Scaling AI Solutions: Evaluating if Your AI Is Truly Enterprise-Grade Instead of Merely Sounding the Part

Assessing the Legitimacy of Your AI Solution: Identifying When It's Really Suited for Large-scale Deployment

In the rapidly evolving world of artificial intelligence (AI), it's no longer about wondering if an AI solution sounds enterprise-ready. Instead, it's crucial to focus on whether it acts that way under pressure, demonstrating reliability, security, and alignment with business goals.

Starting with Real Problems

AI deployment is not just about integrating new technology; it's an organizational transformation. To ensure success, start by identifying real problems within your organisation, such as volume spikes, long wait times, or agent burnout. By addressing these challenges, AI can bring about significant improvements and add value to your operations.

Understanding the Why

Before embarking on an AI journey, it's essential to understand the motivation behind adopting AI. Is it a board mandate, a solution to a pressing issue, or a means to lead transformation in the industry? Knowing the 'why' will guide your decision-making process and help you choose an AI solution that best fits your needs.

The Role of Leadership

Success in AI deployment depends on champions who can align technology with business priorities. Priya Vijayarajendran, CEO of ASAPP, with a background in technology and business leadership, is one such example. Her experience in product management and engineering at major companies has equipped her to lead ASAPP effectively.

The Four Layers of AI Evaluation

Executives evaluating AI vendors should unpack four layers: The Foundation, The Model Layer, The Orchestration Layer, and The Software Layer. By understanding these layers, you can assess whether an AI solution is robust, scalable, and capable of delivering value within the messy reality of enterprise operations.

AI Readiness: Key Components

Best AI readiness involves no "black box" behavior, full data lineage, model drift detection, multiple model environments, and state-of-the-art observability. Additionally, it includes role-based access controls, real-time monitoring, configuration options, and the ability to insert human oversight when needed. Good AI readiness also encompasses regulatory compliance, uptime Service Level Agreements (SLAs), and compatibility with common APIs and systems.

Avoiding Red Flags

There are several red flags to watch out for during the AI deployment process. Lack of observability, heavy reliance on humans to patch gaps, overpromising by a vendor, and the presence of black box models in AI systems are all signs that the solution may not be enterprise-ready.

The Importance of Stakeholder Involvement

Bring stakeholders along early in the AI deployment process, including legal, risk, data governance, and IT. Their input will help ensure that the AI solution is compliant, secure, and aligned with the organisation's goals and standards.

Timeboxing and Documentation

Timebox your proof of value for AI deployment, and document the journey of AI deployment for scalability. This approach will help you measure the impact of AI on your business outcomes and ensure that the solution continues to deliver value as your operations evolve.

Industry Standards for AI Readiness

Industry standards for AI readiness are framed across a Good-Better-Best spectrum. The level of readiness for AI should match the level of ambition. For instance, in enterprise environments, AI systems must be battle-tested in three areas: integration with existing systems, resilience in production at scale, and clear, measurable impact on business outcomes.

In conclusion, navigating AI deployment in the enterprise requires careful consideration of various factors. By focusing on real problems, understanding the 'why' behind AI adoption, evaluating AI solutions comprehensively, and involving stakeholders throughout the process, organisations can ensure they choose an AI solution that is reliable, explainable, secure, and aligned with their business goals.