In the third AI imperative of our 4 E’s framework, we look at engineer. Engineering through responsible AI solutions from the ground up.
We advocate a practical, human-centred approach that tempers the hype and enables organisations to confidently embrace AI and deliver expected value. We believe the organisations best positioned for success adopt four imperatives for action: envision, explore, engineer and expand. This series delves further into each imperative. In this article we look at engineer. Engineering through responsible AI solutions from the ground up.
Engineering AI solutions that stand tall
AI solutions make the biggest impact when built on a solid foundation of governance, data and change management strategies.
As the artificial intelligence (AI) revolution shifts into the next gear, organisations are racing to develop cutting-edge AI models to revolutionise their operations and drive growth. But even the coolest AI projects can crumble without a firm operational foundation to support them.
Build on bedrock
Your organisation’s AI strategy should rest on a firm yet flexible foundation of rigorous governance, robust data pipelines, thoughtful change management and other key enablers. By grounding AI projects in responsible practices, organisations will realise faster ROI, mitigate risks, drive more sustainable transformation, and better weather the shifting sands of digital disruption, market dynamics and evolving customer expectations.
Embed trust through an AI governance model
A comprehensive governance model is essential to the success of any AI initiative. Robust, transparent processes and policies help build trust; they also help minimise risk by addressing key concerns around data security, privacy, legal and compliance standards, and ethical use of AI.
We work with clients to develop AI governance and operating models using our Responsible Use of AI framework, which lays out three key guardrails and nine supporting principles:
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Guardrail: Robustness
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Guardrail: Trustworthiness
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Guardrail: Ethics
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Reliability and safety
Privacy and security
Legal and regulatory compliance
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Explainability and interpretability
Accountability
Transparency
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Human values alignment
Fairness and inclusiveness
Beneficence and sustainability
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Responsibility
Using this framework, organisations can 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.
It’s a meticulous but necessary process that paves the way for responsible deployment of AI capabilities across the enterprise.
Establish an AI ‘command centre’
As AI use cases multiply, centralised coordination becomes critical for driving effectiveness and economies of scale. This is where an AI Centre of Excellence (CoE) can provide immense value as the hub for holistic AI strategy, operations and capability building.
We help 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.
Shore up your data strategy
Reliable data is the lifeblood of every successful AI initiative. And as an organisation’s AI strategy expands in scope, so must its data management practices. After all, if the goal is to enable more people across the organisation to rely on AI to make real-world business decisions, old and incomplete data won’t cut it.
Prioritise practices that minimise the risk of data drift or situations where an AI 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.
Invest in change management
Operationalising AI is as much about culture change as technological change. Even the most dazzling AI models will fall flat in the face of organisational resistance, lack of user buy-in, or skills deficiencies.
Too often, organisations see change management as an afterthought. 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 often include training programs, documentation and more. Post-deployment, we use communication campaigns, coaching sessions and employee networks for sharing best practice and to help drive widespread adoption.
No matter the tactics, the goal remains consistent: preparing and supporting employees as they embrace new ways of working with AI.
Engineer your AI-enabled future
Ultimately, realising AI's transformative potential requires a holistic approach. This involves five essential steps for success:
- engineering solutions for real-world deployments
- establishing a governance model
- forming an AI CoE
- ensuring data readiness
- implementing a comprehensive change management program.
By building a solid foundation, organisations can accelerate the realisation of AI benefits and advance their objectives.
Ready to find out how CGI can help?
Reach out to our AI experts for more information, or continue our 4 E’s series by reading our envision, explore and expand articles.
