As organizations progress their artificial intelligence (AI) initiatives, many are grappling with how to translate early successes into long-term results. Bringing AI to scale across the enterprise requires balancing the need for speed and efficiency with responsible practices. Without a holistic strategy aligning with organizational values and objectives, your first sparks of AI innovation could fizzle before they've caught fire.
In our recent position paper, "AI without fear or favor," CGI lays out a practical, human-centered approach to achieving long-term success with AI through four imperatives for action: envision, experiment, engineer and expand. In the first three installments of our follow-up series, we discussed envisioning your AI-enabled future with a clear strategy aligned with business priorities, experimenting with ROI-led AI use cases sourced from across the enterprise and engineering AI solutions grounded in responsible practices.
The final piece: expanding and operationalizing AI at scale.
Drive early success, then re-invest
Like all large-scale digital transformation projects, building towards an enterprise-scale AI capability is an iterative process that will gain momentum as it begins to show success. If you're seeing results with some early AI models, make sure you're directing their return on investment (ROI) into further AI innovation and expansion efforts. By doing so, you'll create a healthy pipeline of AI projects and gain buy-in and support as more users begin to feel their impact.
Early AI successes play another critical role in your scaling strategy: They often provide reusable processes, technical setups and architecture that can be applied to different data. As you push new AI models into production, look for opportunities to drive efficiency and economies of scale by repeating what works (and jettisoning what doesn't).
Plan for data updates and model retraining
AI models are only as good as the data they're trained on – and as your AI models move from the experimentation phase to real-world application, the need for high-quality, up-to-date data is imperative. Ensure you have a plan in place to monitor data quality and determine when it's time to update or retrain your AI models.
For more on CGI's approach to a data management strategy, see our viewpoint, "Is your data ready for the AI revolution?"
Be ready to pivot
You may also need to evolve your AI models' functionality as new use cases emerge. This will require an agile approach to AI development, where teams can iterate on existing models and experiment with new techniques and algorithms.
At CGI, we've helped clients adopt a multidisciplinary "pod" mentality, where cross-functional teams collaborate within a framework of continuous feedback loops. These pods can quickly pivot between AI workstreams as necessary, responding to evolving requirements and insights. For example, a pod may operate within short two-to-three-week cycles, focusing on a specific AI initiative. They experiment with various approaches, gather feedback, and assess effectiveness, sharing their results along the way. If the AI model proves ineffective or if new opportunities arise, the pod can swiftly shift its focus to the next priority.
Automate to accelerate
Over time, your organization's scale efforts may result in tens or even hundreds of models in production. At this point, automation will be essential to streamlining formerly manual processes such as testing, deployment and monitoring. By automating routine tasks, teams can redirect their efforts towards more impactful innovation and research and development work.
At this stage, it's wise to take advantage of advancements that can help reduce operating costs. These specialized hardware components are designed to efficiently process language-related tasks while consuming less energy than traditional processing units. By incorporating LPUs into your AI infrastructure, you can achieve substantial energy savings over time – a win for your electricity bill and the environment.
Case in point: CGI PulseAI supercharges automation
CGI PulseAI is a hyper-automation platform that enables clients to drive efficiencies and reduce costs through intelligent process automation (IPA). It uses AI and machine learning (ML) methods to deliver high-performance IPA while easily integrating with existing systems. A core service within CGI PulseAI is the Operations Platform, where users can move AI models from training to deployment and manage them through an end-to-end operations platform. Learn more.
Set sail toward bright horizons
Whether your organization is just getting started in AI or is already beginning to see results, now's the time to strategize for long-term success. And as with any significant journey, it all starts with the proper preparation. If you've envisioned a clear, AI-enabled future, experimented with ROI-led use cases, and engineered responsible solutions, you're poised to expand on your early successes and spark an AI revolution that transforms your organization's operations in the digital age.
Ready to talk about how CGI can help you bring AI to scale at your organization? Get in touch.
