As the artificial intelligence (AI) revolution shifts into high gear, organizations are racing to develop cutting-edge AI models to revolutionize their operations and drive growth. But even the coolest AI projects can crumble without a firm operational foundation to support them. Discover the trade secrets for engineering responsible AI strategies from the ground up.
What is CGI’s experience with creating AI governance frameworks?
In a recent position paper, "AI without fear or favor," CGI lays out a practical, human-centered approach that enables organizations to embrace AI responsibly through four imperatives for action: envision, experiment, engineer and expand. In the first two installments of our follow-up series, we discussed envisioning your AI-enabled future with a clear strategy aligned with business priorities and experimenting with ROI-led AI use cases sourced from across the enterprise. Now it’s time to discuss engineering an AI governance framework.
Why is an AI governance framework a foundational necessity?
The Burj Khalifa in Dubai is the world's tallest building, impressive not only for its striking silhouette but for what lies underneath it. Engineers addressed the challenge of building a massive structure on Dubai's loose, sandy soil by driving nearly 200 concrete pylons deep into the ground. By transferring the structure's weight to the harder subsoil and bedrock layers, they created a strong yet flexible foundation that keeps the Burj Khalifa stable and on terra firma even as desert sands shift around it.
Similarly, your organization's AI strategy should rest on a firm yet flexible foundation of rigorous AI governance frameworks, robust data pipelines, thoughtful change management and other key enablers. By grounding AI projects in responsible practices, organizations realize faster ROI, mitigate risks, drive more sustainable transformation and better weather the shifting sands of digital disruption, market dynamics and evolving customer expectations.
How do AI governance models build trust?
A comprehensive AI governance model is essential to the success of any AI initiative. Robust, transparent processes and policies help build trust; they also help minimize risk by addressing key concerns around data security, privacy, legal and compliance standards and the ethical use of artificial intelligence.
When CGI works with a client to develop an AI governance model, we rely on our Responsible Use of AI framework to help organizations establish a rigorous vetting process for moving their AI experiments into production. This can involve submitting use cases that articulate the intended application, potential risks, mitigation strategies and any required training. After multiple success stories, one thing is clear: our approach to AI governance is a meticulous but necessary process that paves the way for responsible deployment of AI capabilities across the enterprise.
3 guardrails of CGI’s Responsible Use of AI:
- Robustness
- Trustworthiness
- Ethics
AI governance framework guardrail #1: Robustness
Having robustness as a core AI governance guardrail ensures that artificial intelligence systems will perform reliably and consistently in the face of unexpected inputs or malicious attacks. In healthcare or finance fields, for example, a robust AI governance model can be the secret weapon in reducing high-stakes risk, preventing dangerous errors from harming patients and clients.
Key principles of robustness in AI governance frameworks:
- Reliability and safety
- Privacy and security
- Legal and regulatory compliance
AI governance framework guardrail #2: Trustworthiness
Prioritizing trustworthiness when building an AI governance model gives peace of mind to everyone involved in the process, whether that’s team members, customers or stakeholders. Bridging the gap between technological ability and accountable deployment is crucial to maintaining a good reputation.
Key principles of trustworthiness in AI governance frameworks:
- Explainability and interpretability
- Accountability
- Transparency
AI governance framework guardrail #3: Ethics
As AI data becomes the norm, responsible use of AI is regularly called into question. Centering ethical AI when creating AI governance models ensures a human-centered design and prevents bias from discriminating against specific groups of users.
Key principles of ethics in AI governance frameworks:
- Human values alignment
- Fairness and inclusiveness
- Beneficence and sustainability
Establishing an AI “command center”
As AI use cases multiply, centralized coordination becomes critical for driving effectiveness and economies of scale. This is where an AI Center of Excellence (CoE) can provide immense value as the hub for holistic AI strategy, operations and capability building.
CGI helps clients establish, staff and operate their AI CoE, including overseeing AI policy and governance, the AI use case innovation portfolio, AI operating model and more. By consolidating AI strategy, innovation, operations and capability-building under one roof, an effective AI CoE can become a powerful engine that accelerates AI transformation at scale.
Why is refining your AI data strategy a crucial step to governance?
Reliable data is the lifeblood of every successful AI governance initiative. And as an organization's AI strategy expands in scope, so must its data management practices. After all, if the goal is to enable more people across the organization to rely on AI to make real-world business decisions, old and incomplete data won't cut it.
Prioritize practices that minimize the risk of data drift or situations where an AI governance model's input data statistically diverges over time from the data on which it was trained. Left unchecked, data drift can lead to degradation in performance – which, in turn, erodes trust and usefulness. Practices that can mitigate this risk include statistical monitoring, retraining models at appropriate intervals and maintaining an audit trail that tracks data sources, processing steps and model versions over time.
For more on CGI's approach to data management, see our viewpoint paper "Is your data ready for the AI revolution?"
Do you need to invest in change management for your AI strategy?
Operationalizing AI is as much about culture change as technological change. Even the most dazzling AI governance models will fall flat in the face of organizational resistance, lack of user buy-in or skills deficiencies.
Too often, organizations see change management as an afterthought. At CGI, we advocate a more proactive approach that factors it in from the very beginning. Our business consultants work with clients to design and deliver comprehensive change management strategies that prepare end users for what's to come, build their skills, restructure workflows and address concerns around job impacts. These efforts can sometimes include training programs, documentation and more.
Post-deployment, we use communication campaigns, coaching sessions and employee networks to help drive widespread adoption. But no matter the tactics, the goal remains consistent: preparing and supporting employees as they embrace new ways of working with AI.
What’s the best approach for introducing AI governance to an organization?
Ultimately, realizing AI's transformative potential requires a holistic approach. This involves engineering solutions for real-world deployments, establishing a governance model, forming an AI CoE, ensuring data readiness and implementing a comprehensive change management program. While these may not be the most glamorous aspects of an AI strategy, they are essential for success. By building a solid foundation, organizations can accelerate the realization of AI benefits faster and advance their objectives.
Engineer your AI-enabled future with CGI
Ready to talk about how CGI can help you engineer AI that sticks? Connect with our AI experts. Read the final installment of our "AI Without Fear or Favor" series to learn more about expanding on early success to deliver AI at scale.
