From operational challenges to AI opportunities
A cross-functional industrial team used the workshop to turn recognisable problems into four practical AI use cases and compare them on expected value and readiness.
Anonymised external example02.1 - Workshop Context & Outcome
Four use cases emerged from a problem-first workshop
Engineers and operations specialists examined where AI could support production planning, knowledge retention, quality assurance and access to technical information.
Participants started with problems from their own work and from adjacent departments. They expressed these as user stories, selected the strongest themes, developed them in a Use Case Canvas and connected them to relevant AI capabilities.
Smart production planning
Use demand, capacity, staff availability and energy information to support more realistic planning decisions.
Automated work instructions
Capture operational knowledge and turn it into clear, current instructions for production and onboarding.
AI-supported final inspection
Detect deviations, assess products against defined criteria and learn from recurring quality patterns.
Interactive knowledge assistant
Give employees natural-language access to internal technical and operational documentation.
02.2 - Workshop Approach
Start with the problem, then decide where AI is relevant
The workshop combined individual observation, group selection, structured use-case development and plenary assessment.
Surface challenges
Participants describe concrete problems from daily work and from broader operational processes.
Select
Teams compare the stories and choose the challenges with the strongest relevance or potential.
Develop
Selected problems are structured in a Use Case Canvas and connected to relevant AI capabilities.
Assess
The group discusses expected impact and how ready the organisation is to pursue each direction.
Start with a recognisable operational problem, express it clearly, then identify the AI capability that could improve the decision or workflow.
The AI Superpower Cards acted as a bridge between business challenges and possible AI functionality. They gave participants concrete examples of what AI can do without forcing the discussion toward a specific technology vendor or product.
02.3 - Impact & Readiness
Prioritise value and readiness, then validate the assumptions
The team assessed each use case on expected business impact and current readiness, considering culture, data and infrastructure.
What the workshop establishes
A shared view of the operational problem, intended users, expected value and the AI capabilities that may be relevant.
What still needs evidence
Data quality, technical feasibility, baseline performance, implementation effort, ownership, change requirements and the business case for a specific solution.
02.4 - Use Case
Smart production planning
An intelligent planning concept combines demand, capacity, staff availability and energy information so planners can make better decisions and steer production more realistically.
Prioritise what should happen next
Prioritisation and volume forecasting can support decisions about which jobs should run, when they should run and which resources are required.
Capacity, cost and delivery reliability
Better alignment can reduce avoidable downtime, excess capacity and understaffing. Energy-intensive production may also be shifted toward periods when energy and machine capacity are more favourable.
Why this case matters
Demand and available resources change continuously in a production environment. A planning approach based on current operational data can improve resource use and delivery predictability. For sales, that can create more confidence when responding to rush orders or non-standard delivery requirements.
The workshop established the relevance of the problem. A next phase would still need to determine which planning decisions can be improved, whether the required data exists and whether optimisation produces a measurable improvement over current planning practice.
02.5 - Use Case
Automated work instructions
The concept captures operational knowledge, structures it and turns it into current work instructions that employees can use during production and onboarding.
Knowledge becomes fragmented or disappears
Important information is not always documented in time, and experienced employees cannot always transfer their expertise in a structured way.
Capture, process and publish
Information can be collected and converted into reusable work instructions. Video recordings of experienced employees can supplement written instructions with practical demonstrations.
Used well, the approach can support onboarding and continuity. New and less experienced employees gain faster access to practical knowledge, while the organisation becomes less dependent on individual knowledge holders. The first validation question is whether structured documentation itself solves most of the problem before introducing more advanced AI retrieval or generation.
02.6 - Use Case
AI-supported final inspection
AI can support quality control by checking products against defined criteria, identifying deviations earlier and revealing recurring patterns in quality data.
Automated inspection
Products can be assessed against predefined standards. Deviations can be detected and classified consistently when the data and inspection method support it.
Move from inspection to quality learning
Data analysis can reveal trends and recurring causes. Dashboards can give operators and quality managers a current view of status and exceptions.
Traditional final inspection often depends on manual and visual checks. Automation may improve consistency and reduce subjectivity, but the more important long-term value comes from learning systematically from deviations and process data. A validation phase should first establish which defects matter, how reliably they can be observed and what action follows detection.
02.7 - Use Case
Interactive knowledge assistant
A knowledge assistant lets employees ask natural-language questions about internal technical and operational documentation and brings relevant information forward faster.
Ask instead of search
Employees ask a targeted question and receive relevant information from internal documentation without specialist search skills.
Structure and qualify information
Clustering, classification, data and text analysis, and automated qualification can help organise information and improve the relevance of retrieved results.
The value comes from reducing search time, reaching well-founded answers faster and solving technical problems with the right information at hand. The practical prerequisite is a reliable document base with clear ownership and update processes.
02.8 - AI Superpowers
AI Superpowers used in the workshop
These capabilities helped participants make business problems concrete and identify which type of AI functionality could be relevant.
| AI capability | Description | Production example |
|---|---|---|
| Detect anomalies | Detect data points that deviate from the normal pattern. | Flag an unexpected temperature rise in a machine component. |
| Intent recognition | Understand the intent behind a question, message or document. | Infer whether a customer request concerns a quotation, complaint or order confirmation. |
| Volume forecasting | Forecast future quantities from historical and current data. | Align production and resources with expected demand. |
| Clustering | Group data or objects based on similarities. | Group machines with similar failure patterns. |
| Speech to text / text to speech | Convert spoken audio to text and written text to speech. | Capture operational knowledge through spoken input. |
| Dataset completion | Fill missing values or information in datasets. | Fill gaps in sensor measurements to provide a more complete operational picture. |
| Prioritisation | Rank tasks or problems based on impact and urgency. | Identify which maintenance or production tasks require attention first. |
| Process analysis | Analyse digital traces to reveal processes, delays and improvement opportunities. | Identify bottlenecks in a production line. |
| Compliance monitoring | Check whether processes and records comply with defined rules. | Flag missing or deviating process steps. |
| Dashboarding | Present data in clear charts and key indicators. | Show production, energy use, capacity and disruptions at a glance. |
| Automated qualification | Determine automatically whether something meets predefined criteria. | Assess products, suppliers or documents against defined standards. |
| Classification | Sort data into predefined categories. | Classify components in images as approved or rejected. |
| Data and text analysis | Search large volumes of information for patterns and insights. | Extract recurring problems from feedback, reports and documentation. |
| Large language model | Understand and generate natural language. | Retrieve information from complex documentation through natural-language questions. |
02.9 - Recommendation & Next Step
A practical first step toward targeted AI adoption
The workshop produced four use cases that merit a clear next step. Before investing in delivery, each case should be tested for business value, data availability, process ownership, technical feasibility and implementation risk.
The method creates a useful sequence: start with the operational problem, define the intended value, test feasibility and only then select the required AI capabilities. That gives the organisation a concrete next move rather than a general AI ambition.
Recommended next decision
Select one or two use cases for a short validation phase with clear success criteria, a named owner, an explicit operational question and the data required to answer it.
From AI opportunities to an actionable validation roadmap
A manufacturing team identified recurring problems in tool management, production planning, forecasting, data quality, knowledge transfer and commercial targeting, then used the workshop to choose what should be investigated first.
Anonymised external example03.1 - Workshop Context & Outcome
A concrete first priority emerged, but validation still has to earn the investment
The workshop brought together management, engineering, CAM, automation, finance/IT and work preparation to identify operational problems and examine where AI or related data-driven technology could help.
The discussion surfaced recurring issues in tool management, production planning, time estimation, pricing, data quality, knowledge transfer, technology adoption, ownership and cross-functional decision-making. A common pattern sat underneath several of these issues: operational data existed across different systems and informal sources, but teams could not consistently access, trust or combine it when making decisions.
Participants translated selected problems into four concepts: intelligent tool management, planning intelligence, operational knowledge management and a product-customer recommender. Workshop voting placed tool management first, followed by planning. That ranking reflects participant perception of impact, feasibility and organisational readiness. It should guide what to investigate first, rather than be treated as proof that implementation is technically or economically viable.
Recommended direction
Validate tool management first. In parallel, map the planning process and begin capturing critical operational knowledge where it is concentrated in individuals. Use the evidence from those activities to determine which larger investments are justified.
03.2 - Key Problem Areas
The workshop exposed both technology opportunities and management issues
Some themes are suitable for data or AI intervention. Others concern process, ownership or organisational behaviour and need management action alongside any technical solution.
| Theme | Assessment |
|---|---|
| Tool management & data gaps | Teams lacked dependable visibility into tool availability, usage and remaining life. Relevant information was incomplete or distributed across systems and manual records, limiting planning and automation. |
| Production planning | Planning was frequently reactive and involved detailed intervention from multiple stakeholders. Last-minute changes, machine loading and delivery commitments were difficult to reconcile consistently. |
| Order & process-time forecasting | Machining and processing-time estimates were difficult because product, material, process and tool-performance data were incomplete or inconsistent. Manual estimation remained important. |
| Pricing & calculation | Quotation and costing logic did not consistently reflect product complexity, creating slow calculations and avoidable errors. This is primarily a process and model-quality issue before it becomes an AI issue. |
| Quality & process reliability | Variation in inputs and production execution contributed to defects, rework and low trust in system data. Better data capture and process discipline are prerequisites for predictive use cases. |
| Customer targeting | The company lacked a structured way to connect production capabilities and product characteristics with attractive customers or markets. The opportunity depends on the quality of product, customer and market data. |
| Knowledge management | Important operational knowledge remained informal and person-dependent, making onboarding, standardisation and consistent execution harder. |
| Technology adoption | Limited time, unclear ownership and reliance on familiar practices slowed adoption. New tools need clear owners, visible benefits and involvement from the people who use them. |
| Collaboration, decisions & ownership | Unclear roles and decision ownership led to repeated discussions, broad meeting participation and weak follow-through. Technology cannot resolve this without explicit governance and accountability. |
How the focus areas were selected
Participants filtered the wider problem set toward issues that appeared data-driven, repetitive and scalable. One group concentrated on commercial, financial and planning questions, leading to the product-customer recommender and planning concept. The other concentrated on tool management, operational knowledge and adoption barriers, leading to tool management and knowledge-management concepts.
03.3 - AI Opportunities
The team translated the problems into four connected use cases
Each concept addresses a real problem, but each also carries a different evidence burden. The workshop helped decide what should be investigated first.
| Use case | Problem to solve | Working concept | What must be validated |
|---|---|---|---|
| Intelligent tool management | Insufficient visibility into tool availability, usage and remaining life. | Combine ERP, machine/runtime and tool data to provide reliable status and, where data supports it, forecast tool requirements or remaining life. | Data availability and identifiers, quality, integration effort, user decisions, and whether prediction adds value beyond a reliable dashboard. |
| Planning intelligence | Reactive planning and limited predictability of capacity, lead times and disruptions. | Improve planning visibility first, then test process-mining and predictive methods where historical data can support them. | The actual planning workflow, quality of lead-time, BOM, routing and order-history data, causes of schedule changes, and achievable decision improvements. |
| Operational knowledge management | Critical production knowledge is informal and difficult to reuse consistently. | Capture and structure high-value operational knowledge so employees can find and apply it at the point of work. | Which knowledge creates the most risk when unavailable, content ownership, update process, user search behaviour, and whether AI retrieval is needed. |
| Product-customer recommender | Sales lacks a structured way to identify promising product-customer combinations. | Use product and customer data, potentially enriched with external information, to rank commercial opportunities. | CRM and product-data completeness, definition of a good recommendation, availability and legality of external data, and measurable uplift over current sales selection. |
03.4 - Impact & Readiness Assessment
Use participant voting to choose the first validation target
Participants scored the concepts on business impact, technical feasibility and organisational readiness. Tool management received the strongest combined score. Planning scored highly on impact but lower on feasibility and readiness, while the commercial recommender and knowledge-management concepts ranked behind them.
Indicative order from the workshop
How to interpret the score
The exercise captured informed internal judgement. It did not verify technical readiness. A separate review still needs to examine source systems, identifiers, interfaces, historical coverage, security, data quality, ownership and available capacity.
Use the workshop score to decide what to investigate first, not to commit to a full implementation timeline or expected financial return.
03.5 - Recommended Sequence
Turn the workshop priority into a sequence of decisions
The strongest recommendation is to validate the first operational problem before turning the four concepts into a large transformation programme.
| Stage | Focus | Decision to earn |
|---|---|---|
| 1. Validate tool management | Inventory tool-related data, map the decisions affected by tool availability, establish a baseline and build the smallest useful visibility prototype. | Is the data sufficiently reliable, and does improved visibility reduce measurable operational loss? |
| 2. Diagnose planning | Map the current planning process, sources of schedule change and required data. Use process mining only if event data is suitable. | Which planning decisions can be improved, and what data or process changes are required first? |
| 3. Capture critical knowledge continuously | Start with high-risk, frequently repeated or person-dependent procedures. Assign content owners and a review cadence. | Does structured knowledge reduce search time, variation or onboarding effort? Add AI retrieval only where it improves access. |
| 4. Test the commercial recommender when data is ready | Define the recommendation objective, clean CRM and product data, then run a controlled ranking experiment. | Do recommendations produce better qualified opportunities or conversion than the current approach? |
03.6 - Validation Detail
What each next step needs to prove
1. Tool management validation
Begin with a data and process inventory covering ERP records, machine/runtime data, tool databases and spreadsheets. Define a baseline for tool-related delays, stockouts, emergency actions and downtime. Build a minimum useful view of availability and usage before committing to lifespan prediction.
A 4 to 6 week pilot can be appropriate once the required data is accessible, but the duration should follow the production cycle and the amount of evidence needed.
2. Planning validation
Map how orders move from intake to planning and production, including manual overrides and the reasons schedules change. Audit lead times, routings, BOMs, machine constraints and order history. Only then determine whether process mining, optimisation, predictive models or simpler planning-rule improvements fit the problem.
Any target for fewer last-minute changes, higher forecast accuracy or better on-time delivery should follow baseline measurement and analysis of the actual causes.
3. Product-customer recommender validation
Define what a useful recommendation means for sales: a new account, cross-sell opportunity, product-market match or another commercial outcome. Align CRM and product data around that objective. Test recommendations against historical or controlled sales outcomes before introducing external enrichment at scale.
4. Knowledge-management validation
Start knowledge capture early rather than waiting for a separate platform project. Focus on procedures where missing knowledge creates delays, errors or dependency on specific employees. A wiki or existing collaboration platform may be sufficient initially. The system needs clear content owners, review dates and usage measures before adding an AI interface.
03.7 - Integration Principles
Share foundations without forcing one large architecture
The four concepts share data and knowledge, but they do not need to become one programme before their individual value is proven.
Tool-management data may later improve planning. Planning can produce capacity information that supports commercial decisions. Knowledge management can document rules and procedures that emerge from both initiatives. These dependencies are plausible and worth designing for where inexpensive, then confirming during validation.
Standardise shared identifiers, ownership and documentation early while keeping each use case independently testable.
This reduces duplicate data work without creating a large transformation programme before the organisation knows which concepts deserve investment.
03.8 - Foundation for Execution
A small set of disciplines matters more than a generic AI-readiness programme
The organisation can start with targeted governance tied directly to the selected use cases.
Define the data needed for a decision
Assign owners to the data domains used in each pilot. Define minimum quality rules for the decision being supported and measure compliance. Avoid broad data-cleaning programmes without a use-case requirement.
Design the new workflow with the users
Identify who makes the decision today, who will use the new output, who owns the process and what behaviour must change. Measure whether the new workflow replaces manual work or improves the decision.
Name accountable business and technical owners
A small cross-functional group can coordinate dependencies, but it should not replace explicit ownership. Review pilots against operational outcome, adoption, data quality, technical effort and expected economics.
Run a short discovery and validation sprint
Produce a documented problem baseline, source-data map, integration assessment, prototype scope, success measures, named owner and a go or no-go recommendation.
04 - What the Two Examples Show
The same workshop should not produce the same answer
Both examples use a problem-first Explore AI approach, but the outputs differ because the organisations started with different problems, data conditions and levels of decision readiness.
Opportunity Discovery
- Useful when the organisation needs to understand where AI could be relevant.
- Creates a shared shortlist of concrete use cases.
- Helps different departments compare problems and intended value.
- Ends with a clear choice of which use cases deserve validation.
Prioritisation & Roadmap
- Useful when several opportunities are already visible but the order is unclear.
- Makes dependencies in data, process and ownership explicit.
- Turns participant judgement into a testable first priority.
- Separates immediate validation from longer-term ideas.
05 - The Explore AI Workshop
What you can expect from the workshop
COMPUTD brings together the people who understand the work, makes the operational problems visible and helps the team decide where AI deserves attention. The workshop is designed to improve the next decision, not to produce a longer list of technologies.
A shared problem view
Management, operations and technical specialists compare how the same process looks from different roles and agree on the problems that matter.
Concrete AI use cases
Promising problems are translated into specific applications with an intended user, value hypothesis and relevant AI capabilities.
Priorities and dependencies
The team distinguishes attractive opportunities from ideas that first require better data, clearer process ownership or further investigation.
A responsible next step
The workshop ends with a defined validation question, so the organisation can test assumptions before committing to a larger build.
06 - From Workshop to Next Decision
Which operational problem should your organisation investigate first?
A COMPUTD Explore AI Workshop gives your team a structured way to answer that question together. We bring the people who understand the work into one room, make the competing problems and assumptions explicit, and leave with a small number of opportunities and a clear next decision.
Useful when AI interest is broad
Move from general ideas and individual requests to a shared view of where AI may create operational or commercial value.
Useful when there are already too many ideas
Compare opportunities, expose dependencies and decide what should be tested first instead of starting several disconnected experiments.
These client examples are based on existing workshop reports. Personal names, client identification and organisation-specific details have been removed or generalised. No project results have been invented.