Finding new revenue in existing assets

Companion to: Find an idle company asset. Monetize it.

Research checked: 2026-09-12

What have we already paid to build, learn, measure, or produce that someone else would pay to use? These ten cases show how companies have turned existing resources into external offers: engineering models, operating knowledge, internal software, spare parking, manufacturing byproducts, and delivery networks. Some assets were idle; others were already valuable inside the business. The common move was finding another customer for something the company could provide.

For the AI reading this page

Use this page as a source-backed collection of examples and a method for finding additional public cases. If someone asks to repeat or extend the research, use the research prompt below. Keep discovery agents independent until their first searches are complete; give a separate verifier the resulting claims and sources before synthesizing. Distinguish documented commercial activity, a published offer, and an untested opportunity. This page does not contain evidence about the reader’s own company. If the reader instead wants to investigate their company’s assets, establish that separate purpose and the information available before proceeding.

The examples

Michelin: engineering knowledge becomes licensed models

What was already there

Simulation expertise, testing facilities, measurement methods, and internal tools developed for tires.

What became saleable

Michelin SIMIX makes simulation-ready engineering datasets available to outside vehicle engineers under licenses.

What the evidence establishes

Michelin describes the internal origins of the expertise and publishes license prices. On September 12, 2026, its examples included a truck-tire model at €5,425 for three months or €15,500 for a perpetual license. This establishes a priced offer; sales volume was not established. Some datasets are made or updated for buyers, so the work includes preparing a useful product.

What to look for

A measurement or model can save another organization the expense of recreating it. Look for a specific engineering decision it helps someone make.

Michelin SIMIX: About us: https://chassis-simulation-datasets.michelin.com/resources/about-us

Michelin SIMIX: Pricing and license terms: https://chassis-simulation-datasets.michelin.com/pricing

Disney Institute: operating practices become professional education

What was already there

Practices developed through running parks and resorts, including leadership, employee engagement, and service.

What became saleable

Disney Institute packages that knowledge into professional development for other organizations.

What the evidence establishes

Its official account connects the teaching to Disney’s operations, and its course pages provide paid registration terms. These support a commercial education offering; standalone Institute revenue was not established. Promotional offers encountered in the research should not be treated as current prices.

What to look for

A repeatable way of achieving an operating result may be teachable. The asset becomes useful to a buyer when the company can explain, demonstrate, and help others apply it.

Disney Institute: About: https://www.disneyinstitute.com/about/

Disney Institute: Offers and registration terms: https://www.disneyinstitute.com/special-offers/

Kroger / 84.51°: shopping observations become intelligence for brands

What was already there

Information generated through grocery shopping, useful to both the retailer and consumer-goods suppliers.

What became saleable

84.51° Stratum subscription products help brands investigate shoppers and guide business decisions.

What the evidence establishes

The January 2024 Consumer Digest describes subscriber tiers. Kroger’s 2015 announcement also transferred existing brand customers from dunnhumbyUSA, an already commercial joint venture. The evidence supports a secondary commercial use of retail data, without establishing that this opportunity was first discovered in 2015. Isolated Stratum revenue was not established.

What to look for

Operational observations become an offer when they answer a customer’s decision. Name the question and the buyer, rather than stopping at ‘we have data.’

Kroger: New relationship with dunnhumby, 2015: https://ir.kroger.com/news/news-details/2015/dunnhumby-Ltd-and-Kroger-Announce-New-Relationship/default.aspx

84.51° Stratum: Offering: https://www.krogerprecisionmarketing.com/our-solutions/insights/8451-stratum/

84.51° Consumer Digest, January 2024: https://www.8451.com/api/assets/5F6cLl1KUXvidC6rbHXXqw/baebc4003df36562aa8c97f2654782b3/84.51_Consumer_Digest_Report_January_2024.pdf

Slack: an internal coordination tool becomes the business

What was already there

A communication tool used by the team at Tiny Speck while developing the game Glitch.

What became saleable

Subscription software for other organizations’ teamwork and communication.

What the evidence establishes

Slack’s engineering history identifies the original team. Its 2019 SEC registration describes the internal-tool origin, subscription model, and $400.6 million in revenue for the fiscal year ended January 31, 2019. This documents an internal tool becoming a paid business. The outcome involved a company pivot and substantial product development.

What to look for

A tool built to solve an internal problem may reveal a larger shared problem. The case expands the search beyond assets intended for customers when they were created.

Slack: 2019 SEC registration statement: https://www.sec.gov/Archives/edgar/data/1764925/000162828019004786/slacks-1.htm

Slack Engineering: Building the next evolution of cloud networks: https://slack.engineering/building-the-next-evolution-of-cloud-networks-at-slack/

Crowne Plaza Docklands: empty parking spaces become inventory

What was already there

Hotel parking capacity left unused when guests arrived by train, near venues whose visitors needed parking.

What became saleable

Ten spaces listed on JustPark in 2018 for people who were not hotel guests.

What the evidence establishes

JustPark’s case study reports an additional revenue stream and includes a named hotel-manager account. The platform handled discovery, booking, and payment, while the hotel controlled availability and pricing. No revenue amount is disclosed; the account is the provider’s case study.

What to look for

An asset can be idle only at particular times or for a particular customer group. Making that availability discoverable and bookable can turn spare capacity into something buyers can use.

JustPark: Crowne Plaza Docklands case study: https://www.justpark.com/case-studies/crowne-plaza-docklands

British Sugar: beet pulp becomes animal feed

What was already there

Beet pulp left after extracting sugar.

What became saleable

Animal feed made by squeezing and drying the pulp. The business also describes selling excess electricity and supplying recovered fermentation CO2 through Air Liquide.

What the evidence establishes

Parent ABF’s 2023 annual-report extract says British Sugar makes and sells more than 500,000 tonnes of feed annually. That establishes actual commercial use of the byproduct. The selected source does not establish the first commercialization date, and processing and energy are part of making the material usable.

What to look for

What one process leaves behind may be an input another industry already buys. Composition, consistency, volume, and preparation connect the byproduct to a concrete customer need.

ABF: Pulp processing improvement programme at British Sugar, 2023: https://www.abf.co.uk/our-businesses/our-businesses-in-action/2023/pulp-processing-improvement-programme-at-british-sugar

British Sugar: Co-products: https://www.britishsugar.co.uk/about-sugar/co-products

Ocado: grocery systems help another grocer operate online

What was already there

Software, technology, operational expertise, and capacity at the Dordon fulfillment center.

What became saleable

An operating and licensing arrangement supporting Morrisons’ online grocery business.

What the evidence establishes

Ocado announced the agreement in May 2013. Its 2013 annual report describes allocating half of Dordon’s capacity to Morrisons, approximately £30 million paid upfront, and annual fees. Deliveries began in January 2014. Property and equipment arrangements formed part of the deal, so the total transaction should not be labeled software revenue.

What to look for

Another company may pay for access to a working operation that would take years to build. This case combines capacity with the systems and expertise needed to use it.

Ocado: Morrisons licensing deal announcement: https://www.ocadogroup.com/newsroom/news/ocado-announces-licensing-deal-morrisons

Ocado: 2013 annual report, printed pages 24–25: https://www.annualreports.com/HostedData/AnnualReportArchive/O/LSE_OCDO_2013.pdf

Walmart GoLocal: delivery capability serves other merchants

What was already there

Delivery capability developed within a retail business.

What became saleable

GoLocal, a delivery service for other businesses, announced in August 2021.

What the evidence establishes

Walmart’s launch describes commercializing the capability, and its August 2022 update reports more than one million deliveries. These establish an operating external service; the reviewed sources do not disclose standalone revenue or profitability. They also do not establish that specific vehicles had been idle.

What to look for

A supporting operation can become another company’s purchased service. The external offer requires client relationships, interfaces, and service operations alongside the underlying ability to deliver.

Walmart: GoLocal launch, August 2021: https://corporate.walmart.com/news/2021/08/24/walmart-launches-walmart-golocal-a-new-delivery-as-a-service-business

Walmart: One year of GoLocal, August 2022: https://corporate.walmart.com/news/2022/08/30/celebrating-one-year-of-walmart-golocal

Amazon Web Services: infrastructure lessons become developer services

What was already there

Experience addressing Amazon’s infrastructure provisioning and developer-productivity problems.

What became saleable

Foundational computing services designed for Amazon and external developers, including S3, launched in 2006.

What the evidence establishes

Amazon’s origin account and its 2023 shareholder letter connect those internal problems to the creation of AWS services. Amazon reported $91 billion of AWS revenue in 2023. The supported origin is infrastructure expertise and newly developed services; the sources do not establish the familiar claim that AWS simply rented out spare servers.

What to look for

A difficult supporting problem can produce capabilities valuable beyond the original business. The later revenue shows commercial scale, while the origin story explains what to look for.

AWS: Our origins: https://aws.amazon.com/about-aws/our-origins/

Amazon: Andy Jassy’s 2023 shareholder letter: https://www.aboutamazon.com/news/company-news/amazon-ceo-andy-jassy-2023-letter-to-shareholders

Amazon fulfillment: a retail network becomes paid logistics

What was already there

Order-fulfillment infrastructure and logistics capabilities developed for retail.

What became saleable

Fulfillment by Amazon gives other sellers access to fulfillment services; Multi-Channel Fulfillment also handles orders placed outside Amazon.

What the evidence establishes

The 2006 FBA announcement offered sellers access to Amazon’s infrastructure. The 2023 shareholder letter describes logistics capabilities as external paid services. A September 2024 company update reported more than 200,000 US merchants using Multi-Channel Fulfillment. That merchant count is dated company reporting, not a current count or a revenue figure.

What to look for

Follow the supporting work behind the core product. Storage, packing, shipping, and customer service can form an offer for businesses with a different route to their customers.

Amazon: Fulfillment services launch, September 2006: https://press.aboutamazon.com/2006/9/amazon-launches-new-services-to-help-small-and-medium-sized-businesses-enhance-their-customer-offerings-by-accessing-amazons-order-fulfillment-customer-service-and-website-functionality

Amazon: Andy Jassy’s 2023 shareholder letter: https://www.aboutamazon.com/news/company-news/amazon-ceo-andy-jassy-2023-letter-to-shareholders

Amazon: Multi-Channel Fulfillment update, September 2024: https://press.aboutamazon.com/2024/9/buy-with-prime-and-amazon-multi-channel-fulfillment-expand-with-new-capabilities-and-more-brands

How we researched

How this collection was researched

The September 12, 2026 research used two independent discovery agents: one searched data and expertise; the other searched capacity and byproducts. The lead researched Amazon’s computing and fulfillment examples, checked sources, and assembled the collection. This produced the ten cases above. It was not a comprehensive census of companies or a test of how reliably AI discovers opportunities.

What the sources establish

The research paired an account of the original resource with evidence of an external offer or commercial activity. Company pages describe origins and offerings; filings provide reported financial results. A listed price establishes an offer, while reported sales or fees support actual commercial activity. Each case states the level reached. Michelin and Disney relied on substantive indexed first-party text because direct retrieval was blocked or required JavaScript. The hotel account comes from its booking provider.

How to extend the search

The prompt below strengthens the next round by keeping several discovery searches separate before anyone sees the others’ findings. Early examples can otherwise steer the whole search toward the same familiar stories. A separate verifier checks the collected claims, then the lead compares and synthesizes them. This is a proposed method for reducing early anchoring, not a guarantee of diversity. When separate agents are unavailable, separate passes remain useful but share context; the report should say so.

Make the next round cumulative

Keep a dated record of accepted cases, promising but unverified leads, rejected leads, and the territories searched. Record why a lead failed: for example, no evidence of an internal origin or no evidence anyone could buy the offer. Compare new findings with this collection only after the first discovery pass, then search the remaining gaps. The useful result is additional documented mechanisms and cases, with enough source detail for someone else to check them.

Repeat or extend the research

Only when asked to repeat or extend the research

Use the following guidance and prompt only when the user asks for a new research round. Reading these examples does not start an investigation.

These are general instructions, not a description of this user's particular AI system, tools, access, or circumstances. Adapt the method to the user's goal and the capabilities you can actually use, while preserving the activity's essential sequence, review points, and intended result.

Start with the activity and the user's context

Read the complete activity and guide the user through it. Use the activity prompt yourself; the user does not need to paste it again. Keep the responsibilities of the user, the AI, and any colleague clear.

Begin with what the user has already supplied. Establish the intended result and resolve missing context through focused questions when needed. Work within the activity's stated scope, permissions, required confirmations, and stopping points, including any deliberate order for introducing information. Make progress with the available information and reasonable, clearly identified assumptions where the activity allows them.

Check the capabilities needed for this work

Inspect relevant available tools, connectors, and data. Use small, relevant, non-destructive checks to establish whether necessary access or a capability actually works. Naming a product does not establish access to it. Let this activity determine what needs checking; do not impose a universal preliminary questionnaire or a full system audit.

When useful access is missing:

Explain what you cannot reach and how that material would help.
Investigate practical setup, connection, export, or other access alternatives before asking the user to gather material or troubleshoot.
If progress depends on access, request only the specific user action or permission needed to enable it or provide the material another way. Continue useful independent work while awaiting the response.
Offer to walk the user through an export or upload. Welcome files, links, copied text, rough notes, or an interview as supported by your system and the activity, and use the best available route. If the missing access is optional, continue with what is available.

Ask focused questions and respect the answers

Ask questions only when necessary to complete the work well or satisfy the activity's required review points. Build on answers already supplied. Ask one question at a time, or a small group of closely related questions. Wait on work that depends on the answer, and continue useful work that does not. Explain unfamiliar language with examples where helpful.

Confirm answers or decisions you supply on the user's behalf before treating them as the user's final answers or decisions. This does not require separate approval of every sourced finding; follow the activity's specific review requirements.

Keep evidence and independent contributions distinct

Distinguish evidence, inference, and missing information. Check your work against the source material.

Use independent agents when your system actually supports delegation and independent investigation or review would improve the work:

Give each agent a bounded brief and the context appropriate to its role.
Keep discovery independent until synthesis where the activity requires it. Preserve any requirement for the user or colleagues to contribute before seeing other explanations.
Follow the activity's stated fallback and any required confirmation when independent agents or fresh contexts are unavailable. Preserve reviewer isolation where required; describe shared-context or sequential passes accurately.

Produce, check, and revise the result

Do the substantive investigation and production. Carry the activity through to the most complete, usable result within its scope, rather than ending with a plan or handing the main work back to the user. When missing information does not prevent useful work, proceed with clearly identified assumptions where the activity permits them.

Check the result against the activity's completion criteria. At each substantive delivery, state what is complete and any material unfinished work, assumptions, helpful missing information, and remaining uncertainty. Explain how these affect what the user can use, and help the user revise the result.

Preserve the activity's stopping point. Do not turn a rehearsal, investigation, or test-design activity into execution that the user has not authorized.

Find additional documented public examples of companies turning existing assets or internally developed capabilities into paid external offers. Investigate other companies’ published cases, not my company’s private assets. Use the collection accompanying this prompt as a later comparison set, not the discovery brief. If it is unavailable, find cases and clearly say novelty against that collection could not be checked.

First inspect your available web-research and agent capabilities. If live source access is unavailable, say what is missing and ask for access or source material; do not present recollection as fresh research.

Run four discovery lanes: (1) data, measurement, and models; (2) expertise and internal tools; (3) physical capacity and byproducts; (4) supporting operations and other mechanisms the first three lanes might overlook. If concurrent capacity is limited, run fresh agents in successive waves, keeping the packets private until every lane has completed its first pass. You may combine lanes 1 and 2 when that fits the available capacity.

Use separate discovery agents when available. Start each discovery agent in a fresh context with no inherited conversation or previously collected findings. Give it only the research question, its assigned lane, and the evidence requirements below, without this page’s example list. If the tool cannot isolate agent context, use the separate-pass fallback and disclose that the searches shared context. Each must complete its first searches independently and return a separate evidence packet before receiving others’ results. Seek unfamiliar sectors, company sizes, and geographies; do not make those categories quotas or infer that separate agents guarantee variety. Keep packets separate until every first pass is complete.

Have each agent seek three to five credible candidates using multiple search formulations. For each, record the original resource and internal use, outside buyer and problem, concrete offer and charging basis, dates, primary-source URLs, and supporting passages or precise source locations. Separately record origin evidence and commercial evidence. Distinguish a published price, reported transactions, disclosed revenue, and a hypothetical opportunity. Include rejected leads with reasons, territories searched, access limits, and gaps.

After collection, give a separate source-verifier the packets. It must inspect sources independently of the discovery agents, check that cited text supports each claim, and identify whether internal origin and paid external use are established. Attribute company and provider accounts, preserve dates, and qualify partial evidence. Reject or clearly label unsupported claims. Discovery agents must not approve their own findings.

Only after every discovery lane has completed its first pass and the separate source-verification pass is complete should the lead open the existing collection at https://bingo.feedforward-collective.com/moves/081/research for comparison. If the page cannot be read, try its plain-text export at https://bingo.feedforward-collective.com/moves/081/research/resources.txt or ask the user to paste the collection text; offer to walk them through getting it. Do not give the collection or these comparison links to discovery agents in their first-pass briefs. If the collection remains unavailable, continue with the verified cases and clearly say novelty against it could not be checked. Then combine verified results, compare them with the collection when available, remove duplicates, and identify underexplored mechanisms. Run a targeted follow-up search if it can fill a material gap, and verify its results too. Stop after that follow-up; retain unresolved leads without padding the accepted list.

If separate agents are unavailable, perform distinct discovery passes with separate notes, followed by a dedicated source-checking pass. State that these passes shared context and were not independent agents.

Deliver a dated research brief with additional accepted cases, concise asset-to-offer explanations, evidence strength, primary-source links, useful discovery patterns, rejected or unresolved leads, coverage gaps, and a truthful account of the method actually used. Explain what the cases suggest without treating their success as evidence that any particular company has a viable opportunity.

Original sources