins vs strategic improvements
- Feasibility check: data, systems, and integration
- Practical roadmap toward execution
Typical travel industry pain points AI can solve
- Volatile demand and operational complexity
- Customer experience gaps and service inefficiencies
- Manual planning, support, and disruption management
- Pressure to optimize costs while improving service
Outcomes
- Identification of AI use cases in planning, service, and operations
- Clear distinction between automation and decision support
- Prioritized initiatives based on impact and feasibility
- Direction toward implementation-ready use cases
Typical Telco industry pain points AI can solve
- High operational complexity and cost pressure
- Network optimization, customer experience, and churn challenges
- Massive data volumes with unclear ways to use them effectively
- Legacy systems and slow transformation cycles
Outcomes
- Clear AI opportunities across network operations, customer experience, and internal processes
- Prioritization between efficiency, reliability, and growth use cases
- Feasibility view: data, architecture, and integration constraints
- Practical next steps toward execution
Typical Energy industry pain points AI can solve
- Asset-heavy operations and high downtime costs
- Pressure to optimize production, maintenance, and trading
- Increasing complexity from energy transition and regulation
- Data spread across multiple systems
Outcomes
- Identification of AI use cases in maintenance, optimization, and forecasting
- Prioritization between operational efficiency and strategic initiatives
- Realistic view of data and integration challenges
- Step-by-step path toward implementation
Typical Education industry pain points AI can solve
- Administrative overhead and limited resources
- Need to improve student experience and outcomes
- Disconnected systems and data silos
- Unclear where AI can help without overcomplicating things
Outcomes
- Identification of AI use cases in administration, operations, and learning support
- Clear boundaries: what is realistic vs hype
- Prioritized low-risk starting points
- Direction toward pilots and gradual adoption
Typical aviation industry pain points AI can solve
- High operational costs and tight margins
- Complex scheduling, maintenance, and disruption management
- Safety, reliability, and compliance constraints
- Legacy systems and slow innovation cycles
Outcomes
- Clear AI opportunities in operations, maintenance, and planning
- Feasibility view with safety and compliance in mind
- Prioritized roadmap balancing risk and impact
- Next steps toward pilots and scaled deployment
Typical constraction industry pain points AI can solve
- Low productivity, delays, and cost overruns
- Fragmented workflows across planning, execution, and reporting
- Poor visibility into risks, timelines, and resource usage
- Manual, document-heavy processes
- Unclear where digital and AI investments deliver real value
Outcomes
- Identification of AI opportunities in planning, risk, scheduling, and reporting
- Clarity on what can be automated vs augmented
- Prioritized roadmap tied to real operational bottlenecks
- Guidance on data and system readiness
What you get from AI strategy workshop
Pick the right agentic AI
strategies for your business
Adapting your existing software and APIs so AI agents can use them
Adapting your existing software and APIs so AI agents can use them
We prepare your current systems (ERP, CRM, internal tools, APIs) so AI agents can safely and reliably use them to read data, trigger actions, and complete tasks. You don’t replace your software—you make it “AI-operable.”
Automating workflows while hiding AI complexity from users
Automating workflows while hiding AI complexity from users
We embed AI directly into your product or internal tools so workflows run automatically in the background. Your users just see faster, simpler processes, and not “AI features” they need to learn.
AI agents that people use
directly as a product or feature
AI agents that people use
directly as a product or feature
We build AI agents that act as a new interface for your customers or employees, helping them get answers, complete tasks, or make decisions faster.
AI systems designed to
work with other AI systems
AI systems designed to
work with other AI systems
We design AI components that don’t just talk to people, but also coordinate with other AI agents, splitting work, checking results, and managing complex processes at scale.
Book AI workshop
to secure business growth
Euristiq at a glance
About Euristiq
Euristiq is an AI-native custom software development company, acknowledged for the excellence in AI automation, AWS Advanced Tier Service Partner, and ISO 27001:2022 certified.
Our services include AI workshop, AI-native and AI software development as well as traditional custom software development. We integrate AI into the existing products and build AI-powered ones from scratch. By hiring our AI developers you get the pros with the expertise in AWS Bedrock, Snowflake, vector database, LangChain, Kafka, and cloud-native AI infrastructure.
What our clients say
FAQ
How long does the workshop take?
Typically 1–6 weeks, depending on scope and stakeholder availability. The core workshops are focused and time-efficient. We focus on finding the solutions your business needs and can benefit from right away, and look at the technical debt that you can start working on immediately.
Is this technical or business-focused?
It’s business-first and decision-focused. We cover technical realities where needed, but the goal is to support strategy, prioritization, and next steps, not deep technical design. However, we have a senior tech team supervising the process and ready to step in whenever your business needs it.
Do we need to have AI expertise internally to start?
No. The workshop is designed for teams at different levels of maturity. The Euristiq AI development team deals with the requests ranging from “we’re just starting” to “we already have POCs or AI features in production.”
Will you recommend buying tools or building custom solutions?
We look at all options: buy, build, integrate, or combine. The recommendations are based on your business goals, data, systems, and constraints and not on a one-size-fits-all approach. You can pick human-assisted AI, in some cases autonomous AI will cover some operations, and so on. We look into the requests you have, AI readiness, and technical debt to suggest the options for you.
Can this help us decide where not to use AI?
Yes. A big part of the workshop is identifying what’s not worth doing right now so you avoid wasting time and budget on low-impact or high-risk initiatives. There are other technology options you can implement to improve the existing operations. In some cases, legacy systems become bottlenecks for AI implementation, so we suggest modernization first.
Do you also implement the solutions after the workshop?
Yes. In fact, that’s where most of our clients go next. The workshop is built to simplify execution. Once priorities and scope are clear, we can move straight into delivery. We can start with a PoC or pilot and scale to production. You don’t lose momentum, you don’t repeat strategy work, and you don’t need to re-onboard another vendor.
What happens after the workshop?
You’ll have a clear set of options and recommended next steps. Some teams use this to align internally and plan budgets. Others choose to move straight into implementation with us, starting with a PoC, pilot, or full delivery, using the workshop outputs as the foundation for execution.
Who should attend from our side?
Usually a mix of business, product, technology, and data leaders. For example: CEO/COO, Head of Product, CTO/VP Engineering, and a data or operations lead.
What if our data or systems are not ready for AI?
That’s exactly what the workshop is designed to uncover. You’ll get a clear, honest view of your readiness gaps across data, systems, and architecture, and what needs to be modernized first to make AI work in practice. Instead of guessing or overinvesting too early, you’ll know precisely where to focus and how to remove the blockers. And if modernization is needed, we can take you from that foundation work straight into AI implementation, using one coherent plan and one delivery team.
Is this suitable for enterprise-scale organizations?
Yes. The workshop is designed to work for complex organizations with multiple stakeholders, legacy systems, governance requirements, and real operational constraints.
What happens after the workshop is finished?
You’ll have a clear plan, a prioritized roadmap, and you can move straight into execution. We can immediately start with a PoC or pilot and scale to full delivery using the roadmap created in the workshop. Because the strategy, scope, and priorities are already defined, you save weeks or months and move from decisions to results faster.
How do we get started?
Start with a short call with our team. We’ll discuss your business context, goals, and constraints, and outline the right format and scope for your AI Strategy Workshop. From there, we’ll propose a clear plan and timeline—so you can move quickly from conversation to action.
