Appian CEO Matt Calkins Urges AI Industry to Put Trust First and Embrace a New Era of Responsible Development

In a bold move aimed at shaping the future of AI governance, Appian cofounder and CEO Matt Calkins has unveiled a fresh set of guidelines designed to advance responsible AI development and bolster trust between providers and users. His proposal arrives amid intensifying debates over data privacy, intellectual property rights, and the speed at which AI capabilities are evolving. Calkins frames his stance not as a shield against progress but as a practical blueprint to maximize AI’s potential by grounding it in transparency, consent, and fair use. The conversation centers on data provenance, consent, and the equitable handling of information, with Calkins pointing to contemporary regulatory signals as insufficient to address core accountability concerns. The result is a roadmap that seeks to recalibrate the industry’s approach to data, ownership, and user rights, in the hope of aligning rapid technological advancement with broad social trust.

Context and the call for responsible AI

The current moment in AI policy and practice is characterized by a mix of urgency, opportunity, and concern. On one side, enterprises are racing to harness AI to automate operations, unlock insights, and create new product offerings. On the other side, stakeholders—from policymakers to the public—are demanding tighter guardrails to prevent misuse, preserve privacy, and protect creators’ rights. In this climate, Calkins argues that the industry needs a principled framework that can coexist with speed and scale. He positions his approach as constructive and forward-looking, designed to accelerate AI’s real-world value while reducing the risks that have become a focal point for regulators and advocates.

From his vantage point, the discourse around regulation often misses critical elements such as data provenance—the provenance of data used in training, validation, and inference—and the fair-use implications of data aggregation. He contends that too many current regulatory narratives concentrate on broad strokes without addressing how data enters systems, how it can be traced back to its origins, and how the people and firms involved in its creation and use can be appropriately compensated or constrained. According to Calkins, this omission creates vulnerabilities: a gray zone in which large tech players may continue operating with limited accountability, while smaller firms and potential partners hesitate to engage for fear of uncertain rules or inadvertent noncompliance. This stagnation, he argues, stands between the industry and a more expansive, beneficial deployment of AI across sectors.

Calkins’s concerns are not rooted in opposition to innovation. He emphasizes that his goal is to enable “the maximum flourishing of AI.” In his view, responsible principles can coexist with aggressive development timelines if they are embedded into the design, deployment, and governance of AI systems. He underscores that the aim is not to erect roadblocks but to create a predictable, trustworthy foundation on which enterprises can build. The proposed guidelines are meant to offer clarity to developers, practitioners, and customers, ensuring that responsible practices become a core, not an afterthought, of AI strategies. This framing reflects a broader industry shift toward governance-driven AI that can scale without sacrificing individual rights or societal norms.

The timing of the proposal matters as well. The AI landscape is undergoing rapid transformation, with applications moving from prototype into mission-critical operations in industries ranging from finance and health to manufacturing and public services. The speed of adoption heightens the stakes for data handling, privacy protections, and IP management. At the same time, lawmakers and regulators are scrutinizing the tech sector more intensely, seeking clear standards that can be translated into compliance requirements. In this environment, Calkins’s call for structured guidelines aims to bridge the gap between aspirational ethics and practical implementation, offering a concrete path that aligns business incentives with user trust and regulatory expectations. By articulating a set of four core principles, he attempts to provide a common language that can be adopted by a broad spectrum of AI vendors, platform providers, and enterprise buyers alike.

This section of the broader discussion also touches on the broader social and economic implications of AI deployment. As automation and intelligent systems become embedded in decision-making processes, concerns about data ownership, algorithmic transparency, bias, and accessibility intensify. Proponents of responsible AI argue that meaningful governance can reduce the likelihood of harmful outcomes, improve model interpretability, and foster broader adoption by mitigating fear and uncertainty. Critics may worry that stringent rules could hamper experimentation or slow innovation. However, Calkins’s framing suggests that well-crafted rules can actually unlock more durable, scalable value by fostering trust, ensuring fair competition, and protecting the rights of individuals and content creators. The proposed guidelines are thus positioned as a strategic tool to align enterprise AI initiatives with long-term societal expectations while preserving competitive dynamism.

In this broader narrative, Appian seeks to position itself not only as a technology provider but as a thought leader in responsible AI practice. The company’s emphasis on low-code automation speaks to a different dimension of the AI value chain: the ability to democratize app development and put governance controls in the hands of enterprise teams. By advocating for explicit data-source disclosures, consent-based data usage, robust anonymization, and compensation for copyrighted material, the guidelines aim to reduce ambiguity around how AI systems are trained and operated. This clarity can help enterprises design, deploy, and scale AI with confidence, avoiding potential regulatory missteps and reputational risk while delivering tangible outcomes for users and customers. The result is a vision of AI where enterprise adoption is complemented by a robust trust framework that supports both innovation and accountability.

As the industry absorbs these ideas, the practical implications begin to unfold. For developers, the guidelines translate into concrete design choices: transparent data lineage that makes it possible to trace the origins of inputs and outputs; consent management mechanisms that capture user permissions and ensure fair compensation when data is used beyond agreed purposes; and processes for anonymization and protection of personally identifiable data. For enterprises, the emphasis on data provenance and fair use can drive more responsible sourcing of training data, stronger risk management practices, and clearer expectations in contracts with vendors and data providers. For users, this approach translates into clearer information about how personal data is used and more meaningful control over their digital footprints. Across the board, the proposed framework seeks to turn trust from a vague virtue into a practical, measurable asset that underpins the value AI can deliver.

In short, Calkins’s message is one of balanced progress. It acknowledges the undeniable benefits of AI while insisting that responsible governance must accompany technical advancement. The four-principle framework he articulates is designed to be comprehensive yet adaptable, capable of guiding diverse players through a rapidly changing landscape. As enterprises consider how to align their AI strategies with regulatory expectations and public sentiment, the guidelines offer a pathway to more predictable, auditable, and user-centered AI systems. The next sections delve into each principle in detail, unpacking how they can be interpreted, implemented, and measured in real-world settings, and why they matter for the future of enterprise AI.

Appian’s four principles for responsible AI: data sources, consent, anonymization, and copyright

Appian’s proposed guidelines are structured around four core principles intended to anchor AI development in accountability, fairness, and user respect. Each principle addresses a distinct facet of data handling and rights management, yet they are interconnected in practice, shaping how AI systems collect, process, and utilize information. The overarching aim is to establish a transparent, equitable framework that can be adopted by AI providers at scale while enabling enterprises to deploy AI with confidence in governance and compliance. Below, each principle is broken down with emphasis on implementation, potential challenges, and practical examples that illuminate how these ideas might work in real-world contexts.

Disclosure of data sources

The first principle centers on transparency about where data originates. In practice, this means AI developers and providers should clearly disclose the sources of data used to train, validate, and test models, as well as the data used for ongoing inference when applicable. This disclosure can take multiple forms: data catalogs that publicly or semipublicly describe datasets, metadata accompanying model releases that explain provenance, and contract terms that specify data lineage expectations between suppliers and purchasers. The objective is to bring visibility to the data supply chain so that users, customers, and regulators can assess the quality, relevance, and potential biases embedded in the data.

Implementing this principle requires robust data governance and cataloging capabilities. Organizations would need to maintain comprehensive metadata, including the origin of each data element, licensing terms, consent status, and any transformations applied during preprocessing. It also implies a commitment to traceability, such that if an issue arises—such as a bias identified in a subset of data or a regulatory concern about a particular dataset—stakeholders can pinpoint its source and understand who was responsible for its use. The practical outcome is an auditable trail that supports accountability and easier incident response.

However, disclosure is not without challenges. Some data sources may be proprietary or sensitive, and full disclosure could raise competitive concerns or risk exposure. The guidelines, therefore, must balance openness with confidentiality by offering structured disclosures that provide meaningful transparency without compromising trade secrets or security. This balance might take the form of tiered disclosures, where critical provenance information is made widely accessible, while highly sensitive details are restricted to necessary parties under appropriate non-disclosure agreements and governance controls. It also requires industry-standard terminology and schemas so that disclosures are interpretable across different platforms and organizations, enabling consistent evaluation by customers and regulators.

In practical terms, the data-source disclosure principle encourages firms to publish a data provenance policy as part of product documentation, to maintain a data catalog that lists sources, and to document any licensing or platform constraints affecting data use. Enterprises can leverage these disclosures to perform due diligence during vendor selection, to assess model risk, and to ensure alignment with internal governance policies. Training programs for developers and data scientists can reinforce the importance of provenance and teach them how to record and communicate data lineage effectively. Overall, the aim is to render the data behind AI both visible and accountable, enabling users to understand what informs the model’s outputs and how those inputs may influence decisions.

Use of private data only with consent and compensation

The second principle addresses private data and the conditions under which it can be used. It emphasizes that private data should be collected, stored, and used only with explicit user consent, and that such use should be accompanied by fair compensation where appropriate. This principle recognizes the value that individuals derive from the data they generate and seeks to ensure that those contributions are acknowledged and remunerated in proportion to their use, especially when private or sensitive information is involved. It also implies a strong preference for data minimization and purpose limitation: data should be used for clearly defined purposes that align with the consent provided, and not repurposed for unrelated tasks without fresh consent.

Operationalizing this principle requires robust consent collection mechanisms, clear opt-in and opt-out pathways, and transparent notices about how data will be used. Consent management platforms may be employed to track user permissions across apps, data sources, and use cases, ensuring that any data processing activities remain within the boundaries of the agreed purposes. Compensation arrangements must be carefully designed to be fair, transparent, and scalable. Compensation could take various forms, including monetary remuneration, access to enhanced services, or reciprocal value through improved privacy controls and user protections. The exact model will likely depend on the context, such as healthcare, finance, or consumer apps, and may be shaped by evolving regulatory frameworks beyond best practices.

Challenges arise in determining what constitutes “private data” and how to balance consent with practical usability. Some types of data—such as anonymized or aggregated data—may still enable de-anonymization in some circumstances, raising concerns about privacy even when personal identifiers are removed. The principle advocates for a cautious approach that errs on the side of user protection and emphasizes post-consent accountability: if data usage shifts beyond the originally stated purposes, fresh consent and possible compensation should be revisited. Additionally, organizations will need to implement robust data governance to track consents over time, handle revocation requests, and manage data retention in alignment with user preferences and regulatory requirements. In this framework, consent is not a one-time checkbox but an ongoing, dynamic process that evolves with product features, data sources, and business models.

The compensation aspect also invites consideration of interdisciplinary collaboration. Legal teams can help define appropriate compensation models within jurisdictional boundaries; product teams can design user-friendly consent flows; ethics and compliance specialists can ensure alignment with societal expectations and industry norms. As AI systems increasingly mediate or influence decisions that affect individuals’ lives, compensation policies may become part of broader debates about data economy fairness, platform liability, and the social value of personal data. The principle thus embeds a principle of reciprocity into AI development, ensuring that private data usage is tethered to user consent and to a fair, transparent acknowledgment of data’s value.

Anonymization and permission for personally identifiable data

The third principle centers on data that remains personally identifiable. It insists on strong anonymization practices and explicit permissions before handling any data that could reveal an individual’s identity. Anonymization is not a one-time technical step; it is part of an ongoing privacy protection strategy that must withstand advanced re-identification techniques and evolving data ecosystems. The guideline thus advocates for privacy-preserving techniques, data minimization, and a cautious approach to PII, particularly in contexts where inference could expose sensitive attributes or reveal patterns unique to individuals.

Implementation requires layering multiple privacy techniques. Traditional pseudonymization and de-identification must be complemented by modern methods such as differential privacy, secure multi-party computation, and federated learning where feasible. The principle also calls for rigorous access controls, threat modeling, and audit mechanisms to ensure that only authorized personnel can access PII and that data flows are monitored for potential exfiltration or misuse. In practice, this could include classifying data by sensitivity, applying appropriate protection levels, and enforcing automated safeguards that prevent data from being used beyond approved purposes or transformed in ways that could re-identify individuals.

Permissions for PII must be explicit and tightly scoped. This means that individuals should be able to view, modify, or revoke permissions for their data, and organizations should implement clear governance around什么时候 data containing PII can be processed, stored, or shared. Moreover, organizations should establish robust data retention policies that minimize the duration for which PII is kept, with automatic deletion when it is no longer necessary for the stated purposes. The principle underscores a proactive stance toward privacy by design, ensuring that PII handling remains a central consideration throughout the AI lifecycle—from data collection to model deployment to ongoing monitoring.

A practical challenge is balancing the needs of enterprise analytics and AI systems with stringent privacy protections. Anonymization and PII permissions can sometimes reduce the richness of data available for training or optimization, potentially impacting model performance. Yet, the framework argues that privacy safeguards should not be an afterthought but a design constraint that guides data engineering choices. Organizations may need to invest in privacy-centric data architectures, including synthetic data generation for certain use cases, and to explore privacy-preserving techniques that enable meaningful insights without exposing individuals’ identities. This principle, therefore, pushes for a disciplined approach to handling sensitive information while still supporting rigorous AI development.

Consent and compensation for copyrighted information

The fourth principle focuses on copyrighted materials and the need for consent and fair compensation when such content informs AI systems. This aspect acknowledges the rights of creators and publishers whose works may be included in training datasets or used in other data-intensive processes. It calls for explicit permissions and appropriate remuneration when copyrighted content contributes to training, validation, or downstream uses of AI models. This principle seeks to address potential IP concerns, reduce the risk of infringement claims, and encourage responsible content sourcing practices.

Implementing consent for copyrighted information requires clear licensing pathways and contract terms that specify how and where content can be used in AI workflows. It also demands transparency about the provenance of copyrighted data, so users and rights holders can verify that usage aligns with licensing terms and compensation agreements. The compensation framework could involve licensing fees, revenue-sharing arrangements, or other equitable arrangements that reflect the value of the copyrighted material embedded in AI outputs. This approach also incentivizes content creators and rights holders to participate in AI-enabled ecosystems, potentially expanding the pool of data sources while ensuring that ownership rights are respected.

The practicalities of this principle include building systems to track licensing status, enforce usage constraints, and manage revocation or renegotiation if terms change. It also calls for collaboration among platform operators, data providers, publishers, and rights holders to establish scalable, standardized licensing models that accommodate the dynamic nature of AI development. In addition, the principle supports the ongoing assessment of outputs to ensure that generated content or decision-making does not infringe on copyrighted works or undermine the value of ownership. By incorporating explicit rights management into the AI lifecycle, the industry can foster a more sustainable creative ecosystem, where innovation and intellectual property coexist in a balanced, enforceable framework.

Together, these four principles create a cohesive framework for responsible AI that emphasizes transparency, consent, privacy, and IP rights. They are designed to be practical, scalable, and adaptable to various industries and regulatory contexts, while providing a clear blueprint for how AI providers can operate with integrity. The guidelines also facilitate better collaboration among data suppliers, enterprises, and users by establishing common expectations around data provenance, consent mechanics, privacy protections, and compensation for rights holders. As AI becomes more embedded in everyday products and services, adopting such a framework could help reduce risk, improve trust, and unlock broader, more sustainable adoption. The next sections explore how this shift—from a data-centric race to a trust-centric ecosystem—could unfold in practice and what that means for the architecture, governance, and business models of enterprise AI.

The next phase of AI: a race for trust, not merely data

The industry’s context is shifting from a traditional emphasis on acquiring vast data troves to a new emphasis on earning and maintaining user trust. Calkins describes the current era as “phase one,” characterized by a relentless push to ingest and leverage as much data as possible. He argues that this approach has been necessary to unlock early breakthroughs but has now reached a plateau where the marginal gains from data accumulation diminish. The coming phase, he contends, will be defined by trust as the primary currency driving AI adoption, development, and value creation. This reframing positions trust as the essential factor enabling more meaningful use of AI, including access to richer data resources under consent and with transparent provenance.

Trust, in this view, is not merely about user satisfaction or brand reputation; it becomes a strategic asset that influences the quantity and quality of data organizations can access and utilize. When users and data providers perceive fairness, privacy protections, and fair compensation within AI systems, they are more likely to participate, share data, and consent to more advanced capabilities. Trust reduces uncertainty about how data is used, who benefits, and how privacy is protected. It also fosters a sense of accountability and shared responsibility among all stakeholders, including developers, platform providers, enterprises, and content creators. In a trust-centered world, enterprises can pursue AI-driven outcomes with greater confidence, knowing that governance mechanisms, policy commitments, and rights protections are designed into the technology’s fabric.

Calkins emphasizes that the transition to a trust-driven model requires a rethinking of incentives, architectures, and operational practices. Rather than treating data as an unbounded resource to be hoarded and exploited, the focus shifts toward building systems that justify trust through verifiable practices: transparent data provenance, consent-driven data usage, privacy-preserving technologies, and clear compensation for content and IP contributions. This shift is not about restricting AI or slowing progress; it is about aligning the pace and direction of development with sustained social acceptance and regulatory alignment. In practice, it means designing AI products that explain their decision-making processes, provide users with straightforward tools to control their data, and ensure that rights holders are fairly compensated when their works contribute to AI capabilities.

The transition to trust also has significant implications for how AI products are architected. It requires more robust governance models, including risk assessment, model monitoring, and ongoing auditing. It calls for architectural patterns that support data lineage, traceability, consent management, and compliance controls. It suggests adopting privacy-enhancing technologies that minimize exposure of sensitive information while preserving analytic utility. It also implies new business models and revenue frameworks that recognize the value of data and content rights, providing incentives for responsible data sharing and licensing. All told, the “race to trust” reframes competitive advantage in AI from a race to accumulate data to a race to demonstrate reliability, fairness, and respect for user autonomy.

Appian’s positioning in this evolving landscape rests on its core strength as a provider of low-code automation platforms. The company contends that its tools allow organizations to rapidly build AI-enabled applications while maintaining strict data privacy and security controls. By embedding responsible AI practices into the platform from the outset—through transparent data provenance, consent management, and privacy-preserving options—Appian envisions delivering more trustworthy AI outcomes at scale. This aligns with the trust-centric vision, suggesting that enterprises seeking to deploy AI at scale without sacrificing governance can find a compelling partner in Appian. The emphasis on responsible development, therefore, could translate into a differentiator in a crowded market where the speed of AI deployment often competes with the need for accountability and rights protection.

Yet the shift toward trust is not without its tensions. Regulators, lawmakers, and the public are increasingly vigilant about how AI systems handle data, how they interpret and apply rules, and how they manage bias and potential misuse. The proposed guidelines respond to these concerns by codifying a framework that makes data provenance, consent, privacy, and IP considerations explicit in product design and business practice. If widely adopted, these principles could set baseline expectations for responsible AI across industries, encouraging interoperability and reducing the risk of regulatory friction. The broad implication is a more predictable and ethically grounded AI ecosystem—one in which enterprises, developers, and users share common language and shared commitments, which in turn can drive more sustainable, long-term value for all participants.

However, as with any transformative shift, adoption will depend on practical feasibility, cost implications, and the willingness of the market to embrace stronger governance regimes. The four principles are designed to be implementable at scale, but real-world deployment will require investment in governance, data infrastructure, and human expertise. For enterprises, this means allocating resources to build capable data catalogs, consent management frameworks, privacy-preserving pipelines, and licensing infrastructures for copyrighted material. It also implies strong vendor management and due diligence to ensure that suppliers and partners adhere to the same standards. The outcome, if the market embraces the approach, could be a more stable, transparent, and trustworthy AI ecosystem—one where the most successful deployments are not only technically advanced but also ethically grounded and rights-respecting.

The broader question remains: will other industry players, regulators, and customers rally behind Appian’s four-principle framework? The answer may hinge on practicality, industry readiness, and the ability to demonstrate clear benefits in real deployments. If the guidelines yield measurable improvements in data governance, user trust, and risk mitigation, they could set a high bar for responsible AI that others strive to meet. In the meantime, the proposed framework provides a concrete direction for how to translate the abstract ideals of responsible AI into actionable policies, architectural choices, and business strategies. As AI evolves, the race to trust will increasingly determine which organizations lead, which adopt more cautious approaches, and which risk falling behind due to governance gaps or stakeholder pushback. The following sections explore how this framework could influence enterprise architecture, competitive dynamics, regulatory considerations, and actionable steps for adoption.

Enterprise architecture and governance in a trust-forward AI world

The shift toward trust as a core AI currency necessitates a reimagining of enterprise architecture and governance. Organizations must embed governance, risk management, and compliance (GRC) into the design, development, and deployment of AI systems, ensuring that transparency, consent, privacy, and IP considerations are not add-ons but foundational pillars. This transformation has several practical implications for how AI systems are built, operated, and evaluated across business units.

First, data provenance must become an enterprise-wide standard. This entails creating comprehensive data lineage maps that track data from its source through every processing step to its final outputs. Proactive data lineage supports accountability by making it possible to answer questions about data quality, source reliability, and potential bias at every stage of a model’s lifecycle. It also supports regulatory compliance and audit readiness. To achieve this, organizations will need to invest in metadata management, standardized schemas, and automation capable of capturing lineage across diverse data systems, including external data sources, partner feeds, and customer data repositories. A robust provenance framework enables faster issue detection, root-cause analysis, and remediation when problems arise.

Second, consent management must be baked into product design and platform capabilities. This includes both initial consent for data usage and ongoing consent management as purposes evolve, data flows change, or new features are introduced. A scalable consent framework supports granular permissions, revocation, and clear notices about how data will be used. It should be integrated with user interfaces, APIs, and data processing pipelines so that updates to consent preferences propagate through the system in real time wherever possible. From an enterprise perspective, consent management also interacts with customer contracts, data-sharing agreements, and vendor compliance programs, making it essential to establish clear ownership, accountability, and enforcement mechanisms.

Third, privacy-preserving technologies should be standard options in AI workflows. Techniques such as differential privacy, homomorphic encryption, secure multi-party computation, and federated learning can reduce the exposure of sensitive data while preserving analytical usefulness. The challenge is selecting the right tool for the right use case and balancing privacy with performance and accuracy. Enterprise teams will need to build a portfolio of privacy-preserving methods, understand their trade-offs, and implement governance around when and how each technique is appropriate. This approach often requires specialized expertise, cross-functional collaboration among data scientists, security engineers, and privacy officers, and ongoing monitoring to ensure that privacy protections remain effective as models and data evolve.

Fourth, model governance and risk management must become more rigorous and continuous. This means formalizing model risk practices to include ongoing monitoring for bias, fairness, drift, and data quality changes. It also entails establishing escalation paths, documentation standards, and independent reviews that can be invoked when governance concerns arise. A mature governance framework supports model versioning, reproducibility, and the ability to explain outputs in human-understandable terms. It also ensures that updates to models—whether due to data shifts, regulatory changes, or new features—are evaluated for safety and compliance before deployment in production.

Fifth, IP and licensing considerations must be integrated into platform design and vendor relationships. Companies should formalize licensing for third-party data and copyrighted material used in training, validation, and inference, including clear terms on scope, duration, compensation, and use limitations. Intellectual property rights management requires close collaboration between legal teams, procurement, and data engineering to ensure compliance and to avoid disputes. It also calls for a transparent approach to content provenance and licensing metadata, enabling customers to understand what content informs AI outputs and whether rights holders have been compensated.

Sixth, organizational alignment and culture come to the fore. A culture that values transparency, accountability, and user-centric design is essential to support the technical changes described above. This includes training and development programs to expand employees’ understanding of data provenance, consent, privacy, and IP considerations, as well as performance metrics that reward responsible AI practices. Leaders must model a commitment to trust, communicate clearly about governance decisions, and foster cross-disciplinary collaboration to ensure that governance is systemic rather than siloed.

Seventh, measurement and auditing become ongoing activities rather than periodic afterthoughts. Organizations should define key performance indicators (KPIs) that reflect governance, privacy, and IP objectives. These could include metrics for data provenance completeness, consent coverage rates, the rate of successful privacy-preserving experiments, compliance incident frequency, and the proportion of AI outputs that can be traced back to licensed data. Regular internal and external audits, along with independent verification of claims about data sources and usage, can help assure stakeholders of continued alignment with the guidelines and provide a framework for continuous improvement.

Eighth, vendor and partner ecosystems need to align with these governance norms. As enterprises source data, tools, and platforms from multiple vendors, those suppliers must be able to demonstrate compliance with the same principles. This may involve standardized data contracts, consent and licensing mechanisms, and interoperability standards that enable governance to scale across different technology stacks. A harmonized ecosystem reduces complexity for customers and protects them from misalignment that could undermine trust. It also accelerates adoption by offering a more predictable, transparent, and compliant set of options for building AI-enabled solutions.

In practice, these architectural and governance changes require a phased approach. Organizations can begin with the most impactful and feasible changes—such as establishing a data provenance policy, building an auditable data catalog, and implementing basic consent management—while incrementally layering more advanced privacy technologies and governance processes. The goal is to create a durable foundation that not only supports current AI initiatives but also scales as models become more complex, data flows expand, and regulatory expectations become stricter. The result is an enterprise capable of delivering AI-enabled outcomes that respect user rights, protect content creators, and maintain robust security and compliance postures—an environment in which innovation can flourish without compromising trust.

Enterprise implications for trust-centered AI deployment

The governance and architectural shifts described above translate into concrete implications for how enterprises plan, build, and operate AI solutions. The focus on data provenance, consent, anonymization, and IP rights shapes the entire lifecycle of AI systems—from initial ideation to ongoing maintenance and improvement. This section outlines practical implications across several dimensions—strategy, product development, risk management, and business models—highlighting how a trust-centered framework can influence decision-making and outcomes.

Strategy and planning

• Trust as a strategic differentiator: Enterprises can position themselves as trusted AI providers by adopting and publicly documenting these four principles. This can become a differentiator in markets where buyers demand transparency, ethics, and rights protection alongside performance.
• Long-term governance planning: Early investment in governance infrastructure (data catalogs, lineage tracing, consent systems) yields compounding benefits as AI initiatives scale. It reduces risk, accelerates due diligence, and improves collaboration with regulators and customers.
• Risk-based prioritization: Projects can be prioritized based on the data governance and IP risk profile. For example, use cases involving sensitive or copyrighted data may require more rigorous consent management and licensing processes, while less sensitive data may proceed with lighter governance.

Product development and deployment

• Built-in transparency: AI products can incorporate automatic disclosures about data sources and licensing terms, providing users with visibility into data provenance and rights.
• Consent-aware workflows: Applications can be designed to capture, manage, and respect user consents in real time, with the ability to revoke or modify permissions as needed.
• Privacy-by-design: Architectural choices should emphasize privacy preservation, data minimization, and robust security to minimize exposure of PII and sensitive data.
• IP-aware training pipelines: Training pipelines can be structured to respect licensing and attribution requirements, with clear traceability from inputs to outputs and inclusive licensing metadata.

Governance, risk, and compliance

• Continuous monitoring: Models should be subject to ongoing monitoring for drift, bias, and unintended consequences, with predefined remediation pathways.
• Documentation and audit readiness: Comprehensive documentation of data provenance, consent, licensing terms, and privacy measures should be maintained to support regulatory inquiries and internal reviews.
• Rights management integration: IP considerations should be embedded in data pipelines and licensing systems, enabling transparent handling of copyrighted content in training data and outputs.

Data operations and security

• Provenance-driven data operations: Data engineers and data scientists will need to integrate provenance capture into ETL processes, feature stores, and model deployment pipelines.
• Consent lifecycle management: Systems must be capable of recording, updating, and honoring user consent across multiple products and services, ensuring consistent data handling.
• Privacy-preserving techniques: Organizations should evaluate and adopt privacy-enhancing technologies where appropriate to balance data utility with privacy protection.

Business models and partnerships

• Licensing-based collaboration: The need for permissions and compensation for copyrighted data opens new collaboration models with publishers and content creators, potentially expanding the data and content ecosystem.
• Value-sharing arrangements: New economic models may incentivize rights holders to contribute data and content to AI ecosystems under fair terms, enabling broader access to high-quality datasets.
• Risk-sharing across the ecosystem: Shared governance commitments among vendors, customers, and regulators can lead to more stable and predictable AI adoption, reducing the likelihood of regulatory friction or reputational damage.

Organizational culture and capabilities

• Cross-functional expertise: Implementing the four principles requires close collaboration among data engineers, data scientists, privacy professionals, legal teams, and product managers.
• Training and awareness: Ongoing education about data provenance, consent, privacy, and IP rights should be integrated into professional development programs to embed responsible AI practices.
• Accountability and governance ownership: Clear ownership for governance processes, including data stewardship, consent management, and IP licensing, ensures that responsible AI remains a core competency of the organization.

The implications above underscore that a trust-forward AI strategy is not simply a compliance exercise but a holistic shift in how AI initiatives are conceived, designed, and delivered. When governance, privacy, and IP considerations are baked into the technology and culture from the outset, enterprises can benefit from clearer risk management, stronger customer trust, and more sustainable innovation. As the AI landscape continues to evolve, those organizations that adopt transparent, rights-respecting, and consent-driven practices are likely to achieve greater resilience, realize higher-quality data for training, and unlock broader enterprise value. The next section explores how this shift might influence the competitive dynamics of the AI market and why Appian could be well-positioned within this transition.

Market positioning: why trust-centered AI can reshape competitive dynamics

The emergence of a trust-centered AI paradigm has significant implications for how firms differentiate themselves in a crowded market. In this context, Appian’s focus on low-code automation, combined with a commitment to responsible AI practices, may offer a compelling value proposition to enterprises seeking faster deployment without sacrificing governance. The broader market implications hinge on several factors: the demand for transparent data usage, the willingness of customers to adopt consent-driven architectures, and the ability of platform providers to deliver scalable, auditable AI capabilities.

Platform-level differentiation

• Speed and governance parity: Low-code platforms that integrate governance controls directly into their development environments can enable faster delivery of AI-powered applications while preserving traceability and consent management. This can reduce the time-to-value for enterprises that must navigate complex data governance requirements.
• Built-in data lineage: Platforms that provide out-of-the-box data provenance support can help customers satisfy regulatory expectations, perform risk assessments, and demonstrate compliance to auditors and customers.
• Privacy-first design: By offering privacy-preserving options as first-class features, platform providers can address privacy concerns proactively, attracting customers who require strict GDPR-like protections or sector-specific privacy regulations.

Customer trust as a competitive moat

• Transparent data sourcing: The ability to disclose data sources within the platform’s workflows can become a standard expectation among enterprise buyers, helping reduce vendor risk and enabling more informed procurement decisions.
• Clear IP licensing workflows: Integrated IP management capabilities can simplify the handling of copyrighted materials in training data and AI outputs, lowering the barrier to licensing and collaboration with content providers.
• Observable compliance: Continuous auditing, reporting, and certification capabilities can reassure customers and regulators that AI systems remain within defined policy boundaries, reducing the risk of regulatory penalties or consumer backlash.

Enterprise adoption dynamics

• Risk mitigation and governance costs: While implementing these principles may require upfront investments in data governance and privacy infrastructure, the long-term risk reduction and compliance resilience can yield a lower total cost of ownership and more stable operations.
• Vendor ecosystem alignment: A market where many vendors embrace the four principles can yield interoperable solutions, enabling customers to mix and match components with confidence, rather than being locked into proprietary, opaque pipelines.
• Ecosystem growth: Content creators and rights holders could participate more readily in AI ecosystems if licensing mechanisms are clear and compensation terms are standardized, broadening the data and content available for responsible AI deployments.

Appian’s positioning within this market shift rests on several factors. First, its core strength in enabling rapid creation of AI-enabled applications aligns with a desire for faster, governance-aware deployments. By integrating the four principles into its product strategy—data provenance, consent-based data usage, anonymization safeguards, and rights-aware data licensing—Appian can demonstrate a practical commitment to responsible AI that translates into credible customer value. Second, the emphasis on low-code can help customers achieve governance at scale. When developers can build AI features with built-in controls and transparent data flows, larger organizations can adopt AI more broadly across lines of business without surrendering oversight. Third, Appian’s focus on responsible AI could position it as a preferred partner in sectors with strict regulatory requirements, such as healthcare, finance, and government services, where transparent data handling and IP rights protections are non-negotiable.

Nevertheless, competition is intense, and many players are pursuing similar themes through their own governance and privacy initiatives. A key differentiator will be the ability to operationalize these principles in practical, scalable ways that integrate seamlessly with existing data ecosystems, across on-premises and cloud environments. Success will hinge on delivering a compelling combination of technical capability, governance depth, and business value—evidenced by clear case studies, measurable improvements in risk posture, and demonstrable ROI from responsible AI deployments. As more enterprises demand accountability and rights protection, the demand for platforms and partners that can deliver these capabilities at scale is likely to grow, creating opportunities for leaders who can translate policy into practical, repeatable solutions.

Industry regulation and public scrutiny will continue to shape competitive dynamics in AI. If the four-principle framework gains traction, it could set a de facto standard that nudges competitors toward similar transparency, consent, privacy, and IP practices. In markets where the regulatory environment is more permissive, organizations may still adopt these principles to differentiate themselves and attract risk-aware buyers. In more stringent markets, the framework could become a prerequisite for participation in major procurement processes, partnerships with public sector entities, and collaborations with content providers. In this context, Appian’s explicit leadership on responsible AI can translate into first-mover advantages, greater credibility with risk-conscious customers, and a reputational edge that translates into market share over time.

The broader implications of a trust-centered AI market extend beyond individual firms. As more players adopt explicit governance standards, there could be spillover effects across the supply chain, encouraging vendors, data providers, and service integrators to align their practices. This alignment can help reduce friction in cross-organizational AI initiatives, making it easier for enterprises to assemble end-to-end solutions that meet stringent governance criteria. The collective move toward transparency, consent, privacy, and IP rights could also influence lending practices, insurance coverage, and regulatory oversight, shaping risk profiles and investment decisions across the AI ecosystem.

In conclusion, a trust-centered AI paradigm has the potential to redefine competitive dynamics by elevating governance and rights protections to strategic priority. Appian’s emphasis on responsible AI aligns with the broader market shift toward transparent data handling and fair use—and, if successfully operationalized, could yield durable competitive advantages for the company and its customers. The subsequent sections explore how the proposed guidelines can be translated into actionable steps for adoption, including a practical implementation roadmap, measurement strategies, and the expected milestones along the way.

Implementation roadmap: turning guidelines into practice

If organizations aim to move from principle to practice, a structured, phased implementation plan is essential. The four guiding principles provide a strong foundation, but turning them into scalable, repeatable processes requires a deliberate approach that integrates policy, technology, and culture. The roadmap below outlines a pragmatic sequence of steps, aligned with typical enterprise rhythms, to help enterprises operationalize disclosure of data sources, consent and compensation for private data, anonymization and PII permissions, and licensing for copyrighted information.

Phase 1: Foundation and governance setup

• Establish governance bodies: Create a cross-functional AI governance committee responsible for policy, compliance, and risk oversight. Include representatives from data engineering, data science, privacy, legal, procurement, and business units to ensure diverse perspectives and accountability.
• Define policy baselines: Develop clear policies for data provenance, consent management, privacy protections, and IP licensing. Establish a standardized vocabulary and data governance framework to be used across the organization.
• Create data catalogs and lineage models: Implement a data catalog with metadata capturing data sources, licenses, privacy classifications, and usage terms. Build data lineage models that trace inputs through processing steps to outputs, enabling traceability and accountability.
• Implement baseline consent workflows: Deploy consent collection and management capabilities that support opt-in, opt-out, purpose limitation, and revocation. Ensure these workflows are accessible across products and services and integrated with data processing pipelines.

Phase 2: Data provenance and disclosure discipline

• Expand data source disclosures: Develop standardized disclosure formats for data sources used in training and inference. Publish data source inventories as part of product documentation and governance reporting.
• Integrate provenance into product lifecycles: Embed provenance tracking into development workflows, release management, and model governance processes. Require provenance evidence as a gating criterion for model deployment.
• Establish supplier and data provider agreements: Align contracts with disclosure requirements and data source transparency expectations. Ensure suppliers agree to provide necessary provenance metadata and licensing information.

Phase 3: Consent and compensation for private data

• Implement end-to-end consent management: Extend consent capabilities to capture user preferences across products and data flows. Ensure consent status propagates through data processing pipelines in real time and supports revocation.
• Design compensation frameworks: Define compensation models for private data usage, including monetary terms or reciprocal value provisions such as enhanced privacy controls. Create transparent mechanisms to track and verify compensation arrangements.
• Enforce purpose limitation: Ensure data usage remains aligned with the stated purposes for which consent was given. Build automated controls to prevent cross-purpose data processing without updated consent terms.

Phase 4: Privacy, anonymization, and PII controls

• Deploy privacy-preserving technologies: Implement differential privacy, secure enclaves, federated learning, or other approaches appropriate to each use case. Balance privacy risk with the utility needs of models and analytics.
• Strengthen PII protections: Enforce strict anonymization and de-identification standards, supported by governance checks and access controls. Establish data minimization strategies to reduce exposure.
• Establish PII permission protocols: Build explicit consent and permission verification for any PII processing, including revocation workflows and auditing capabilities.

Phase 5: Copyright licensing and IP management

• Create licensing and rights management capabilities: Implement standardized licensing flows for copyrighted material used in training data and model outputs. Ensure license terms are reflected in data processing activities and outputs.
• Track licensing metadata: Maintain licensing metadata within data catalogs and lineage records to ensure discoverability and auditable compliance.
• Establish dispute resolution processes: Define mechanisms to address licensing disputes or concerns about IP usage, including escalation paths and remediation steps.

Phase 6: Measurement, audit, and continuous improvement

• Define governance KPIs: Establish metrics to monitor data provenance completeness, consent coverage, IP licensing compliance, and privacy risk indicators.
• Build ongoing audit programs: Schedule internal and external audits to verify adherence to policies and to verify the accuracy and completeness of provenance, consent, and licensing data.
• Use feedback loops: Create mechanisms to collect stakeholder feedback, including customers, data providers, and rights holders, and use it to refine policies and technical implementations.

Phase 7: Scale and ecosystem alignment

• Scale governance to the enterprise: Extend the governance framework to new products, data sources, and markets. Maintain consistency while allowing for domain-specific adaptations as needed.
• Align with partners and suppliers: Extend the implementation approach to key vendors and service providers, ensuring their practices align with the same standards and governance requirements.
• Invest in training and culture: Provide ongoing education and training on responsible AI practices, data governance, and IP rights to ensure sustained adoption and adherence.

Housekeeping and risk management considerations

• Privacy by design discipline: Continuously embed privacy protections into product design and system architecture. Treat privacy as a first-class criterion throughout development and deployment.
• Incident response readiness: Develop and test incident response playbooks for data breaches, IP rights disputes, or consent-related incidents. Ensure rapid containment, remediation, and communication capabilities.
• Regulatory alignment: Stay attuned to evolving regulations and standards across jurisdictions. Update policies and controls to reflect new requirements, and maintain a flexible architecture capable of adapting to shifting expectations.

Operationalizing the roadmap requires disciplined execution, clear ownership, and measurable progress. The four guiding principles offer a stable framework, but their success depends on the organization’s ability to translate policy into technical capabilities and everyday practices. The next section considers practical outcomes, potential challenges, and how organizations can track success as they move along this journey toward trust-centered AI.

Practical outcomes, challenges, and success measures

Implementing a trust-centered AI framework is a multi-year effort with tangible benefits and real-world hurdles. This section outlines expected outcomes, potential obstacles, and how to measure progress across the journey from principle to practice. It also provides guidance on how to communicate progress to stakeholders, including customers, partners, and regulators.

Expected outcomes

• Increased transparency: Organizations will be able to present clearer, auditable data provenance, licensing metadata, and consent histories, enhancing customer confidence and facilitating regulatory compliance.
• Improved risk management: Strong governance reduces the likelihood of data misuse, IP disputes, and privacy violations, translating into lower incident rates and less exposure to regulatory penalties.
• More sustainable data ecosystems: Clear consent and compensation frameworks encourage participants to contribute data and content under fair terms, expanding the pool of high-quality data while respecting creators’ rights.
• Accelerated adoption with governance confidence: Enterprises can pursue AI deployments at scale with a known governance baseline, shortening procurement cycles and reducing project risk.

Key challenges and mitigating strategies

• Complexity and cost: Implementing end-to-end provenance, consent, and licensing can be technically and organizationally complex. Start with high-impact, low-friction areas and progressively extend coverage, leveraging automation and standardized templates to reduce costs.
• Data quality and interoperability: Maintaining high-quality provenance data and consistent licensing metadata across diverse sources can be difficult. Invest in data stewardship programs and adopt common data schemas to improve interoperability.
• Balancing privacy with analytics: Privacy-preserving techniques may introduce trade-offs in model performance. Use a mix of approaches and tailor privacy methods to the specific use case to optimize both privacy and utility.
• Regulatory fragmentation: Laws and standards vary across regions. Develop modular governance that can adapt to different regulatory requirements while maintaining a consistent core framework.
• Partner alignment: Ensuring suppliers and partners comply with the same standards can be challenging. Establish clear contractual requirements, audits, and incentive structures to promote alignment.

Key metrics and indicators

• Data provenance coverage: Percentage of training and inference data with documented provenance, including source, license, and governance context.
• Consent utilization rates: Proportion of data processing activities governed by active, verifiable user consent, with real-time propagation through pipelines.
• Anonymization effectiveness: Measures of privacy risk remaining after anonymization and privacy-preserving techniques, assessed through independent reviews.
• IP licensing compliance: Share of data and content used in training that is licensed or otherwise rights-cleared, tracked via licensing metadata.
• Incident frequency and remediation speed: Number of governance incidents and the average time to detect, respond, and remediate.
• User trust indicators: Customer satisfaction, perceived transparency scores, and consent-revocation rates as proxies for trust.

Communication and stakeholder engagement

• Transparent stakeholder reporting: Publish governance reports and anonymized case studies demonstrating how provenance, consent, privacy, and IP considerations are implemented and monitored in real-world deployments.
• Education and training: Offer ongoing learning opportunities for engineers, product managers, and executives to deepen understanding of responsible AI practices, governance requirements, and rights management.
• Collaboration with regulators and industry groups: Engage in constructive dialogue with policymakers, standards bodies, and industry peers to shape practical, scalable governance norms.

As enterprises progress through these phases, they can expect to realize increasing alignment between AI capabilities and societal expectations. The journey requires commitment, resources, and sustained leadership, but the potential payoff is a more trustworthy AI ecosystem that can deliver substantial business value while reducing risk and building enduring trust with customers and partners. The final section synthesizes these insights and reflects on the broader implications for the AI industry and Appian’s role in shaping this trajectory.

Conclusion

The initiative led by Matt Calkins to articulate a four-principle framework for responsible AI—disclosure of data sources, consent and compensation for private data, anonymization with protection for PII, and consent and compensation for copyrighted information—offers a concrete, scalable path for moving AI from rapid experimentation to trustworthy, enterprise-ready implementations. By reframing the next phase of AI as a race to trust rather than a race to acquire more data, the proposal highlights a fundamental shift in how value is generated and sustained in AI-enabled businesses. Trust becomes the central asset that unlocks broader data access, better user engagement, and more durable partnerships with rights holders and content creators, all while supporting regulatory compliance and ethical standards.

Appian positions itself at the forefront of this transition by coupling its low-code platform with a principled approach to responsible AI. The proposed guidelines are not merely theoretical; they outline a practical blueprint for implementing governance, data provenance, consent management, privacy-preserving techniques, and IP licensing across AI initiatives. If adopted widely, these practices could set a new benchmark for how AI is built, deployed, and governed—one where enterprises can move faster without compromising user rights or societal values. The journey toward trust-centered AI will involve architectural changes, cultural shifts, and ongoing collaboration among developers, business leaders, rights holders, and regulators. The potential rewards—a more transparent, fair, and resilient AI economy—could redefine competitive advantage in the coming era of intelligent technology.

As the industry navigates these reforms, the central question will be whether other players quickly align with this trust-focused approach and whether regulators and customers reward transparency and accountability with faster adoption and deeper partnership. If the momentum behind these four principles persists, the AI landscape could evolve into a more predictable, ethical, and productive environment—one in which the most successful implementations are not only the most capable but also the most trustworthy. The next chapters of AI development, informed by data provenance, consent, privacy, and rights, will reveal how trust shapes technology’s ultimate impact on business, society, and everyday life.