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This article was provided by Steve Escarvage, Senior VP and Booz Allen’s Analytics Practice and AI Services business leader.
Today, almost any organization can prove the potential of AI in a non-manufacturing, innovation setting – but fielding AI in a real-world laboratory environment is the true test of success. This is the structure to close the gap.
National security is rapidly becoming a digital enterprise – and requires constant advances in Artificial Intelligence (AI) to win the battlefield of the digital future.
But right now, many AI applications are stuck in the lab at the conceptual stage, and have very little access to the “field” (i.e., the production environment with real workloads, users, and problems). This gap is dangerous because AI improves through operationalization, learning how to work faster and better from real world data.
The global AI race is accelerating, and if we don’t invest in measuring AI more effectively now, we risk falling behind. That’s why now is the time to get AI out of “pilot cleansing” and put it into practice.
What is the role of AIOps in measuring AI?
For AI to function successfully, we need to think about adoption and deployment from a holistic and enterprise perspective.
With our experience supporting more than 150 federal AI projects, we have created the AI Operations (AIops) engineering framework that focuses on the critical elements needed to overcome the post-pilot challenges of responsibly integrating AI. AIops enable AI development and sustainability by bringing together responsible AI development, data, algorithms and teams into an integrated, managed solution.
The AIops Framework increases an organization’s success rate in deploying AI, helping to unlock more scalable, sustainable, and integrated AI capabilities. This framework should have several key components, including the following:
Mission Engineering: In this crucial first step, organizations define the problem they want to solve and validate it if applied to the AI solution.
Responsive AI with human-centric design: These are risk and change management activities to ensure that AI solutions meet operational requirements, organizational standards and core values. The adoption of AI requires responsible AI and hence scalability.
Data Operations, Machine Learning Operations and DevSecOps: By employing data engineering, data management and machine learning processes and implementing a structured framework for integration, documentation and automation, organizations strengthen their ability to develop, deploy and monitor AI solutions throughout the enterprise.
Reliability Engineering: How do you know if your AI solutions are effective in generating value and resilient in a changing environment? Reliability Engineering gives AI teams a quantitative, repetitive, and measurable way of monitoring deployment.
The AIops framework should also include a robust technical architecture and cyber security policies, a feedback loop for learning and improving, and a cross-functional, integrated team.
Why do organizations need an AIops framework?
AIops offer the organization many technical benefits, including the ability to quickly deploy pre-configured AIops pipelines across the environment; Automated model governance, versioning and monitoring, as well as automated processes for ingesting data; And consistent, comprehensive metadata collection.
However, many of these advantages are on the human side, not on the technical side. Because AIops is based on agile principles and more efficient resource utilization, the AIops Framework supports team members working in research in areas of their technical expertise with roles and responsibilities distributed throughout the enterprise. This improves speed, efficiency, productivity and employee satisfaction.
In addition, AIops focus on the “ability to explain” to ensure that people understand the AI system’s output and the processes behind it. This addresses the confusion or resistance that people may experience with “black box” solutions. The ability to achieve fast, growing wins generates more buy-in, helping organizations move AI from sealed projects in the lab to full-scale operationalization across the enterprise and the field.
How can organizations put AIops into practice?
Ready to start putting AIops framework to use? Success and scalability start with these four steps:
- Install your AI Vision. What do you want to achieve with AI and how does this fit into your current capabilities and overall strategic plan?
- Start clarifying the potential impact of AI. What goals and objectives will be affected by the integration of the AI system and how do you measure the return on investment?
- Identify your AI champions. Which employees are ready to lead the AI project? Which leaders have already bought into the AI culture?
- Capitalize on quick wins. Among the contenders for pilot projects, with the clear parameters for success, which is technically the most feasible?
Achieving reliable, repetitive AI development and deployment is our most important future challenge. By strategically taking advantage of AIops through a comprehensive, proven framework, IT leaders can close the gap between imaginative innovation and real-world deployment, helping the US stay ahead in the global competition for AI supremacy.
Steve Ascaravez is the senior VP and leader of Booz Allen’s analytics practice and AI services business.
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