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:
Assess whether AI-related blockchain demand is commercially meaningful and where it is moving.
Validate whether Cardano's technical properties translate into buyer-relevant AI advantage.
Review Cardano-side AI activity neutrally, with source traceability and clear evidence limitations.
Screen and prioritize AI use cases based on buyer pull, Cardano fit, competitive position, reachability, and Cardano-side value.
Benchmark Cardano against relevant specialised AI chains, agent-payment infrastructure, general-purpose blockchain ecosystems, and non-chain alternatives.
Map buyer, builder, partner, and audience credibility requirements for Cardano's AI positioning.
Assess delivery pathways, partner routes, revenue pathways, transaction pathways, and adoption signals for priority use cases.
Test AI narratives or claims by audience where the applicant proposes a credible method.
Produce a go/no-go, conditional-go, monitor, or no-go recommendation for AI as a Cardano strategic vertical.
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.
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:
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.
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:
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
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
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
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
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.
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.
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.
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:
23. Appendix B: Suggested Decision Gate Mapping Template
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
[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]
[Requirement]
[Position]
[Alternatives]
[Evidence]
[Evidence]
[Condition]
[High / Medium / Low]
[Compete / Partner / Narrow / Monitor / Avoid]
26. Appendix E: Suggested Narrative Stress-Test Template
[Claim]
[Audience]
[Proof]
[High / Medium / Low]
[Objection]
[Gap]
[Lead / support / revise / avoid]
27. Appendix F: Suggested AI Adoption Pathway Template
[Segment]
[Use case]
[Capability]
[Route]
[Revenue]
[Transaction]
[Value]
[Signal]
[Threshold]
[Condition]
28. Appendix G: Suggested Current Activity Evidence Register
[Category]
[Source]
[Claim]
[Public / confidential / interview / dataset / vendor judgment]
[Validated / partial / unvalidated]
[Signal]
[Limitation]
[High / Medium / Low]
[Publish / anonymize / omit]
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