# RFP 09: AI Commercial Positioning Tender type: Request for Proposals Issued by: Cardano Product Committee / Intersect Research portfolio: Product Research Initiatives Final publication date, submission deadline, clarification window, expected project period, submission method, and contact: will be confirmed on the Product Research Initiatives - Grants page before the call opens. *** ## 1. Executive Summary ### Strategic question Can AI become a credible Cardano adoption vertical with identifiable buyers, delivery partners, revenue pathways, and measurable Cardano-side activity; and if so, which AI use cases, audience claims, and next actions should Cardano stakeholders prioritize? ### Evidence gap AI is an active commercial theme across technology and blockchain markets. Agent payments, agentic commerce, AI auditability, provenance, privacy, compliance, and autonomous service monetization are all emerging as areas where infrastructure choices may matter. Cardano also has AI-related ecosystem activity and plausible technical fit in areas such as deterministic transaction processing, formal methods, auditability, low-cost settlement, and privacy-adjacent infrastructure. However, Cardano stakeholders do not yet have a decision-ready assessment of whether those properties translate into a defensible commercial position against specialised AI blockchains, agent-payment infrastructure, general-purpose L1/L2 ecosystems, or non-chain alternatives. The evidence gap is not whether Cardano can tell an AI story. The gap is whether AI can become a real Cardano adoption vertical with buyer pull, credible use cases, reachable delivery pathways, revenue logic, transaction pathways, and evidence thresholds for future investment. ### Decision unlocked This research should enable Cardano stakeholders to decide: * whether AI should be treated as a strategic vertical, conditional opportunity, monitor-only area, or no-go area; * which AI use cases are commercially credible for Cardano; * where Cardano has buyer-relevant differentiation and where it does not; * which audiences require which proof points before taking Cardano's AI position seriously; * which partner, delivery, pilot, product, or ecosystem actions would be justified; * what Cardano stakeholders should stop saying, avoid funding, or not overclaim. ### Expected outputs The selected vendor will produce an executive decision memo, AI commercial positioning assessment, AI use-case prioritization matrix, buyer and audience credibility map, use-case-specific competitive benchmark, current activity evidence review, AI adoption pathway model, narrative or claim stress-test findings where proposed, research-to-action pathway, cross-RFP handoff memo, final research report, final presentation, and public summary. Vendors may combine deliverables where sensible, provided that every decision gate is answered and every required output is covered. ### Requirement priority The required core is to answer the decision gates with traceable evidence and produce the required decision outputs. Optional methods, templates, or stretch work should be separated from the core scope and budget. This RFP is not AI product development, pilot execution, protocol implementation, technical roadmap authorship, grant advocacy, or marketing campaign work. *** ## 2. Strategic Context The Cardano Product Committee has received funding to commission a portfolio of product research initiatives aligned with Cardano's long-term product direction and Strategy 2030 priorities. The purpose of the portfolio is to define the evidence Cardano needs before making product, adoption, funding, partnership, and strategic decisions. This RFP focuses on AI commercial positioning. Its purpose is to determine whether AI should become a Cardano strategic vertical and, if so, under what conditions. The external market is moving quickly. AI agents are creating demand for new approaches to payments, identity, access, accountability, transaction authorization, service monetization, and machine-to-machine interaction. Protocols and standards such as x402, Agentic Commerce Protocol, Machine Payments Protocol, and other applicant-justified developments may indicate market movement, but they do not by themselves prove that Cardano has a right to win. Cardano's potential relevance must therefore be tested, not assumed. Technical properties such as auditability, deterministic processing, formal methods, predictable settlement, and privacy-adjacent infrastructure may matter in some AI contexts and not in others. The research should identify where those properties translate into buyer-relevant advantage, where they are merely interesting, and where other ecosystems or non-chain infrastructure are better positioned. The research should inform the Cardano Product Committee and the broader Cardano ecosystem, including AI builders, business development teams, grant allocators, product contributors, delivery partners, enterprise-facing teams, communications teams, and future research vendors. *** ## 3. Research Problem Statement Cardano lacks a decision-ready AI commercial positioning assessment. Current AI discussion can collapse several different questions into one broad narrative: * whether AI is commercially important; * whether blockchain infrastructure matters to AI; * whether Cardano has relevant technical properties; * whether current Cardano-side activity is adoption signal; * whether enterprises or developers would choose Cardano for AI-related reasons; * whether AI use cases create Cardano-side value; * whether Cardano should invest, partner, position, monitor, or avoid. These questions must be separated. A real AI opportunity for Cardano must connect a buyer or adopter problem to a use case, a technical requirement, a delivery route, a revenue or transaction pathway, and a measurable adoption signal. Without this evidence, Cardano risks: * promoting generic AI narratives without market validation; * treating technical possibility as buyer demand; * using named projects as proof before adoption evidence is validated; * overestimating privacy, auditability, or formal-methods claims without buyer testing; * ignoring specialised AI chains, agent-payment infrastructure, or non-chain alternatives; * investing in AI opportunities that produce little Cardano-side value; * missing a real adoption pathway if one exists. The selected vendor must close this evidence gap by producing a neutral, source-traceable assessment of AI demand, Cardano fit, competitive position, current activity, buyer credibility, adoption pathway, and strategic posture. *** ## 4. Definitions For this RFP: **AI commercial positioning** means the evidence-based assessment of where Cardano can credibly position itself in AI-related markets, which claims are buyer-relevant, which use cases are defensible, and which strategic actions are justified. **AI adoption vertical** means a repeatable area of AI-related adoption where Cardano infrastructure may support buyers, builders, operators, partners, or users in a way that produces measurable Cardano-side value. **Buyer-relevant technical fit** means a Cardano property or capability that maps to an actual buyer, builder, operator, or partner requirement. Technical fit is not sufficient unless linked to a decision criterion or deployment condition. **Agent payments** means payment flows in which AI agents, autonomous services, programmatic clients, or machine actors pay for or monetize resources, services, API calls, data, compute, tasks, or other digital outputs. **Agentic commerce** means commerce workflows where AI agents help discover, evaluate, initiate, or complete transactions between buyers and businesses. **Adoption signal** means evidence of recurring paid usage, production deployment, identifiable buyers or users, repeated agent-service transactions, revenue relevance, Cardano-side transaction activity, partner commitments with delivery roles, or operational dependency on Cardano infrastructure. **Cosmetic signal** means activity that appears relevant but does not demonstrate adoption, such as vague announcements, demos without users, unsourced claims, social attention, internal enthusiasm, or technical possibility without buyer need. **Specialised AI chain or AI-focused infrastructure** means a blockchain, protocol, network, payment standard, marketplace, service layer, or other infrastructure positioned specifically around AI, autonomous agents, machine payments, inference, data provenance, identity, or related AI workflows. **Cardano-side value** means value that accrues to Cardano infrastructure or ecosystem activity, such as transaction activity, retained users, recurring payments, partner commitments, developer adoption, ecosystem revenue relevance, protocol usage, or strategic infrastructure dependency. The selected vendor may refine these definitions during the research design phase, but revised definitions must remain measurable and decision-useful. *** ## 5. Objectives The selected vendor will be expected to: 1. Assess whether AI-related blockchain demand is commercially meaningful and where it is moving. 2. Validate whether Cardano's technical properties translate into buyer-relevant AI advantage. 3. Review Cardano-side AI activity neutrally, with source traceability and clear evidence limitations. 4. Screen and prioritize AI use cases based on buyer pull, Cardano fit, competitive position, reachability, and Cardano-side value. 5. Benchmark Cardano against relevant specialised AI chains, agent-payment infrastructure, general-purpose blockchain ecosystems, and non-chain alternatives. 6. Map buyer, builder, partner, and audience credibility requirements for Cardano's AI positioning. 7. Assess delivery pathways, partner routes, revenue pathways, transaction pathways, and adoption signals for priority use cases. 8. Test AI narratives or claims by audience where the applicant proposes a credible method. 9. Produce a go/no-go, conditional-go, monitor, or no-go recommendation for AI as a Cardano strategic vertical. 10. Identify findings that should be handed to adjacent Product Research Initiative RFPs or ecosystem workstreams. *** ## 6. Decision Gates Applicants must design their methodology around the following decision gates. A proposal that does not map methods and deliverables to these gates will be considered weak fit for this RFP. | Decision Gate | Evidence Required | YES Means | NO Means | Action Unlocked | | ------------------------------------------------------------------------------------------- | ---------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------- | --------------------------------------------------------------------------------------- | -------------------------------------------------------------------------------------------- | ------------------------------------------------------------------------------------- | | Is there a real AI-commercial opportunity where blockchain infrastructure matters? | External demand scan across agent payments, AI-agent commerce, auditability, provenance, privacy, compliance, and automation workflows; source-traceable review of relevant standards and market signals | AI-related blockchain demand is commercially relevant enough to test Cardano's position | AI demand is mostly narrative, too early, or better served by non-chain infrastructure | Decide whether AI merits serious Cardano ecosystem attention or only light monitoring | | Where does Cardano's technical fit translate into buyer-relevant advantage? | Buyer/problem mapping against Cardano properties such as deterministic execution, formal methods, low-cost settlement, auditability, transaction traceability, and privacy-adjacent infrastructure | Some Cardano properties map to specific AI buyer requirements | Technical strengths do not translate into purchase criteria or deployment decisions | Focus AI positioning on defensible claims and avoid claims buyers do not value | | What current Cardano AI activity is evidence of adoption rather than isolated building? | Source-traceable inventory of AI-related Cardano activity, production status, users, transactions, revenue where available, partner involvement, and evidence limitations | Existing activity provides credible early signal for an AI vertical | Current activity is too isolated, small, unaudited, or off-chain to justify vertical status | Decide whether to build from current Cardano AI activity or treat it as exploratory | | Which AI use cases are most credible for Cardano? | Use-case screening across agent payments, agent identity, on-chain logging, audit trails, privacy-preserving AI, compliant enterprise agents, data provenance, AI-enabled DeFi, and applicant-justified categories | A shortlist of AI use cases has buyer pull and Cardano-side relevance | AI use cases remain too broad or generic to prioritize | Select use cases for deeper validation, pilots, partner engagement, or rejection | | Which buyer or adopter segments are reachable? | Applicant-justified primary research with some mix of enterprise buyers, AI builders, AI-agent platforms, Cardano AI teams, non-Cardano AI infrastructure teams, agencies, integrators, payment/commerce providers, and ecosystem stakeholders | There are identifiable buyers, users, or partners Cardano can reach | The opportunity lacks reachable buyers or relies mainly on internal ecosystem interest | Define target segments and access routes for next-stage engagement | | Can Cardano compete against specialised AI chains and broader agent-payment infrastructure? | Competitive benchmark against specialised AI chains, general-purpose L1/L2 ecosystems, agent-payment protocols, and non-chain infrastructure where relevant | Cardano has a defensible position in selected AI use cases | Cardano is structurally disadvantaged or undifferentiated in the tested comparison set | Decide whether to compete, partner, narrow the positioning, or deprioritize | | What would make AI a real Cardano adoption vertical? | Adoption pathway showing buyer segment, use case, technical requirement, delivery partner, revenue path, transaction path, measurable adoption signal, and evidence threshold | AI can be expressed as a practical adoption pathway with measurable outcomes | AI remains a narrative without a credible path to buyers, revenue, or Cardano-side activity | Define conditions for go, conditional go, monitor, or no-go | | Which AI narratives are credible by audience? | Audience-specific claim testing or credibility assessment across enterprise, developers/builders, ecosystem funders, partners, and broader external audiences | Some AI positioning claims are credible with named audiences | AI positioning is liked internally but fails external credibility tests | Build precise AI positioning guidance and avoid weak campaign themes | | What partner or delivery pathway would be needed? | Mapping of implementation partners, agencies, integrators, AI-agent platforms, payment providers, privacy/compliance partners, and Cardano ecosystem teams; evidence of reachability and role clarity | AI adoption can be supported by identifiable delivery routes | AI opportunities lack delivery capacity or depend on unavailable partners | Hand off partner gaps to delivery-network work or define targeted partner development | | What Cardano-side value would AI create? | Evidence of possible transaction activity, retained users, recurring payments, service revenue, developer activity, partner commitments, or ecosystem funding relevance | AI could generate measurable Cardano-side adoption or economic activity | AI value is mostly off-chain, reputational, or captured by vendors without ecosystem benefit | Decide whether AI deserves ecosystem investment and which KPIs should govern it | | What is the go/no-go recommendation for AI as a strategic vertical? | Synthesis across demand, technical fit, current activity, competitive position, buyer reachability, narrative credibility, delivery path, and Cardano-side value | Cardano can proceed with clear conditions, priority use cases, and evidence thresholds | Cardano should deprioritize AI or monitor only until conditions change | Approve a strategic posture: go, conditional go, monitor, or no-go | | Which findings should be handed to adjacent RFPs? | Boundary map to brand, enterprise/RWA, L2/interoperability, use-case positioning, delivery partners, stablecoin/payment liquidity, and customer segmentation | RFP #9 informs adjacent work without absorbing it | The AI RFP becomes a general ecosystem strategy or technical roadmap study | Keep scope clean and route non-AI blockers to the right workstream | *** ## 7. Scope of Work ### In scope The selected vendor should cover: * AI commercial positioning assessment; * external AI/blockchain demand scan; * Cardano technical-fit validation against buyer requirements; * neutral review of current Cardano-side AI activity; * AI use-case screening and prioritization; * buyer and adopter segment mapping; * use-case-specific competitive benchmarking; * audience credibility and proof-requirement mapping; * delivery partner and implementation pathway assessment; * revenue, transaction, and Cardano-side value pathway analysis; * go/no-go, conditional-go, monitor, or no-go recommendation; * research-to-action pathway; * cross-RFP handoff notes; * public and confidential reporting. ### Screening-first scope Applicants should propose a screening-first approach. The research should begin with a broad scan of AI-related opportunities and then focus deeper work on the most credible use cases. A proposal that attempts to deeply analyze every AI use case, every competitor, and every audience may be considered weak if it cannot justify evidence access and decision value. A narrower proposal with clear screening logic, credible buyer validation, and strong go/no-go logic may be stronger than a broad generic AI market study. Candidate AI areas may include: | AI Area | Research Purpose | | ---------------------------------------------- | --------------------------------------------------------------------------------------------------------------------- | | Agent payments and service monetization | Test whether agent-to-agent, agent-to-business, or pay-per-use service flows create Cardano-relevant demand | | Agent identity and accountability | Test whether agents need verifiable identity, audit trails, or transaction accountability that blockchain can support | | On-chain logging and auditability | Test whether immutable logs or decision records matter to buyers and where they create adoption value | | Privacy, compliance, and governed AI workflows | Test whether privacy or compliance requirements create buyer pull for Cardano-relevant infrastructure | | Data provenance and proof of origin | Test whether data lineage, model input provenance, or content authenticity require blockchain-backed verification | | AI-enabled DeFi or automation | Test whether AI agents can create meaningful on-chain activity, liquidity, transactions, or user value | | AI marketplaces or agent service platforms | Test whether marketplaces for AI services need Cardano settlement, identity, payments, or verification | | Applicant-justified alternatives | Allow vendors to add or combine categories where evidence value is stronger | This list is guidance, not a fixed required list. Applicants should explain which use cases they will screen, which they will deep dive, and why. ### Out of scope and handoff boundaries This RFP should remain focused on AI commercial positioning, buyer validation, competitive position, use-case prioritization, and go/no-go decision-making. It should not become a general AI strategy, technical roadmap, or product implementation brief. | Topic | In Scope for This RFP | Out of Scope for This RFP | | --------------------------- | ----------------------------------------------------------------------------------------------- | -------------------------------------------------------------------------------- | | Current Cardano AI activity | Source-traceable evidence review and adoption-signal assessment | Promotional profiles, project ranking for funding, or unaudited project claims | | Brand and messaging | AI-specific audience credibility and proof requirements | Full Cardano brand awareness or perception study | | Enterprise and RWA | Enterprise AI buyer requirements where relevant | Full enterprise readiness, SLA, compliance, or production-conversion assessment | | L2 and interoperability | AI adoption blockers or requirements that touch scaling, cross-chain flows, or interoperability | Full L2 demand study, bridge strategy, or technical requirements register | | Delivery partners | AI-specific delivery route and partner need | Full partner certification model or delivery partner network design | | Stablecoins and payments | Agent-payment pathways and payment-related adoption signals | Stablecoin liquidity incentive design, market-maker strategy, or venue analysis | | Use-case landscape | AI as a strategic vertical and AI-specific competitive position | Full Cardano use-case audit or broader vertical landscape | | AI product development | Requirements and opportunity evidence | Product build, agent implementation, protocol implementation, or pilot execution | | Campaign execution | Evidence to inform future positioning | PR, media buying, creative campaign development, or brand redesign | Findings that materially affect another research initiative should be captured in the Cross-RFP Handoff Memo rather than expanded inside this RFP. ### Optional / nice-to-have stretch scope Applicants may propose optional stretch scope, priced separately, such as: * deeper primary validation with enterprise AI buyers; * deeper competitor analysis of specialised AI chains or agent-payment infrastructure; * targeted AI claim testing by audience; * pilot design templates for top validated AI opportunities; * more detailed adoption impact-tier model; * additional public-facing artifacts or community briefing material. *** ## 8. Required Methodology The selected vendor must use a mixed-method research design. Desk research alone is not sufficient. Applicants should propose the respondent categories, methods, and evidence sources needed to answer the decision gates. The proposal should justify why the plan is proportionate to budget, access, timeline, and expected decision value. ### Core research hypotheses Applicants should test, refine, or reject hypotheses such as: | Hypothesis | What Must Be Tested | Decision Relevance | | --------------------------------------------------------------------------------------- | ------------------------------------------------------------------------------------------------------------------------------------------------------------- | ------------------------------------------------------------------------ | | AI is a commercially relevant blockchain opportunity, not only a narrative theme | Whether agent payments, AI accountability, provenance, privacy/compliance, autonomous services, or related markets show buyer demand where blockchain matters | Determines whether AI deserves strategic attention | | Cardano has buyer-relevant AI advantages in specific use cases | Whether Cardano properties map to actual buyer requirements and deployment decisions | Prevents technical claims from being mistaken for market pull | | Current Cardano AI activity provides early adoption signal | Whether existing activity shows real users, deployments, transactions, revenue, partner commitments, or repeat usage | Determines whether Cardano has a foundation to build from | | Cardano's AI position is use-case specific | Whether Cardano is credible in some AI use cases but weak or undifferentiated in others | Enables prioritization rather than broad AI positioning | | Specialised AI chains and agent-payment infrastructure create real competitive pressure | Whether buyers, builders, or partners prefer alternatives, and why | Shapes whether Cardano should compete, partner, narrow scope, or monitor | | AI positioning must differ by audience | Whether enterprise buyers, developers, partners, and ecosystem funders believe different claims and require different proof | Prevents one generic AI narrative | | AI can become a Cardano adoption vertical only if there is a measurable pathway | Whether a use case can connect buyer, technical requirement, delivery partner, revenue path, transaction path, and adoption signal | Supports go/no-go or conditional-go decision | ### Required evidence types | Evidence Type | Required Use | Notes | | ---------------------------------- | ---------------------------------------------------------------------------------------------------------- | ----------------------------------------------------------------------------------------------------------------------------------------------------- | | External market and standards scan | Establish whether AI/blockchain demand is real and where it is moving | Should include source-traceable review of agent payments, agentic commerce, AI accountability, provenance, privacy/compliance, and relevant standards | | Cardano ecosystem evidence review | Identify current AI-related activity and assess adoption signal | Should avoid promotional treatment and label evidence limitations | | Primary interviews or expert calls | Validate buyer needs, technical requirements, competitive alternatives, and adoption pathways | Respondent categories should be proposed and justified by applicants | | Competitive benchmark | Compare Cardano with relevant alternatives by use case | Competitor set should be vendor-justified | | Use-case screening | Rank AI opportunities by demand, fit, competitiveness, reachability, and Cardano-side value | Should include rejected or weak use cases | | Audience credibility assessment | Identify which claims, objections, and proof requirements matter by audience | Narrative testing may be included where the applicant proposes a credible method | | Adoption pathway analysis | Connect use cases to buyer segment, delivery route, revenue path, transaction path, and evidence threshold | Required for go/no-go logic | ### Primary research expectations Applicants should propose a primary research plan that may include some combination of: * enterprise or institutional AI buyers; * AI-agent platform builders or operators; * AI infrastructure providers; * developers building AI services, agents, or automation tools; * payment, commerce, identity, provenance, or compliance experts; * Cardano ecosystem builders with AI-related activity; * non-Cardano builders or operators using specialised AI chains or competing infrastructure; * delivery partners, agencies, or integrators; * privacy, compliance, or auditability specialists; * ecosystem stakeholders with cross-market visibility. The RFP does not require every category. Applicants should explain: * which categories they will include; * why each category is necessary; * how respondents map to decision gates; * how they will avoid over-reliance on Cardano insiders; * how they will include negative or skeptical evidence; * how they will handle confidential or commercially sensitive respondent information. ### Desk research requirements Desk research should include: * agent payments and agentic commerce standards, such as x402, Agentic Commerce Protocol, Machine Payments Protocol, and comparable vendor-justified standards; * specialised AI blockchains and AI-focused infrastructure; * AI-agent marketplaces, payment models, and service monetization patterns; * enterprise AI auditability, compliance, privacy, provenance, and accountability needs; * public evidence of Cardano AI-related activity, without treating project claims as confirmed adoption; * relevant peer-chain activity and non-chain alternatives; * public market evidence that indicates whether demand is commercial, speculative, early-stage, or unclear. If a claim depends on unavailable, proprietary, paid, or confidential data, the vendor must mark it as requiring validation or state the limitation. ### Competitive benchmarking requirements The benchmark should compare Cardano against relevant alternatives by use case, not through one generic competitor table. Applicants should justify the competitive set. It may include: * specialised AI blockchains; * L1s or L2s used for agent payments or AI-adjacent workflows; * payment-oriented infrastructure; * agent commerce protocols; * non-chain infrastructure where blockchain is not necessary. For each benchmarked use case, the vendor should assess: * buyer requirement; * Cardano's claimed fit; * competitor alternatives; * evidence of Cardano advantage; * evidence of Cardano weakness; * switching or adoption condition; * confidence level; * strategic implication. ### What should not count as evidence The following should not be treated as sufficient evidence: * generic "AI plus blockchain" market narratives; * claims about Cardano AI traction without sources or validation; * project self-promotion without adoption evidence; * competitor comparisons based only on marketing language; * market sizing without assumptions and source traceability; * buyer interest without budget, use case, pain point, or decision condition; * technical fit that is not tied to a buyer requirement; * narrative testing only with Cardano insiders; * all-positive recommendations; * recommendations that do not map to go/no-go conditions. *** ## 9. Expected Deliverables Deliverables are grouped into core decision outputs, supporting research outputs, and public/community-facing outputs. ### Core decision outputs | Deliverable | Description | Format | Acceptance Criteria | | ------------------------------------ | ----------------------------------------------------------------------------------- | --------------------------------- | -------------------------------------------------------------------------------------------------------------------------------------------------------------------------------- | | Executive Decision Memo | Concise synthesis of findings and recommended strategic posture | PDF/doc, max \[8] pages | States recommended posture, priority use cases, evidence confidence, conditions, actions, and what should not be concluded | | AI Commercial Positioning Assessment | Assessment of where Cardano has a credible AI market position and where it does not | Report section plus summary table | Separates credible, weak, unproven, and rejected positions; ties each to buyer need, competitor alternative, and evidence source | | AI Use-Case Prioritization Matrix | Ranked screening of AI-related use cases | Spreadsheet/table plus narrative | Includes buyer segment, problem, technical requirement, Cardano relevance, competitor alternative, reachability, Cardano-side value, confidence, and recommended action | | Buyer and Audience Credibility Map | Map of which claims, proof points, and objections matter by audience | Table and narrative | Includes audience, decision criteria, credible claims, required proof, objections, disqualifiers, and evidence basis | | Competitive Benchmark | Use-case-specific comparison against relevant alternatives | Benchmark table plus explanation | Vendor justifies competitor set; benchmark is by use case; includes evidence of advantage, weakness, switching conditions, and confidence | | AI Adoption Pathway Model | Practical pathway from buyer problem to Cardano-side value | Framework/table | For each priority path, includes buyer segment, use case, technical requirement, delivery route, revenue path, transaction path, adoption signal, and minimum evidence threshold | | Go/No-Go Recommendation | Strategic posture for AI as a Cardano vertical | Included in memo and final report | Provides go, conditional-go, monitor, or no-go recommendation; conditional-go includes conditions, evidence thresholds, next action, and next decision point | ### Supporting research outputs | Deliverable | Description | Format | Acceptance Criteria | | ----------------------------------------- | --------------------------------------------------------------------------------- | -------------------------------- | ------------------------------------------------------------------------------------------------------------------------------------------------------------------------- | | Current Activity Evidence Review | Neutral review of Cardano-side AI building and adoption evidence | Evidence register plus narrative | Uses source-traceable evidence; separates public claims, validated data, confidential evidence, and vendor judgment; avoids promotional language | | Narrative Options and Stress-Test Results | Tested AI positioning options by audience, where proposed | Memo or report section | Includes tested claims, target audience, required proof, credibility response, objections, evidence gaps, and recommendation | | Research-to-Action Pathway | Recommended next engagements, partner actions, pilots, or evidence-building steps | Roadmap table | Includes action, owner/workstream, dependency, evidence threshold, impact tier, and suggested next decision point | | Cross-RFP Handoff Memo | Findings that belong in adjacent RFPs or workstreams | Memo, max \[5] pages | Maps findings to brand, enterprise/RWA, L2/interoperability, use-case landscape, delivery partners, stablecoins/payments, or segmentation; explains why each is a handoff | | Final Research Report | Full research record | PDF/doc | Includes methodology, sources, respondent categories, findings by decision gate, evidence tables, limitations, and confidence labels | ### Public/community-facing outputs | Deliverable | Description | Format | Acceptance Criteria | | ------------------ | ------------------------------------------------- | ---------------------------- | -------------------------------------------------------------------------------------------------------------------------- | | Final Presentation | Presentation-ready synthesis | Slide deck | Covers question, evidence gap, method, key findings, strategic posture, use-case priorities, limitations, and next actions | | Public Summary | Non-confidential summary suitable for publication | Markdown/PDF, max \[5] pages | Includes methodology overview, key findings, evidence caveats, publishable recommendations, and excludes sensitive data | *** ## 10. Acceptance Criteria Each deliverable must be decision-useful, evidence-based, and traceable. Every factual claim must be traceable to a source, interview, expert call, dataset, public documentation, confidential evidence available to approved reviewers, or clearly identified vendor judgment. External AI market claims, competitor claims, and current Cardano-side AI activity claims must include source dates and be current as of submission. Every priority AI use case must include: * buyer or adopter segment; * problem being solved; * required technical capability; * reason Cardano may be relevant; * relevant competitor or alternative; * evidence basis; * delivery route; * revenue pathway, transaction pathway, or other Cardano-side value path; * adoption signal; * confidence level; * recommended action. Every recommendation must state: * what should be done; * who the action is relevant for; * what evidence supports it; * what uncertainty remains; * what would change the recommendation; * what should not be concluded from the evidence. A deliverable may be considered unacceptable if it: * treats AI as strategically important without evidence; * assumes Cardano's technical properties are buyer-relevant without validation; * relies mainly on Cardano insider views; * uses named ecosystem projects promotionally; * repeats project claims without source traceability or source dates; * ignores specialised AI chains or non-chain alternatives; * produces only positive recommendations; * fails to distinguish real adoption from cosmetic activity; * lacks a go/no-go, conditional-go, monitor, or no-go posture; * does not connect outputs to decision gates. *** ## 11. Vendor Eligibility Applicants may be independent researchers, consultancies, ecosystem teams, academic groups, product strategy firms, AI commercialization specialists, or consortia. Subcontracting is permitted where responsibilities are clearly defined. Applicants should demonstrate: * AI commercialization, market research, or product strategy capability; * ability to conduct primary research with relevant buyers, builders, operators, or experts; * understanding of Web3, L1/L2 ecosystems, AI-agent infrastructure, agent payments, or adjacent markets; * competitive benchmarking capability; * ability to assess technical claims against buyer requirements; * ability to translate research into decision-oriented positioning and adoption strategy; * ability to manage confidential evidence, conflicts of interest, and human-subject safeguards. Preferred but not mandatory capabilities include: * prior research on AI agents, agentic commerce, machine payments, AI accountability, privacy, compliance, provenance, or enterprise AI; * experience with blockchain ecosystem strategy; * enterprise buyer research; * technical review capability or access to qualified technical subcontractors; * experience producing public and confidential research outputs. *** ## 12. Proposal Submission Requirements Applicants must follow the shared Submission Pack and include the common documents: cover letter, technical proposal, budget proposal, team credentials, evidence access plan, ethics/data handling statement, and conflicts declaration. Applicants bidding on multiple Product Research Initiative RFPs must disclose shared staffing, shared respondent recruitment, shared evidence collection, and any assumptions that could create respondent fatigue or duplicated outreach. For this RFP, the technical proposal must also include: * current AI/blockchain market scan approach, with source-date discipline; * AI use-case screening and prioritization method; * buyer, builder, partner, or audience access plan; * competitive benchmark approach; * Cardano-side value and adoption pathway logic; * go/no-go recommendation framework. External AI market claims, competitor claims, and current Cardano-side AI activity claims should be current as of submission and should include source dates. Budgets should explain how cost relates to buyer access, AI market expertise, competitive research, narrative or claim testing, and decision value. ## 13. Evaluation Guidance Proposals will be assessed using the CPC standard research grant evaluation framework: Fit to Grant Objectives, Team Capability, Proposal Quality and Execution Plan, and Cost Efficiency. The full scoring framework is maintained on the shared evaluation page. For this RFP, strong fit means the proposal can distinguish AI narrative from buyer demand and determine whether AI should be treated as a Cardano strategic vertical, conditional opportunity, monitor-only area, or no-go area. A strong proposal should source-date external AI market claims; validate buyer-relevant technical fit; review current Cardano AI activity neutrally using source-dated evidence; compare Cardano with relevant AI chains, agent-payment infrastructure, general-purpose chains, and non-chain alternatives; and map Cardano-side value. Proposals should score lower if they rely on generic AI hype, unverified project claims, internal enthusiasm, weak competitive comparison, or recommendations without buyer, revenue, transaction, or Cardano-side value logic. Cost should be judged against evidence value, not lowest price. Higher-cost proposals may be justified by stronger buyer access, specialist AI market knowledge, competitive research, or credible narrative/claim testing. ## 14. Timeline and Milestones Final calendar dates will be confirmed on the Product Research Initiatives - Grants page before the call opens. | Milestone | Target Date / Timing | Purpose | | ----------------------------- | ------------------------- | ------------------------------------------------------------------------------------------------------------------------------------------------------------ | | RFP publication | Confirmed on grants page | Open call for proposals | | Clarification window opens | Confirmed on grants page | Applicants may submit questions | | Clarification window closes | Confirmed on grants page | Final clarification responses issued | | Proposal deadline | Confirmed on grants page | Applicant submissions due | | Award notification | Confirmed on grants page | Selected vendor notified | | Kickoff | Project week 1 | Confirm objectives, decision gates, scope, workplan, communication cadence, confidentiality expectations, and public-summary expectations | | Research design alignment | Applicant-proposed timing | Align on respondent categories, instruments, evidence standards, competitor selection logic, use-case screening criteria, and data handling before fieldwork | | Source and evidence review | Applicant-proposed timing | Review planned public sources, confidential sources, paid/proprietary sources, and current activity validation approach | | Stakeholder access check | Applicant-proposed timing | Surface recruitment issues, weak access, over-reliance on insiders, missing respondent categories, or substitutions needed | | Early signal check | Applicant-proposed timing | Review early evidence on demand, technical fit, competitive position, and possible false positives | | Interim findings review | Applicant-proposed timing | Test whether evidence is answering decision gates and whether scope needs adjustment | | Draft deliverables review | Applicant-proposed timing | Review positioning assessment, use-case prioritization, buyer credibility map, competitive benchmark, adoption pathway, and go/no-go logic | | Final report and presentation | Applicant-proposed timing | Present final findings, confidence levels, limitations, decisions enabled, and recommended actions | | Public summary review | Applicant-proposed timing | Confirm what can be published and what must remain confidential | | Public summary | Applicant-proposed timing | Publish non-confidential summary | Applicants should propose a timeline appropriate to their methodology. Unrealistic timelines should be avoided, especially where primary external validation, competitor research, confidential project evidence, or buyer access is required. *** ## 15. Governance, Reporting, and Communication The selected vendor will participate in structured checkpoints. The process should protect research quality without turning the work into committee-managed consulting. | Checkpoint | Purpose | | -------------------------- | -------------------------------------------------------------------------------------------------------------------------------------------------------------------------------- | | Kickoff | Confirm objectives, decision gates, scope, workplan, communication cadence, confidentiality expectations, and public-summary expectations | | Research Design Review | Align on respondent categories, interview or survey instruments, evidence standards, competitor selection logic, use-case screening criteria, and data handling before fieldwork | | Source and Evidence Review | Review planned public sources, confidential sources, paid/proprietary sources, and how current Cardano-side AI activity will be validated neutrally | | Stakeholder Access Check | Surface recruitment issues, weak access, over-reliance on insiders, missing respondent categories, or substitutions needed | | Early Signal Check | Review early evidence on demand, technical fit, competitive position, and potential false positives | | Interim Findings Review | Test whether evidence is answering the decision gates and whether scope needs adjustment | | Draft Deliverables Review | Review positioning assessment, use-case prioritization, buyer credibility map, competitive benchmark, adoption pathway, and go/no-go logic | | Final Presentation | Present final findings, confidence levels, limitations, decisions enabled, and recommended actions | | Public Summary Review | Confirm what can be published and what should remain confidential | Before primary fieldwork begins, the vendor should align with CPC or a designated review group on: * respondent categories; * recruitment strategy; * proposed interview, survey, or expert-call instruments; * use-case screening model; * competitive benchmark model; * evidence confidence labels; * minimum evidence standard; * data handling and confidentiality approach; * treatment of confidential Cardano-side evidence; * treatment of commercially sensitive enterprise, buyer, project, or partner information; * public-summary boundaries. This review should protect research quality without directing the findings. The vendor should retain methodological independence and should be expected to report inconvenient or negative findings. The vendor should not wait until the final report to disclose: * weak respondent access; * over-reliance on insider evidence; * unsupported demand claims; * project evidence that cannot be validated; * competitor evidence that weakens Cardano's position; * evidence that AI should be monitor-only or no-go; * conflicts of interest; * scope creep into adjacent RFPs. *** ## 16. Risks, Bias Controls, and Safeguards Applicants must include a research integrity plan. | Risk | Required Control | | ------------------------------------------ | ------------------------------------------------------------------------------------------------------------------------------- | | AI hype bias | Require buyer validation, competitor comparison, and rejected or weak hypotheses | | Cardano insider bias | Separate Cardano-side evidence from external buyer and non-Cardano evidence | | Project-promotion bias | Treat named projects as evidence sources to validate, not proof of market position | | Technical-fit bias | Tie technical claims to buyer requirements and deployment decisions | | Privacy/compliance assumption bias | Treat privacy and compliance relevance as hypotheses to test | | Competitor narrative bias | Use buyer/operator evidence and source-traceable benchmarks, not only marketing claims | | Weak source quality | Label source type, access status, and confidence level | | False adoption signal | Distinguish recurring usage, revenue, deployments, and transaction activity from announcements or demos | | All-positive recommendations | Require weak, rejected, conditional, and no-go findings where evidence supports them | | Conflicted recommendations | Require conflict declarations and separate conflicted evidence from independent evidence | | Confidentiality reducing public usefulness | Produce confidential full evidence plus publishable summary themes | | Scope creep | Use cross-RFP handoff logic for brand, enterprise/RWA, L2/interoperability, delivery partners, payments, and use-case landscape | The final report must include a limitations section explaining what the research can and cannot support. *** ## 17. Clarification Process Applicants may submit clarification questions during the clarification window. Questions may address: * AI scope and use-case boundaries; * expected respondent categories; * treatment of confidential project or buyer evidence; * minimum evidence standards; * competitor selection; * agent payments, agent commerce, privacy, auditability, or provenance scope; * treatment of named Cardano-side projects; * narrative or claim testing expectations; * public versus confidential outputs; * budget format; * overlap with other Product Research Initiative RFPs. Responses should be maintained in a rolling Q\&A log where practical so applicants receive consistent information. If a clarification materially changes scope, deadline, eligibility, or deliverables, the RFP timeline may be adjusted. The clarification process should also help assess applicant judgment. Strong questions may demonstrate methodological specificity, commercial realism, understanding of AI-market uncertainty, and awareness of positioning tradeoffs. *** ## 18. Data Handling, Confidentiality, and Public Summary The selected vendor must provide a data handling plan covering: * informed consent for interviews, expert calls, workshops, or surveys; * recording and transcription policy; * anonymization approach; * raw notes and transcript handling; * storage and access controls; * retention and deletion plan; * treatment of commercially sensitive buyer, enterprise, project, or partner information; * treatment of proprietary, paid, or respondent-provided data; * separation of public findings, confidential findings, and raw data; * publication boundaries. Research outputs should distinguish: * public findings suitable for ecosystem publication; * confidential findings suitable only for CPC or approved reviewers; * sensitive respondent information that should not be published; * raw data that should not be published; * proprietary data that CPC can inspect but may not publish; * vendor judgment where primary data cannot be disclosed. A public summary is expected unless specific findings cannot be published for justified confidentiality reasons. The public summary should include: * methodology overview; * respondent category summary; * high-level demand findings; * high-level AI use-case prioritization; * high-level competitive positioning findings; * publishable go/no-go or conditional-go rationale; * evidence caveats and limitations; * recommended next steps where publishable. The public summary should not expose: * confidential respondent identities; * commercially sensitive enterprise or buyer information; * confidential project data; * unreleased product or roadmap details; * strategic details that could harm respondents or counterparties; * proprietary data restrictions; * details that could undermine respondent trust. If proprietary, paid, respondent-provided, or locally restricted data is used, the vendor must state: * source; * access conditions; * whether CPC can inspect it; * whether it can be cited publicly; * what limitations apply; * whether it can be retained after project completion. If a finding depends heavily on data CPC cannot inspect, the vendor must label the confidence level accordingly. *** ## 19. Human Subject Research Ethics Because this RFP may require interviews, expert calls, or surveys with buyers, builders, enterprise stakeholders, ecosystem participants, and commercially sensitive respondents, the selected vendor must apply basic human-subject safeguards. At minimum, the vendor should: * tell respondents the purpose of the research; * explain who the research is for; * state how information may be used; * ask whether comments are attributable, anonymized, or confidential; * obtain consent before recording; * avoid publishing sensitive respondent information without permission; * allow respondents to clarify attribution status; * avoid misleading respondents about the purpose or audience of the work; * avoid exposing respondents to employment, commercial, regulatory, competitive, or reputational risk. *** ## 20. Conflicts of Interest Applicants and subcontractors must disclose any actual, potential, or perceived conflicts of interest, including: * paid work for Cardano ecosystem entities; * paid work for non-Cardano AI or blockchain ecosystems; * advisory roles; * governance roles; * financial exposure to Cardano, AI projects, competitor chains, AI infrastructure providers, or agent-payment providers; * relationships with projects, vendors, buyers, respondents, or partners included in the research; * ownership or commercial interest in an AI product, protocol, chain, tooling provider, agency, payment provider, or integration that may be affected by the research; * intent to apply for future funding connected to the findings; * subcontractor conflicts. Declared conflicts do not automatically disqualify an applicant, but they must be managed. Undisclosed conflicts may be grounds for rejection, non-award, payment holdback, or termination. Research outputs should separate: * evidence from conflicted respondents; * evidence from teams seeking funding; * independent buyer, operator, or competitor evidence; * vendor interpretation; * commercially sensitive findings. *** ## 21. Terms and Conditions Standard terms and conditions will be provided through the shared tender process before award. Applicants should assume that final award terms will cover ownership and permitted publication of deliverables, confidentiality, payment milestones, termination, data protection, subcontractor approval, warranties or disclaimers, and the governing process. *** ## 22. Appendix A: Proposal Checklist Applicants should confirm that their proposal includes: * [ ] Cover letter * [ ] Understanding of the brief * [ ] Proposed methodology * [ ] Decision gate mapping * [ ] Stakeholder access plan * [ ] Competitive benchmark plan * [ ] Current activity evidence plan * [ ] Narrative or claim testing plan, if proposed * [ ] Data sources * [ ] Workplan * [ ] Deliverables plan * [ ] Team qualifications * [ ] Relevant experience * [ ] Risk and bias mitigation plan * [ ] Confidentiality and data handling approach * [ ] Ethics approach * [ ] Timeline * [ ] Budget breakdown * [ ] Conflicts declaration * [ ] Prior work examples, if applicable *** ## 23. Appendix B: Suggested Decision Gate Mapping Template | Decision Gate | Method Used | Evidence Source | Deliverable | Limitation / Risk | | ---------------------------- | ----------- | --------------- | -------------- | ----------------- | | AI commercial opportunity | \[Method] | \[Source] | \[Deliverable] | \[Risk] | | Buyer-relevant technical fit | \[Method] | \[Source] | \[Deliverable] | \[Risk] | | Current activity evidence | \[Method] | \[Source] | \[Deliverable] | \[Risk] | | AI use-case prioritization | \[Method] | \[Source] | \[Deliverable] | \[Risk] | | Buyer/adopter reachability | \[Method] | \[Source] | \[Deliverable] | \[Risk] | | Competitive position | \[Method] | \[Source] | \[Deliverable] | \[Risk] | | Adoption vertical pathway | \[Method] | \[Source] | \[Deliverable] | \[Risk] | | Audience credibility | \[Method] | \[Source] | \[Deliverable] | \[Risk] | | Partner/delivery pathway | \[Method] | \[Source] | \[Deliverable] | \[Risk] | | Cardano-side value | \[Method] | \[Source] | \[Deliverable] | \[Risk] | | Go/no-go recommendation | \[Method] | \[Source] | \[Deliverable] | \[Risk] | | Cross-RFP handoffs | \[Method] | \[Source] | \[Deliverable] | \[Risk] | *** ## 24. Appendix C: Suggested AI Use-Case Screening Template | AI Use Case | Buyer / Adopter Segment | Problem | Technical Requirement | Cardano Relevance | Competitor Alternative | Delivery Route | Revenue Pathway | Cardano-Side Value | Evidence Source | Confidence | Recommended Action | | ----------- | ----------------------- | ---------- | --------------------- | ----------------- | ---------------------- | -------------- | --------------- | ------------------ | --------------- | ---------------------- | ------------------------------------- | | \[Use case] | \[Segment] | \[Problem] | \[Requirement] | \[Relevance] | \[Alternative] | \[Route] | \[Pathway] | \[Value] | \[Source] | \[High / Medium / Low] | \[Go / Conditional / Monitor / No-go] | *** ## 25. Appendix D: Suggested Competitive Benchmark Template | Use Case | Buyer Requirement | Cardano Position | Relevant Alternatives | Evidence of Advantage | Evidence of Weakness | Switching / Adoption Condition | Confidence | Strategic Implication | | ----------- | ----------------- | ---------------- | --------------------- | --------------------- | -------------------- | ------------------------------ | ---------------------- | ----------------------------------------------- | | \[Use case] | \[Requirement] | \[Position] | \[Alternatives] | \[Evidence] | \[Evidence] | \[Condition] | \[High / Medium / Low] | \[Compete / Partner / Narrow / Monitor / Avoid] | *** ## 26. Appendix E: Suggested Narrative Stress-Test Template | Positioning Claim | Target Audience | Required Proof | Credibility Response | Objections | Evidence Gap | Recommendation | | ----------------- | --------------- | -------------- | ---------------------- | ------------ | ------------ | ---------------------------------- | | \[Claim] | \[Audience] | \[Proof] | \[High / Medium / Low] | \[Objection] | \[Gap] | \[Lead / support / revise / avoid] | *** ## 27. Appendix F: Suggested AI Adoption Pathway Template | Buyer Segment | Use Case | Required Capability | Partner / Delivery Route | Revenue Pathway | Transaction Pathway | Cardano-Side Value | Adoption Signal | Minimum Evidence Threshold | Go/No-Go Condition | | ------------- | ----------- | ------------------- | ------------------------ | --------------- | ------------------- | ------------------ | --------------- | -------------------------- | ------------------ | | \[Segment] | \[Use case] | \[Capability] | \[Route] | \[Revenue] | \[Transaction] | \[Value] | \[Signal] | \[Threshold] | \[Condition] | *** ## 28. Appendix G: Suggested Current Activity Evidence Register | Activity / Project Category | Source | Claim | Evidence Type | Validation Status | Adoption Signal | Limitation | Confidence | Treatment in Public Summary | | --------------------------- | --------- | -------- | ---------------------------------------------------------------- | ------------------------------------ | --------------- | ------------- | ---------------------- | ----------------------------- | | \[Category] | \[Source] | \[Claim] | \[Public / confidential / interview / dataset / vendor judgment] | \[Validated / partial / unvalidated] | \[Signal] | \[Limitation] | \[High / Medium / Low] | \[Publish / anonymize / omit] | --- # Agent Instructions: Querying This Documentation If you need additional information that is not directly available in this page, you can query the documentation dynamically by asking a question. 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