How to build an innovation management course: a complete guide for lecturers

Central question: What counts as innovation, why does it matter, and how should innovation choices support an organisation's strategy?

Innovation Management starts by separating invention from innovation and novelty from value creation. Lecturers need students to see innovation as a managed process that can involve products, services, processes, positions, business models and organisational practices. The strategic question is not whether an idea is new, but whether it creates defensible value for customers, users, the organisation or society.

What this concept covers

• Innovation versus invention, improvement and entrepreneurship.
• Incremental, radical, architectural and disruptive change as analytical categories rather than labels of prestige.
• Product, service, process, position and business model innovation.
• Strategic intent, innovation ambition and the link between innovation choices and competitive advantage.
• Value creation, value capture and the difference between novelty and useful adoption.

Learning outcomes

By the end of this concept, students should be able to:

• Classify different forms of innovation using explicit criteria.
• Explain how an innovation can create and capture value.
• Connect an innovation ambition to strategic priorities and constraints.
• Distinguish a technology opportunity from an investable or implementable innovation proposition.

How to teach it

• Open with five familiar innovations and ask students to identify what actually changed for the user, the organisation and the revenue model.
• Use an innovation ambition matrix to distinguish core, adjacent and transformational work without presenting the labels as fixed truths.
• Require students to write a one-sentence innovation thesis: where to innovate, for whom, and why the organisation has a right to win.

Case-style example

Aster Appliances earns £240 million of annual revenue, but 72% comes from three mature product lines growing at 1% a year. Management can spend £8 million improving energy efficiency in its core range or £8 million developing a subscription-based home-energy service with uncertain demand. Students calculate the revenue lift needed for each option to recover the spend, then write an innovation thesis and defend whether the firm should prioritise lower-risk renewal or a new business model. They discover that “more novel” is not automatically “more strategic.”

Where students struggle

Students often rank ideas by how futuristic they sound. Push them to specify the customer problem, the mechanism of value creation, the capability advantage and the assumptions that make the initiative strategically coherent.

Reading and a quick check

Use Tidd and Bessant, Chapter 1, plus a recent corporate innovation example. Quick check: give students three changes and ask them to state what is innovative, for whom, and how value is captured.

Where it leads

This foundation lets students search for opportunities without treating every unmet need or emerging technology as strategically attractive.

Central question: How do managers identify innovation opportunities worth investigating before they commit resources?

Opportunity discovery should precede solution design. Students need to learn how to move from observations, complaints, trends and technologies to a clearly framed problem, a defined user and an evidence-backed opportunity. The goal is disciplined search, not brainstorming volume.

What this concept covers

• Need-pull and knowledge-push sources of innovation.
• Customer discovery, jobs-to-be-done style problem framing and observation.
• Lead users, underserved users and non-consumption.
• Market signals, trend scanning and problem-solution fit.
• Opportunity statements, evidence quality and assumptions that still need testing.

Learning outcomes

By the end of this concept, students should be able to:

• Frame an innovation opportunity around a specific user and problem.
• Separate observed evidence from inference and speculation.
• Prioritise discovery questions by uncertainty and decision value.
• Assess whether an opportunity is sufficiently important and reachable to justify experimentation.

How to teach it

• Give teams the same messy set of interview quotes, service complaints and usage data, then compare how they frame the problem.
• Ban solution language for the first 20 minutes of an opportunity workshop.
• Ask students to rank evidence by strength and identify which assumption would most change the opportunity if wrong.

Case-style example

NorthQuay Mobility serves 180,000 monthly rail commuters. Surveys show 38% miss at least one connection each month, but only 9% say they would pay for a premium travel-planning app. A proposed employer-paid version could reach 26,000 workers through six corporate clients. Students calculate the implied paying market under both routes, write two rival opportunity statements, and design the next three discovery questions. They discover that the largest visible problem is not necessarily the best commercial opportunity.

Where students struggle

Students often ask customers whether they “like” an idea instead of investigating behaviour, workarounds, frequency, stakes and willingness to change. Keep the emphasis on evidence about the problem before preference for a solution.

Reading and a quick check

Use Tidd and Bessant, Chapters 6 and 7. Quick check: ask students to rewrite a vague idea such as “AI for recruitment” into a user, problem, context and measurable outcome.

Where a simulation fits

The Startup Creation Simulation fits after students can frame a problem and define a customer. Its startup teams identify an opportunity, articulate problem-solution fit and begin market sizing before they build the rest of the venture case.

Where it leads

A credible opportunity statement gives students something testable. The next concept turns that uncertainty into experiments rather than premature plans.

Central question: How should teams learn quickly when customer needs, technical feasibility and commercial assumptions are uncertain?

Innovation teams rarely have enough information to justify a full-scale commitment at the start. This concept teaches experimentation as a management discipline: make assumptions explicit, design the cheapest credible test, define evidence thresholds in advance and update the decision after results arrive.

What this concept covers

• Design thinking as an iterative problem-framing and solution-learning process.
• Hypotheses, assumptions and testable claims.
• Prototypes, minimum viable tests and staged learning.
• Experiment validity, leading indicators and evidence thresholds.
• Learning velocity, failure as information and the danger of vanity metrics.

Learning outcomes

By the end of this concept, students should be able to:

• Translate an innovation idea into testable assumptions.
• Design an experiment that can change a management decision.
• Define success, failure and ambiguous evidence before testing.
• Recommend whether to persevere, pivot, pause or stop after new evidence.

How to teach it

• Run an assumption-mapping exercise with impact on one axis and uncertainty on the other.
• Require every prototype to name the decision it is intended to inform.
• Use pre-mortems so teams specify what result would cause them to abandon their preferred solution.

Case-style example

Luma Health proposes a digital triage tool for clinics. A full build would cost £1.2 million. A £28,000 concierge prototype can test whether nurses use the workflow, while a £9,000 landing-page test can test patient demand but not clinical adoption. Management has six weeks before its budgeting cycle. Students compare the information value of the two tests, define evidence thresholds, and recommend a learning sequence. They discover that the cheapest test is not always the test that resolves the most consequential uncertainty.

Where students struggle

Students often celebrate activity rather than learning. A prototype, survey or A/B test is only useful if students can say what belief it tests and what management action changes under each result.

Reading and a quick check

Use Tidd and Bessant, Chapters 6 and 9, alongside Bocken and Snihur (2020) on experimentation and business models. Quick check: require one hypothesis, one metric, one threshold and one decision.

Where a simulation fits

The Startup Creation Simulation supports this concept when lecturers require startup teams to justify how their value proposition, market assumptions and venture logic reflect evidence rather than enthusiasm. The platform activity should follow, not replace, explicit teaching on experiment design.

Where it leads

Experiments reduce uncertainty about desirability and feasibility. Students can then decide how the pieces fit into a viable business model.

Central question: How does an innovation create, deliver and capture value as a coherent system rather than as a standalone idea?

A strong Innovation Management course treats the business model as a system of linked choices. Students should be able to explain who receives value, what activities and partners make delivery possible, what the economics look like and where the organisation captures enough value to sustain the innovation.

What this concept covers

• Customer segments, value propositions, channels, relationships, key activities, resources and partners.
• Revenue logic, cost structure and unit economics.
• Business model fit, complementarities and internal consistency.
• Business model innovation versus product innovation.
• Platform, subscription, service and ecosystem models where appropriate.

Learning outcomes

By the end of this concept, students should be able to:

• Build a coherent business model around an innovation opportunity.
• Identify the assumptions that connect customer value to economic viability.
• Compare alternative value-capture mechanisms.
• Diagnose contradictions between channels, costs, partners, pricing and target users.

How to teach it

• Use a Business Model Canvas only after students have evidence about the customer problem.
• Make teams draw causal links between boxes rather than fill them independently.
• Ask one team to attack another team's model by identifying the first link likely to break at scale.

Case-style example

Cirrus Tools plans a connected equipment-monitoring service. Option A charges industrial customers £4,800 per site per year with expected gross margin of 64% but requires direct sales costing £3,200 per acquired site. Option B licenses through distributors for £2,600 per site with 82% gross margin and lower acquisition cost, but loses customer data. Students calculate first-year contribution for 100 sites, map both models, and recommend which logic to test. They discover that the economically cleaner model can weaken learning and strategic control.

Where students struggle

Students often treat the canvas as a completion exercise. Push them to test consistency: if the value proposition changes, what else in the model must change, and which assumption becomes most exposed?

Reading and a quick check

Use Tidd and Bessant, Chapter 13, and Andreini et al. (2022). Quick check: ask students to identify one reinforcing link and one contradiction in a model.

Where a simulation fits

The Startup Creation Simulation fits directly here. Startup teams develop a Business Model Canvas, size TAM, SAM and SOM, prepare a pitch and negotiate with student VC teams, so the lecturer can assess whether the venture logic is internally coherent before capital is discussed.

Where it leads

Once students can articulate a model, they need to judge whether it fits the organisation and competitive environment rather than evaluating it in isolation.

Central question: Is the innovation attractive given the organisation's capabilities, external environment and strategic risks?

Innovation choices sit inside existing capabilities, competitors, regulation, timing and resource constraints. This concept teaches strategic diagnosis as a way to challenge an innovation thesis, not as a decorative framework exercise. The output should be a decision, not four boxes.

What this concept covers

• Internal strengths and weaknesses relevant to the specific innovation.
• External opportunities and threats, including market timing and regulatory uncertainty.
• Strategic fit, capability gaps and path dependence.
• Assumption-based planning and decision-making under uncertainty.
• Using SWOT selectively and connecting diagnosis to actions or TOWS-style choices.

Learning outcomes

By the end of this concept, students should be able to:

• Distinguish internal conditions from external conditions.
• Prioritise factors by materiality rather than list length.
• Connect strategic diagnosis to a go, adapt, partner, delay or stop decision.
• Identify which capability gaps can be built, bought or accessed through partners.

How to teach it

• Give all teams the same evidence pack but assign different strategic priorities, then compare their factor weighting.
• Require a maximum of three material items in each SWOT category.
• Score recommendations on evidence and consequence, not on completeness of the matrix.

Case-style example

PineWorks Packaging has a patented fibre coating that cuts plastic use by 60%. It has £6 million of cash, no international sales team and one pilot customer representing 18% of current revenue. EU regulation may accelerate demand, while two global rivals can scale faster. Students score eight strategic factors from 1 to 5 for impact, identify the three that should drive the decision, then recommend launch, partner or delay. They discover that the same evidence can support different strategies when capability and timing assumptions differ.

Where students struggle

Students often confuse “positive” with “strength” and “negative” with “threat.” Re-anchor them on locus: strengths and weaknesses are internal; opportunities and threats are external. Then force prioritisation.

Reading and a quick check

Use Tidd and Bessant, Chapters 4 and 9. Quick check: provide six factors and ask students to classify, weight and connect each to a management implication.

Where a simulation fits

The SWOT Analysis Simulation fits after this teaching. Students work in Business Development and Risk Management roles, analyse the same international expansion evidence, score material factors, explain their reasoning and reconcile different interpretations into a recommendation.

Where it leads

Strategic fit exposes capability gaps that may be solved internally or through external partners. That creates the bridge to open innovation and ecosystems.

Central question: What should the organisation build itself, source externally, co-develop, license or protect?

Innovation increasingly crosses organisational boundaries. Students need to understand how firms access external knowledge and complementary assets without treating collaboration as costless. Partnership design, ecosystem position, absorptive capacity and intellectual property determine how much value an organisation can create and retain.

What this concept covers

• Open innovation and inbound, outbound and coupled knowledge flows.
• Innovation ecosystems, complementors, platforms and bottlenecks.
• Alliances, suppliers, universities, startups and customers as innovation partners.
• Absorptive capacity, knowledge transfer and not-invented-here behaviour.
• Patents, trade secrets, licensing and appropriability as strategic choices.

Learning outcomes

By the end of this concept, students should be able to:

• Map the critical actors and dependencies in an innovation ecosystem.
• Recommend which activities to own and which to access externally.
• Evaluate collaboration benefits against coordination, leakage and dependency risks.
• Explain how intellectual property choices affect value capture and partner incentives.

How to teach it

• Build an ecosystem map around a product whose value depends on infrastructure or complements.
• Run a make, buy, partner or license decision with incomplete capability data.
• Use a negotiation brief where each party controls a different complementary asset.

Case-style example

HelioGrid has developed a battery-control algorithm but lacks certified hardware and utility relationships. Building both internally would cost £14 million and take 30 months. Partnering with VoltEdge would cost £2 million plus 18% of licence revenue and could launch in 11 months, but VoltEdge wants access to performance data. Students compare time-to-market, cash exposure and knowledge-control trade-offs, then propose partnership and IP boundaries. They discover that fastest access to complementary assets can create the deepest future dependency.

Where students struggle

Students tend to frame open innovation as “more collaboration is better.” Ask who owns the critical bottleneck, who learns from whom, how incentives evolve and what happens if the partner becomes a competitor.

Reading and a quick check

Use Tidd and Bessant, Chapters 8, 11 and 13, plus Baldwin et al. (2024). Quick check: ask students to name one dependency the focal firm should accept and one it should reduce.

Where it leads

External opportunities add more candidates to the innovation pipeline. Students now need a portfolio logic for selecting, balancing and terminating initiatives.

Central question: How should managers allocate attention across multiple innovation bets when projects differ in risk, horizon and strategic purpose?

Single-project evaluation is not enough. Organisations manage portfolios containing incremental improvements, adjacent opportunities and higher-uncertainty bets. Portfolio management requires explicit criteria, diversification across horizons, kill and scale rules, and metrics that match a project's maturity.

What this concept covers

• Portfolio balance across horizons, strategic themes and uncertainty profiles.
• Selection criteria, stage reviews and kill, hold, pivot or scale decisions.
• Innovation accounting and evidence appropriate to different maturity stages.
• Avoiding metric substitution, sunk-cost escalation and political portfolio management.
• Strategic options, learning value and dependencies between projects.

Learning outcomes

By the end of this concept, students should be able to:

• Design selection criteria suited to an innovation portfolio.
• Compare projects without pretending uncertain forecasts are equally reliable.
• Choose stage-appropriate metrics and evidence thresholds.
• Recommend rebalancing when strategy, evidence or resource constraints change.

How to teach it

• Give students six projects with different maturity, NPV ranges and strategic rationale.
• Ban a single weighted score from being the only decision rule.
• Ask teams to defend one project they stop and one counterintuitive project they protect.

Case-style example

Orion Services has £12 million for innovation. Project Alpha needs £5 million and could save £4 million annually with 80% technical confidence. Beta needs £4 million for a new platform with a 35% chance of generating £20 million annual revenue. Gamma needs £3 million to meet a regulatory change and protects £30 million of existing revenue. Students design criteria, allocate the budget and define one leading metric for each funded project. They discover that a portfolio cannot be optimised on one return number because strategic protection, option value and uncertainty differ.

Where students struggle

Students often use the same ROI hurdle for every project. Innovation portfolios need comparability without false precision, especially when early initiatives are buying information rather than near-term cash flow.

Reading and a quick check

Use Tidd and Bessant, Chapters 9 and 15, plus Röth, Spieth and Dzomba (2025). Quick check: ask which metric should disappear when a project moves from discovery to scaling.

Where it leads

Portfolio logic defines which initiatives deserve resources. The next concept adds disciplined financial analysis without allowing NPV to erase strategic uncertainty.

Central question: How should managers combine financial discipline with strategic and learning value when funding innovation?

Innovation spending still competes for scarce capital. Students should learn NPV, IRR, payback and profitability index where relevant, but also recognise that early-stage innovation often contains option value, staged commitments and evidence gaps. Finance should discipline the decision, not create spurious certainty.

What this concept covers

• Cash-flow estimation, discounting and risk-adjusted assumptions.
• NPV, IRR, payback and profitability index as decision aids.
• Capital rationing and portfolio allocation.
• Staged investment, real-option logic and learning milestones.
• Scenario analysis and the treatment of strategic benefits that are difficult to monetise.

Learning outcomes

By the end of this concept, students should be able to:

• Calculate and interpret core capital-budgeting measures.
• Allocate a fixed innovation budget across competing projects.
• Challenge cash-flow assumptions and identify non-financial strategic value.
• Design staged commitments that preserve learning before full-scale investment.

How to teach it

• Teach the measures quickly, then spend more time on cash-flow assumptions and the decision rule.
• Give one project a high NPV but weak strategic fit and another a lower NPV with strong option value.
• Require teams to state what evidence would unlock the next tranche of capital.

Case-style example

Vela Foods can fund only £10 million this year. A £6 million automation project has expected NPV of £3.1 million and low uncertainty. A £4 million direct-to-consumer pilot has expected NPV of £1.4 million but could create a new data asset and channel. A £5 million sustainable-packaging project has NPV of £2.0 million and protects a major customer contract. Students calculate feasible portfolios, compare total NPV and strategic effects, then design staged funding. They discover why the mathematically highest-return combination may not be the strongest innovation portfolio.

Where students struggle

Students either over-trust the model or reject finance as irrelevant to innovation. The useful position is between them: quantify what can be quantified, expose uncertainty and make the residual judgement visible.

Reading and a quick check

Use a capital-budgeting refresher alongside Tidd and Bessant, Chapters 9 and 13. Quick check: ask students to identify the single cash-flow assumption most likely to reverse a funding decision.

Where a simulation fits

The Capital Budgeting Simulation fits directly after this concept. Students first evaluate three projects using NPV, IRR, profitability index and payback, then allocate a fixed $10 million budget across eight independent projects. Use the debrief to separate calculation quality from the managerial judgement behind the portfolio.

Where it leads

Once initiatives are funded, the management problem shifts from selection to execution: how to structure a process that is disciplined without killing learning.

Central question: How should an organisation structure development when projects differ in uncertainty, speed and coordination needs?

There is no universally best innovation process. Stage-Gate, agile, lean experimentation and hybrid approaches solve different coordination problems. Students should learn to choose and tailor governance, cadence and decision rights to the type of uncertainty rather than repeat method slogans.

What this concept covers

• Innovation as a process from search and selection through implementation and learning.
• Stage-Gate logic, gate criteria and governance.
• Agile principles, iterative delivery and feedback loops.
• Hybrid processes, hardware versus software constraints and regulated environments.
• Cross-functional teams, decision rights, handoffs and escalation.

Learning outcomes

By the end of this concept, students should be able to:

• Compare Stage-Gate, agile and hybrid process designs.
• Match governance to technical, market and regulatory uncertainty.
• Design decision points that generate useful evidence without excessive bureaucracy.
• Diagnose process failure caused by handoffs, incentives or unclear ownership.

How to teach it

• Give the same innovation to teams and assign different contexts such as medical device, SaaS and industrial plant.
• Ask each team to choose cadence, gate criteria and decision owners.
• Use a process post-mortem where students identify where learning slowed or accountability disappeared.

Case-style example

Nexa Medical is developing a diagnostic device and companion app. The app can release every two weeks, but the regulated device requires a nine-month validation cycle. A single Stage-Gate process delays software learning; a fully agile process risks compliance gaps. Students design a hybrid process with four formal investment gates, two-week software sprints and defined evidence required at each gate. They discover that process choice should follow uncertainty and coupling, not ideology.

Where students struggle

Students can mistake agile for “no planning” and Stage-Gate for “bureaucracy.” The teaching task is to expose the control problem each method solves and the costs it creates.

Reading and a quick check

Use Tidd and Bessant, Chapters 3 and 10. Quick check: ask students to name one decision that belongs at a formal gate and one that should be delegated to the team.

Where it leads

A workable development process creates something that must be adopted. The next concept shifts from building the innovation to winning a market and changing behaviour.

Central question: How does an organisation move from a promising innovation to adoption, market traction and repeatable growth?

Commercialisation is where many technically successful innovations fail. Students need to connect segmentation, target selection, positioning, channels, adoption barriers, pricing and launch sequencing to the evidence created earlier in the course.

What this concept covers

• Segmentation, targeting and positioning for innovation.
• Adoption and diffusion, including switching costs and perceived risk.
• Beachhead markets, launch sequencing and channel selection.
• Value communication, differentiation and positioning claims.
• Commercial metrics: acquisition, conversion, retention, contribution and learning.

Learning outcomes

By the end of this concept, students should be able to:

• Select a defensible target segment for an innovation.
• Build a positioning statement grounded in differentiated value.
• Identify adoption barriers and design a market-entry sequence.
• Connect commercial choices to measurable launch assumptions.

How to teach it

• Force teams to choose one target before they design a message.
• Run a positioning comparison where students must show what is different, relevant and provable.
• Ask what a failed launch metric actually diagnoses: awareness, relevance, access, trial or retention.

Case-style example

PeakFuel has £1.5 million to launch a high-protein snack. Student athletes offer a reachable market of 420,000 consumers with strong product fit but low willingness to pay. Busy professionals represent 1.8 million consumers, accept a 22% price premium, but face crowded competition. Students estimate first-year revenue under provided conversion assumptions, choose target regions and positioning, and recommend a launch sequence. They discover that the largest segment is not automatically the best beachhead and that positioning must follow target choice.

Where students struggle

Students often write generic messages before choosing a customer. Keep the sequence explicit: segment, target, position, then channels, offer and metrics.

Reading and a quick check

Use Tidd and Bessant, Chapter 10. Quick check: ask students to complete “For [target], our innovation is [frame] that [benefit] because [reason to believe].”

Where a simulation fits

The Go To Market Simulation fits directly here. Each student acts as the founder of a protein-bar company, segments the US market, selects target customers and regions, builds a differentiated position and records the reasoning behind those choices in a 1.5 to 2 hour competitive exercise.

Where it leads

Commercial traction creates pressure to scale. Students now need to examine whether the organisation, culture and operating system can support innovation repeatedly.

Central question: How should leaders organise people, incentives and structures so the firm can exploit today's business while exploring tomorrow's opportunities?

Innovation Management is an organisational design problem as much as an idea problem. The same routines that make core operations efficient can suppress experimentation. Students should analyse structure, leadership, incentives, psychological safety, boundary spanning and ambidexterity without reducing culture to slogans.

What this concept covers

• Exploration versus exploitation and organisational ambidexterity.
• Structural separation, contextual ambidexterity and integration mechanisms.
• Leadership roles, incentives, resource ownership and executive sponsorship.
• Cross-functional teams, boundary spanning and knowledge transfer.
• Innovation climate, learning from failure and avoiding innovation theatre.

Learning outcomes

By the end of this concept, students should be able to:

• Diagnose organisational barriers to innovation.
• Choose an appropriate structural approach for exploration and exploitation.
• Design incentives and governance that reduce conflict between core and new ventures.
• Recommend how leaders should integrate learning without prematurely absorbing an emerging initiative.

How to teach it

• Give students an organisation chart and ask where a disruptive initiative should sit for its first 18 months.
• Use conflicting KPIs for the core business and venture team to surface predictable tension.
• Run a leadership memo exercise: what should be protected, integrated and measured differently?

Case-style example

Meridian Insurance launches an AI claims venture expected to lose £3 million in year one while reducing claim-handling time by 45%. The core operations director is measured on cost-to-income and wants the venture folded into the existing IT roadmap. The venture leader argues for 18 months of separate governance and a £5 million ring-fenced budget. Students design reporting lines, metrics and integration triggers, then defend centralisation or separation. They discover that organisational conflict can be a rational consequence of incompatible metrics rather than resistance to innovation.

Where students struggle

Students often prescribe “innovative culture” without specifying decision rights, incentives or resource protection. Require a structural mechanism for every cultural recommendation.

Reading and a quick check

Use Tidd and Bessant, Chapter 5, and the Moog case for incentive and organisational tensions. Quick check: ask what the core business should be allowed to veto, and what it should not.

Where it leads

Organisation determines whether innovation can survive internally. The final concept asks how innovation scales responsibly in a digital and AI-intensive environment.

Central question: How should managers scale innovation while governing digital, social, ethical and strategic consequences?

The final concept integrates growth, digital technologies, AI and responsible innovation. Students should leave able to distinguish rapid technical scaling from sustainable organisational scaling, and to recognise that governance is part of innovation design rather than a compliance step at the end.

What this concept covers

• Digital innovation and how data, software and platforms change innovation economics.
• AI-enabled innovation, automation and generative tools in discovery and delivery.
• Scaling bottlenecks in operations, talent, infrastructure, data and customer support.
• Responsible innovation: anticipation, inclusion, reflection, responsiveness and stakeholder effects.
• Learning systems, innovation audits and capability building.

Learning outcomes

By the end of this concept, students should be able to:

• Evaluate whether an innovation is ready to scale.
• Identify digital and AI dependencies that change risk or value capture.
• Integrate stakeholder, ethical and regulatory concerns into design choices.
• Recommend scaling milestones, governance and learning metrics.

How to teach it

• Use a scale-readiness review across demand, economics, operations, technology, governance and talent.
• Ask students to identify a harmful second-order effect that does not appear in the base-case business model.
• Close with an integrated capstone where students defend scale, slow, redesign or stop.

Case-style example

CivicLens sells an AI service that triages local-government applications. Pilots cut processing time from 18 days to 7 and cost £420,000, while a national rollout would require £6.8 million. Accuracy is 94% overall but falls to 86% for applications submitted in two minority languages. Students quantify the service-level gain, identify governance risks, set a minimum performance threshold and recommend scale, redesign or phased rollout. They discover that an innovation can be operationally attractive and still be unready to scale responsibly.

Where students struggle

Students can treat responsibility as an ethics paragraph after the financial case. Make it decision-relevant by tying harms, trust, regulation and stakeholder adoption to scale gates and resource allocation.

Reading and a quick check

Use Tidd and Bessant, Chapters 2, 14 and 15, plus Zhang et al. (2023) and Felicetti et al. (2024). Quick check: ask students for one scale metric, one risk metric and one condition that would stop expansion.

Where it leads

This closes the lifecycle. Students can now move from strategic intent and opportunity discovery through experimentation, funding, execution, commercialisation, organisation and responsible scaling.

10-week course structure

A 10-week version should compress adjacent ideas rather than remove the lifecycle. Combine strategy with opportunity discovery, integrate experimentation with business models, and treat organisation, digital and responsible scaling as one capstone block.

14-week course structure

A 14-week version creates space for deeper discovery, a fuller Startup Creation debrief, separate portfolio and capital-allocation sessions, and a dedicated final integration session rather than simply adding more frameworks.

The Startup Creation Simulation places students into a two-sided venture-creation and funding process. Startup teams identify an opportunity, define problem-solution fit, estimate TAM, SAM and SOM, build a Business Model Canvas, prepare a live pitch and negotiate investment terms with student VC teams. The live product page specifies a 6 to 10 hour team-based format with roughly 70% of participants in startup teams and 30% in VC teams.

Why it fits here

By the end of Session 4, students should already be able to frame a problem, state their most important assumptions and explain how a business model creates and captures value. The simulation then turns those concepts into a venture decision with an external counterparty rather than a completed worksheet.

What students do

• Startup teams identify an opportunity and customer problem, articulate a value proposition and estimate TAM, SAM and SOM.
• Startup teams build a Business Model Canvas and convert the venture logic into a live pitch.
• VC teams create an investment thesis and screening criteria before hearing pitches, so they have a decision rule rather than reacting to presentation style.
• Founders and investors meet live, negotiate investment amount and equity, and reach a post-money valuation where a deal is agreed.
• Because VC capital is limited, not every startup is necessarily funded. That creates a useful debrief on evidence, persuasion, fit and trade-offs.

Recommended course placement

• Begin the analysis work after Session 3 and complete the business-model and pitch stages after Session 4.
• Run as a 6 to 10 hour applied block, or split the stages across several classes and supervised independent preparation.
• Use the reflection immediately before a venture memo or innovation opportunity assessment so students convert experience into evidence.

Knowledge students should have before the simulation

• Problem and opportunity framing.
• Customer segment and value proposition logic.
• Basic TAM, SAM and SOM.
• Business Model Canvas.
• Basic venture funding and dilution language if the lecturer wants a deeper funding debrief.

Delivery format

Suggested debrief questions

• Which assumption in your venture was least supported when you began pitching?
• Did investor screening criteria match the ventures that ultimately attracted capital?
• Which part of the Business Model Canvas changed because of questioning or negotiation?
• How did headline valuation interact with ownership, use of funds and the quality of the underlying opportunity?
• What new evidence would you collect before committing the next tranche of resources?

The Go To Market Simulation is a 1.5 to 2 hour individual, competitive market-entry exercise built around segmentation, targeting and positioning. Each student acts as the founder of a protein-bar company entering the US market, selects customer segments and regions, develops a differentiated market position and records the reasoning behind key decisions.

Why it fits here

Students should enter the simulation already knowing STP and adoption logic. That allows the lecturer to use the activity as a test of coherence: does the chosen target support the position, do the regional choices make sense, and can the student explain why alternatives were rejected?

What students do

• Review a common founder briefing and market information before making the first decision.
• Segment the market, choose priority target customers and decide which regions to enter.
• Build a differentiated positioning choice rather than trying to appeal to every customer.
• Record written reasons for major decisions, creating evidence for the debrief.
• Compare outcomes in a competitive setting and reflect on the relationship between STP choices and commercial performance.

Recommended course placement

• At the end of Session 10 after segmentation, targeting, positioning and diffusion teaching.
• Inside a two-hour class if pre-reading is completed in advance, with a short debrief at the end.
• Before a written commercialisation memo so the student can distinguish simulation outcome from the quality of the reasoning.

Knowledge students should have before the simulation

• Basic segmentation criteria.
• Target-market attractiveness and fit.
• Positioning and differentiation.
• Introductory launch and adoption metrics.

Delivery format

Suggested debrief questions

• Which target choice drove the rest of your commercial strategy?
• Where did your positioning become generic or internally inconsistent?
• What information would have changed your target-market decision?
• Did the strongest outcome come from a better decision or from assumptions that happened to fit the scenario?
• How would you change launch sequencing if the budget were cut by 30%?

Grading with the Startup Creation Simulation

Treat Startup Creation as evidence of venture-creation judgement, not as a pitch contest. Startup teams and VC teams generate different but complementary evidence, and the negotiated outcome should be interpreted alongside the reasoning that produced it.

• Quality of problem and opportunity framing.
• Coherence of value proposition, market sizing and Business Model Canvas.
• Evidence used to justify venture or investment decisions.
• Quality of pitch questions, screening logic and negotiation reasoning.
• Post-simulation individual reflection on one assumption that changed and one that did not.

Grading with the Go To Market Simulation

Go To Market is individual and competitive, which makes it useful for attributable evidence. The academic mark should still reward the reasoning behind STP choices rather than simply the scenario score.

• Quality and evidence behind segmentation and target selection.
• Coherence between chosen target, regions and positioning.
• Ability to explain rejected alternatives.
• Written rationale and use of commercial evidence.
• Post-simulation diagnosis of why the outcome occurred and what would be changed.

Grading with the SWOT Analysis Simulation

Use SWOT Analysis to assess prioritisation and strategic reasoning rather than the ability to fill four boxes. The role split between Business Development and Risk Management makes disagreement part of the evidence.

• Correct distinction between internal and external factors.
• Materiality and weighting of the factors selected.
• Quality of written reasoning and evidence.
• Ability to reconcile conflicting role perspectives into a recommendation.
• Individual note on which factor most changed the final decision.

Grading with the Capital Budgeting Simulation

Capital Budgeting provides individual evidence on calculation and allocation. A high score can inform the lecturer, but it should not become the academic grade without considering assumptions and decision quality.

• Accuracy of NPV, IRR, profitability-index and payback interpretation.
• Recognition of project risk and cash-flow uncertainty.
• Quality of the $10 million portfolio allocation.
• Ability to explain why a mathematically attractive project might still be rejected or staged.
• Written or oral defence of the final portfolio and its trade-offs.

Platform evidence and academic judgement: The platform records what each team decided, the terms they agreed and comparative outcomes across groups. That evidence supports your academic judgement; it does not replace it, and it does not establish which individual student made which argument. For individual simulations such as Go To Market and Capital Budgeting, decision records can be attributable to a student, but the academic mark should still come from your published criteria and moderation process rather than the platform score alone.