How can businesses build AI-driven innovation based on trust?

To embed AI sustainably and responsibly, organizations must enhance their existing governance structures with a set of focused building blocks for responsible AI. We have distilled these into a practical guide designed to elevate corporate AI governance frameworks while prioritizing sustainability and responsibility. These building blocks are organized into three mutually reinforcing layers: strategy, organization and processes.

1. Start with strategy: set the north star and the guard rails

AI strategy

Successful AI integration begins with firmly anchoring each initiative to the organization’s strategic objectives, core values and risk appetite. This foundational step involves articulating a clear vision that defines the purpose and direction of all AI efforts, ensuring that they align with the organization’s broader goals. By establishing this clarity, organizations can effectively guide their AI initiatives toward meaningful and impactful outcomes.

Drivers of the AI journey / thought leadership group

To cultivate a thriving AI landscape, organizations must harness strategic foresight that anticipates emerging trends and potential risks. This forward-looking perspective not only fuels innovation but also ensures that senior leadership remains aligned on the direction and responsible adoption of AI initiatives. By fostering a shared executive vision, organizations can transform complex implementation challenges into coordinated, value-driven opportunities that maximize the benefits of AI.

AI governance model

Clear decision rights and ownership form the backbone of responsible AI governance. By proactively mapping roles, escalation paths and governance forums, organizations establish a transparent framework that guides all stakeholders. This structured approach fosters seamless cross-functional collaboration, enhances accountability and builds greater trust in each AI initiative – from conception to production. As a result, organizations can navigate the complexities of AI implementation with confidence and integrity.

Governance and oversight

a. Oversight and accountability (RACI, boards, committees)
Effective AI governance begins with clearly defined ownership. Establish who is responsible, accountable, consulted and informed (RACI) at every stage of the model lifecycle, and embed these assignments within the charters of dedicated AI boards and committees. By creating crystal-clear escalation paths, organizations ensure that human oversight is both traceable and defensible, reinforcing accountability throughout the process.

b. AI Ethics Board / external advisory group
Introduce an independent layer of scrutiny by forming an AI Ethics Board or engaging external advisory groups. These specialists provide valuable insights on high-risk or socially sensitive use cases, helping organizations navigate ethical grey areas and enhance public trust in their AI initiatives.

c. Compliance and ethics including regulatory alignment
It is essential to align all AI activities with relevant regulations, internal policies and societal expectations. Organizations should systematically address issues such as bias, transparency, sustainability and auditability, ensuring that every deployment can stand up to regulatory review and stakeholder scrutiny.

d. AI policy including code of conduct
Establish a comprehensive AI policy that aligns with the corporate code of conduct, clearly outlining the “rules of the road.” The policy should specify permissible data use, mandatory controls and individual responsibilities. A well-communicated policy not only deters misuse but also fosters a culture of accountability across the organization, promoting responsible AI practices at all levels.

Benefit realization

Maximizing the return on AI requires more than just optimistic business cases; it necessitates a disciplined ROI governance framework. Begin by securing upfront buy-in from key stakeholders and then track a balanced set of key performance indicators (KPIs) that reflect both value creation – such as revenue growth, cost efficiency and customer experience scores – and risk exposure, including model drift incidents and compliance findings.

Regularly review these metrics at each stage of the AI lifecycle – during the ideation, pilot, scaling and operational phases – to make informed decisions about redirecting funding or recalibrating models as necessary. This proactive, data-driven approach ensures that investment remains aligned with strategic goals, enhances accountability and guarantees that AI initiatives deliver sustainable growth rather than devolving into unchecked experimentation.

2. Equip the organization: people, lines of defense and tooling

To fully unlock AI’s potential, organizations must go beyond simply deploying advanced algorithms; they must empower their teams with the right tools, skills and safeguards. This involves integrating robust technical controls with a culture that prioritizes ethics and fosters stakeholder trust throughout every phase of implementation.

Risk and security management

a. AI risk management
Effective AI risk management involves the identification, assessment, prioritization and mitigation of AI-specific risks. By systematically addressing these risks, organizations can enhance the reliability and fairness of their AI systems, ultimately fostering greater trust among stakeholders.

b. AI security and threat protection
To protect AI systems from both internal and external threats, organizations must implement robust security measures. This includes deploying technical controls, enhancing cybersecurity protocols and ensuring preparedness for potential incidents. A comprehensive security strategy not only safeguards AI assets but also reinforces the integrity of the entire organization.

c. Third-party risk and vendor management
Organizations must apply consistent risk management and ethical standards in their external AI engagements, spanning procurement to operational use. By establishing clear guidelines and monitoring practices of third-party vendors, organizations can mitigate risks associated with outsourcing AI functions and ensure that ethical considerations are upheld throughout the engagement process.

Organizational enablement

a. Transforming AI from a pilot initiative to a pervasive force within an organization requires a culture that is literate, inclusive and ethically grounded. To enhance AI literacy, organizations should implement role-specific learning paths. Embedding change management is equally crucial. Organizations should integrate ethical checkpoints, incorporate diversity, equity and inclusion (DE&I) principles into model design and involve employees and customers in governance forums. This way, responsible AI norms become part of the daily workflows. Finally, measuring the cultural shift is essential. Organizations should track indicators such as training completion rates, ethics escalation trends and stakeholder trust scores.

b. Effective IT change management is crucial to maintain the integrity of AI systems. Teams must assess, authorize, implement and review changes in a controlled and coordinated manner. This structured approach minimizes disruption and ensures alignment with risk management, compliance requirements and business objectives. In tandem, robust data governance and quality management are essential for safeguarding AI data throughout its lifecycle. Organizations should ensure that data is secure, private, traceable and suitable for use. By prioritizing these practices, organizations can enhance the reliability of their AI systems and build trust among stakeholders, ultimately driving better business outcomes.

c. In tandem, robust data governance and quality management are essential for safeguarding AI data through its lifecycle. Organizations should ensure that data is secure, private, traceable and suitable for use. By prioritizing these practices, organizations can enhance the reliability of their AI systems and build trust among stakeholders, ultimately driving better business outcomes.

3. Operationalize through replicable processes

In today's rapidly evolving technological landscape, effective operational execution and lifecycle management are essential for the successful deployment and maintenance of AI systems.

Operational execution / lifecycle management

a. AI inventory
Organizations must maintain a comprehensive and up-to-date inventory of all AI systems, detailing their purpose, risk classification and ownership. For high-risk systems, this inventory should also include associated conformity assessments, documentation and logging requirements.

b. AI planning and design
Governance in AI planning is essential, guided by thorough impact and risk assessments, risk-based design principles, stakeholder alignment and structured evaluations of use cases. This proactive approach ensures that AI initiatives are aligned with organizational goals and risk tolerance.

c. AI development and testing
During the development and testing phases, organizations should focus on building and validating AI systems with documented designs that prioritize fairness, privacy and risk mitigation before deployment. This diligence helps to ensure that AI systems are robust and compliant with ethical standards.

d. AI deployment and controls
When it comes to deployment, it is vital to ensure a secure, transparent and controlled process. This includes oversight, necessary sign-offs and post-deployment safeguards to monitor the system’s performance and compliance.

e. Monitoring and performance tracking
Continuous monitoring is crucial for maintaining the integrity of AI systems. Organizations should utilize key performance indicators (KPIs), user feedback and risk indicators to detect issues such as bias, drift and performance degradation. Maintaining explainability and interpretability throughout this process enables responsible decision-making regarding system decommissioning when necessary.

f. AI incident management and business continuity management
Proactive AI incident management and business continuity planning are essential for organizational resilience. The way in which an organization prepares for, responds to and recovers from AI-related incidents and crises can be the factor that determines whether it is able to maintain trust and ensure operational continuity.

g. AI controls assurance & RCSA
To uphold the effectiveness of AI systems, organizations should periodically validate control effectiveness, fairness and bias mitigation through assessments and self-checks. This ongoing assurance process helps to identify areas for improvement.

h. Improvement loop
Finally, it is vital to institutionalize learning and improvement mechanisms for AI systems, models and controls over time. This improvement loop fosters a culture of continuous enhancement, ensuring that AI initiatives evolve in line with best practices and organizational objectives.

AI is not yet ingrained in the DNA of every organization. Before advancing further along the innovation curve, leaders must consider: Is our current governance structure equipped to handle the level of AI we are currently utilizing, and can it scale effectively without producing unnecessary complexity?

We have developed an AI Lifecycle Blueprint that provides clarity on this critical question. It outlines each phase of an AI initiative, defining objectives, activities and key stakeholders to enable establishing the right level of governance to support today’s use cases while allowing for future growth and innovation.

The AI Lifecycle Blueprint: objectives, activities and key stakeholders

1. Plan

During the planning phase, the primary objective is to establish a strong foundation for the project. This involves setting clear objectives, engaging key stakeholders and assessing both technical and data feasibility. Additionally, it is essential to identify potential risks and outline corresponding mitigation strategies, as well as to develop AI-specific governance and communication plans.

To achieve this, collaboration among business functions, risk and compliance experts and data/AI teams should begin from the outset.

2. Design

In this phase teams work to define the target architecture, map the workflows, prepare test data, select and document the AI model and develop risk mitigation measures. Privacy, security and explainability-by-design principles are integrated from the very beginning, while UX guidelines inform the creation of intuitive user interfaces. Success in this phase relies on close collaboration among IT change management, security and compliance, data and AI teams and relevant business functions.

3. Build

The building phase focuses on implementing and configuring the necessary infrastructure and system components. This includes developing system functionalities and checks, setting up and training the selected AI model and integrating security measures and risk mitigation strategies to ensure the model’s robustness. Additionally, this phase establishes continuous integration/continuous deployment (CI/CD) pipelines to facilitate efficient development and deployment processes. In this phase, key stakeholders include IT change management, security and compliance teams, data and AI specialists and relevant business functions.

4. Test

During the testing phase, it is essential to validate the system requirements and the security of the AI model. This validation process also extends to assessing the model’s performance and fairness. These steps ensure that the model meets standards for explainability, security and privacy. Additionally, the findings of the validation and assessment must be thoroughly documented, as a basis for informed go/no-go decisions. IT change management, data and AI teams, risk and compliance experts as well as the relevant business functions are involved in this phase.

5. Deploy

In this phase, secure deployment of the system is achieved by implementing robust monitoring and oversight measures. This includes establishing a formalized deployment plan, developing fallback strategies for AI and ensuring that monitoring, traceability and logging mechanisms are in place. Additionally, compliance measures must be implemented, and a final security review is essential to confirm that all protocols are met and that the AI model operates securely and effectively. Here IT operations, data and AI teams, security and compliance experts as well as the relevant business functions are involved.

6. Monitor, improve and maintain

The final phase focuses on the continuous monitoring and enhancement of the AI system’s performance, stability and drift. In the event of an incident, the organization must respond promptly and implement necessary improvement measures. Additionally, compliance must be maintained through regular reviews, and user support training should be provided to ensure effective utilization of the system. Moreover, fostering adoption of AI is crucial, and conducting bias audits will help identify and mitigate any unintended biases in the system. The key stakeholders that must be involved in the last phase are IT operations, data and AI teams, security and compliance experts, the relevant business functions and AI governance and ethics teams.