Enterprise AI - The New Beginning

This year at the Gartner IT Symposium many leaders in attendance were taken by surprise to learn about the exponential budget allocation and the planned capital expenditure for AI by leading enterprises world wide.

While this year has been unprecedented in many ways, it also marks the beginning of a new enterprise wave, a move from being a data-driven enterprise to the AI-First or intelligence-driven enterprise.

Traditional CISO’s focus has always been on the golden triangle of Infrastructure, Data and People while the CIO’s focus on Cost, Time and Quality. This year MODEL and EXPERIENCE are being added as new vectors to the enterprise triangle.

CIO’s bottom-line focus and the CTO’s top-line focus will be augmented by how quickly they adapt and get ahead in the new Artificial Intelligence turn.

Enterprise AI Engine
Enterprise AI Engine

WHAT is an AI MODEL and WHY is Model life cycle so crucial?

Models are mathematical representations that are “trained” using data and human expert input to replicate a decision process with a goal to enable automation.

AI projects run machine learning algorithms on datasets to build “models” that learn by examples from historical data to predict and forecast, enabling enterprises to make better decisions.

A new role the Enterprise AI Architect (EAIA) oversees model management from model inception through refreshes to eventual replacement. The goal of a successful enterprise model management methodology is in enabling maximum velocity and efficiency with visibility, accountability and control at scale.

Enterprise Model Classification:

• Traditional Regression and Rules-Based models
• Machine Learning (ML) models

Model Management Objectives:

• Make more accurate decisions that provide better outcomes
• Make faster decisions
• Eliminate errors like biases
• Reason, Explainability and Transparency improvement
• Model Security and Governance

What is ModelOps, MLOps and AIOps?

Model Operationalization (ModelOps) is primarily focused on the governance and life cycle management of all AI and decision models (including models based on machine learning, knowledge graphs, rules, optimization, linguistics and agents).

Machine Learning Operationalization (MLOps) focuses on the life cycle of machine learning models.

AI Operationalization (AIOps) utilize big data, machine learning and analytics technologies to enhance IT operations (monitoring, automation and service functions).

The enterprises gaining the most from their AI investments elevate models to first class assets, investing the time and money to ensure that along with having a powerful model factory to create models, they also add the
ModelOps capability, to efficiently drive the deployment, monitoring, and governance of their models and ensure they maximize return on their investments in models — no different than any physical or intellectual asset of the enterprise.

“Experience, Privacy and Performance are the key metrics of the modern AI-first enterprise.” - Ramesh Periasamy

Written by

Technologist, Inventor, Entrepreneur.

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