In today’s data and AI driven world, enterprises across industries are utilizing AI to invent new business models, reimagine business and achieve efficiency in operations. However, enterprises may face challenges like flawed or biased AI decisions, sensitive data breaches and rising regulatory risks, if not controlled and governed. To mitigate these risks, enterprises must embrace robust principles of responsible AI and embed them across all layers of the AI ecosystem — data, model and usage.
- Data: Ensure data used for training AI is accurate, representative, and free from biases, with strong privacy and security controls.
- AI Models: Controls for accuracy, relevance, explainability and fairness along with security measures for trusted reliable AI models.
- Usage and Consumption: Monitor AI initiatives continuously and apply moderation layers to ensure ethical and compliant outputs, maintaining trust in AI solutions.
Enterprises must establish controls across the three layers based on:
- Trust: Trust policy and guardrails to make AI explainable, traceable, accurate, reproducible and accountable
- Ethics: Ethics policy and procedures to ensure AI initiatives are fair, free of biases and are protecting fundamental human rights
- Privacy: Ensure privacy remains at the center of all initiatives, preserving privacy of individuals
- Compliance: Ensure lawful and auditable AI initiatives
- Security: Robust and secure data and AI ecosystems
On these fundamentals, enterprises must take the first three concrete steps to ensure robust governance over AI and data initiatives:
Step 1: Outline an organization-wide AI Governance Strategy
Enterprise's leadership should clearly outline the organization's strategy, vision and mission to ensure that responsible data and AI consumption is the primary focus of all individuals idealizing, developing and consuming data and AI.
Enterprise should define comprehensive policies and procedures at enterprise level around the five principles, actively govern the initiatives and educate its employees.
Step 2: Establish Operating Model
Subsequently, the next area of focus should be the people, processes and technology involved in these initiatives.
- Enterprises must set up a Governance CoE, identify all the personas responsible along with clear roles and responsibilities assigned to different personas.
- Enterprises must set up robust framework to govern the data and AI initiatives along with monitoring to ensure governance and compliance with the evolving regulations.
- Enterprises must modernize their tools and technologies to manage and govern the initiatives. (e.g. tools for assessment, controls implementation, audit and monitoring)
Step 3: Operationalize the data and AI Governance Framework
Enterprises at this stage will be ready to govern each and every data and AI initiative by design.
- Enterprises should start with documenting the AI use cases with governance related fingerprints.
- Next, enterprises must prioritize the use cases and assess the risk for each of the use cases to enforce appropriate control for governance.
These three steps help enterprises to ensure responsible and sustainable data and AI initiatives, leading to better brand value and customer satisfaction.
