01 / DISCOVER
Diagnose before designing.
Understand the business challenge, the work, the people, the context and the evidence before deciding what to build.
Learning & Capability · Experience Design · Digital · AI
Learning is not always the answer. Better performance is.
Helping organisations understand what is getting in the way of performance — and design the right combination of capability, learning, experience and technology to change it.
20+ years at the intersection of learning, design, technology and business.Portfolio · Point of view · Selected work
ashwingarde@hotmail.com
ashwingarde@hotmail.com
01Build a new capability
02Help people work differently
03Scale learning
04Use AI thoughtfully
05Turn learning into performance
Thought Process
Start with the problem.
A request for training is a hypothesis, not a diagnosis. I start by understanding the business objective, where performance is breaking down, who is affected, and what evidence would tell us that something changed.
02 / DEFINE
Frame the real performance need.
Separate the requested solution from the underlying need. Define what people need to do differently and what is preventing that today.
03 / DESIGN
Design for what people need to do.
Move beyond awareness and recall. Create opportunities to practise, make decisions, get feedback and apply learning in context.
04 / EXPERIENCE
Technology should change the experience.
Digitising an old experience is not transformation. Use technology where it changes access, practice, support, collaboration or performance.
01CaptureUnderstand the business challenge
02AnalyseIdentify the nature of the need
03SelectChoose the intervention that best fits
04ImplementDesign, develop, deploy and test
05MeasureLook for application and business impact
5Di lensIterateUse evidence and feedback to improve the solution
Selected Work
Problems I've helped solve.
Four deeper cases show how I approach performance support, application learning, visual explanation and experiential learning. The emphasis is not on the format produced, but on the problem framed, the design decision made, and the experience created.
Solving Performance Problems
Move help into the flow of work.
A complex enterprise environment was generating large volumes of support tickets. Instead of retraining everyone, we analysed where users were struggling and embedded the smallest useful intervention at those moments.
Learning by Doing
Teach the work, not just the tool.
A complex scientific application became part of a realistic court-case scenario. The learner used the application as a scientist would: to examine information, make decisions and progress the case.
Making Complexity Understandable
Don't turn a manual into a PDF.
Interactive exploded views and virtual assembly changed the experience from finding information to exploring how components relate and fit together.
Making Knowledge Experiential
Don't describe what people can experience.
An immersive biotechnology course for non-scientists used animation and virtual-lab activities to make abstract scientific processes observable and actionable.
Building Learning Capability
Design the system behind the learning.
Learning at scale requires more than good instructional design. It needs the right team shape, delivery model, governance, capacity plan and commercial model.
Build for changing demand.
Plan multidisciplinary teams across analysis, design, visualisation, development, QA and project management. Ramp capacity as work moves from discovery to production and then into QA and release.
Illustrative: ramp up → steady state → ramp down
Choose the method for the work.
Predictive, Agile and hybrid approaches are tools — not identities. Stable requirements and sequential dependencies favour predictive planning; evolving solutions and frequent feedback favour iteration. Many learning programmes need both.
Predictive ↔ Hybrid ↔ Agile
What is the client buying: capacity, a deliverable, or value?
Resource model vs value model
My view: large-volume production or conversion work often belongs in a resource/T&M model. A repeatable learning solution that behaves like a product — for example, a design-thinking programme — should increasingly be positioned around the value it creates, not merely the people-hours required to build it.
| Resource / T&M | Deliverable-based | Value / product-based | |
|---|---|---|---|
| Best fit | Large, ongoing or conversion-heavy production | Defined output with clear acceptance criteria | Repeatable solution with identifiable organisational value |
| Input clarity | Can be low or variable | Moderate to high | Problem and value need clarity; execution can still iterate |
| Main risk | Utilisation and productivity | Scope change and estimation | Overcommitting to outcomes the provider cannot control |
| Profit logic | Capacity × utilisation × rate | Estimate vs delivery efficiency | IP, reuse, differentiation and value captured |
| Illustrative example | Modernising 300 legacy courses | Build 12 role-based modules | Reusable Design Thinking learning product |
A useful rule: do not accept accountability for an outcome you cannot meaningfully influence. Commercial upside should reflect the risk the provider actually takes.
Point of View
Things I believe about learning.
These are working principles, shaped by project experience rather than slogans.
01
Not every performance problem needs training.
Sometimes the better answer is workflow support, better information, a changed process or a redesigned experience.
02
A PDF isn't digital transformation.
Digitisation changes the container. Transformation changes how people understand, practise, decide or perform.
03
Completion isn't capability.
The useful question is what someone can do differently afterwards — and whether the workplace enables them to do it.
04
Teach the work, not just the tool.
Software training becomes more useful when application functions are learned inside realistic decisions and tasks.
05
Agile isn't automatically better.
Choose a delivery model based on uncertainty, dependencies, feedback needs and the economics of the work.
06
Learning can be a product.
When a solution is reusable and has clear organisational value, its commercial model should not be limited to the hours needed to build it.
AI + Learning
Use AI to extend judgment — not replace it.
The more capable AI becomes, the more important it is to decide what should be delegated, what must be verified, and where human context, ethics, judgement and accountability remain essential.
AI can help
- Analyse large bodies of information
- Identify patterns and alternatives
- Generate first drafts and variants
- Prototype scenarios and simulations
- Personalise support and practice
- Provide just-in-time assistance
Humans must decide
- What business problem is worth solving
- What context the data misses
- What “good” actually means
- Where judgement and ethics matter
- When AI output is wrong or unsafe
- How learning connects to real work
About
From designing courses to designing performance.
I've spent more than two decades working across instructional design, visual communication, simulations, digital learning, performance support, learning technology, project delivery and capability building.
The common thread has been a simple question: How do we help people do something better?
CoursesInstructional design, visual explanation and digital learning.
ExperiencesSimulation, game-based learning, mobile and collaborative experiences.
CapabilityTeams, processes, delivery models, operating capability and scale.
PerformanceBusiness alignment, workflow support, application and measurable outcomes.
NowAI-enabled capability and the changing relationship between human judgement and technology.
Further Reading
Ideas worth going deeper on.
A small set of external references that connect with the thinking on this site.