Artificial Intelligence (AI) adoption continues to rise exponentially and is expected to contribute $15 trillion to the global economy by 2030 (PwC). However, as AI scales across functions, a lack of strategic alignment causes issues. An estimated 54% to 90% of machine learning (ML) models don’t make it into production (lack of operationalization) from initial pilots (VentureBeat), while 85% fail to deliver business value (Gartner) (lack of value-driven outcomes). Solving deployment inefficiencies requires reducing enterprise disruption from siloed and fragmented AI tools or projects lacking overarching connectivity.
This becomes even more challenging in enterprises such as massive organizations or government agencies. But embracing two synergistic disciplines helps us achieve True Success (value-driven operationalization by way of the TVO):
The first is Machine Learning Operations (MLOps), which provides practices for reliable, efficient model productionization. It focuses on the continuous delivery, monitoring, and evolution of repeatable/reusable ML pipelines, including code, model, and data layers. If you are interested in more details about MLOps, I suggest reading more here LINK. MLOps ensures the “operationalization” aspect of AI systems.
The second is Enterprise Architecture (EA), which supplies a business-driven technology blueprint spanning units and objectives. It delivers critical governance and contextual understanding so innovations generate relevance and are value-driven. If you are interested in more details about EA, I suggest reading my article about EA here LINK. EA ensures the “value-driven” aspect of AI systems.
Value-driven operationalization of AI systems/projects through the augmentation of EA and MLOps enables enterprises to achieve True Success by way of Enterprise-infused AI vs siloed experiments. EA's holistic visibility directs appropriate tool usage while MLOps accelerates churning prototypes into measurable optimizations. Their multiplicative ability manifests across key vectors:
Strategic Direction: EA's bird's eye view steers AI applicability to resonate across business priorities like customer intimacy, intelligent operations or product innovation. MLOps realizes tactical potential within these guardrails to achieve “True Success”.
Coordinated Governance: MLOps team autonomy thrives under EA standards, providing universal data schemas, development environments, and target platforms. Collective efforts converge seamlessly rather than disjointed experiments.
Business Alignment: Fluid MLOps uniquely responds to contextual changes like new market entrants or expanding services. EA funnels appropriate adjustments across the enterprise to drive sustained relevance as core objectives evolve.
Shared Infrastructure: EA draws on holistic datasets, APIs and tool access to potentiate dynamic ML innovations. MLOps provides frameworks to unlock operational reliability opportunities from these assets.
Integrated Roadmaps: MLOps speeds capability enhancement through rapid iteration. EA guides model usage priorities and extension roadmaps reflecting long-term modernization.
The key to scaling AI and unleashing its disruptive potential lies in strategically harmonizing its technical velocity with business vision to achieve True Success.
Imagine a government transportation agency aiming to improve traffic flow and safety. They decide to deploy AI/ML models for predictive traffic management and accident prevention. Integrating MLOps within their EA, they first align the AI goals with their strategic objectives, ensuring the models directly contribute to reducing traffic congestion and accidents. Next, they establish a cross-functional team, including traffic engineers and data scientists, to ensure the model is fed with real-world, constantly updated traffic data. They standardize the development process, maintaining transparency and governance throughout. Continuous evaluation and improvement of the models are set up, utilizing feedback from on-ground traffic monitoring. This approach ensures that the deployed AI systems are not only safe and technologically sound but also effectively address the specific needs of public transportation management. (It is important to note that for safety critical systems, in-operations modification of models will impact safety, continued operational safety, and accident investigations.)
Similar to many enterprise-wide initiatives, we must recognize that there are challenges we must understand and address as enterprise leaders in augmenting EA and MLOps. Realizing the full promise of aligned AI requires overcoming organizational and technical complexities. Core challenges include:
Stakeholder Resistance - Orchestrating established roles, workflows and systems around new MLOps demands prompt friction without effective change management.
Expertise Gaps - Optimizing the interplay spans multiple specialized skill sets, demanding both strategic and tactical mastery.
Transition Complexity - Two multifaceted disciplines add intricacies in policies, processes and technical stacks during integration. This is further magnified in the government's transition from legacy systems.
Data Management - Unifying access, security and governance across disparate systems strains resources for integrity.
Scalability Demands - Exponential growth in models and endpoints requires planning for flexible, sustainable, large-scale support.
Compliance Risks - Increased complexity and data use exposure sparks added regulatory and security considerations.
Overcoming resistance and complexity requires leadership commitment, transition planning and forging expertise. But conquering these barriers unlocks lasting value from AI investments.
Our focus through the Triangle of Value-Driven Operationalization (TVO) lens is on achieving “True Success” for enterprise-infused AI - fully realizing its benefits through both effective deployment and strategic business alignment. As outlined in introducing TVO, True Success requires value-driven operationalization: 1) Transitioning innovations like AI to live production systems at scale rather than isolated pilots, paired with 2) Delivering measurable improvements towards core enterprise objectives and intended ROI. Integrating MLOps and EA fulfills both criteria. I want to call this catalyst Enterprise-Infused AI, where we can achieve a deeper level of maturity via integrated architecture and operations. It moves beyond fragmented experiments lacking coordination. We can add to our catalysts on the TVO framework below.
The time to act is now. As AI proliferates, resonance with enterprise objectives is imperative. Architected alignment breeds capability, while siloed efforts foment fragility. We must elevate analytical investments through orchestrated adoption frameworks that weave MLOps velocity with contextual governance. Let us come together to infuse AI with purpose, continuity, and oversight, which are critical for scaled impact. Join us on the frontier of Enterprise-Infused AI to drive our transformation forward.
Special thanks to the amazing contributors to this article: