Is Your Business AI-Ready? Part 2: Platform Readiness

As Artificial Intelligence (AI) rapidly evolves, organizations across industries are eager to gain a competitive edge by adopting it as quickly as possible—often with a sense of urgency. But should your organization also jump on the AI bandwagon without careful consideration?

This four-part series outlines the key focus areas that require thoughtful planning and internal discussion to ensure AI adoption is strategic, sustainable, and value-driven. If you missed Part 1 – Data Readiness, you can read it here. The four focus areas are:

1. Data

2. Platform

3. Applications

4. Infrastructure

In this article, we focus on Platform Readiness and highlight the critical discussion points every organization should explore with its stakeholders before going live with AI.

a) Shadow AI Management
Similar to “Shadow IT,” employees or business units may independently adopt AI tools outside of approved governance structures. Organizations must establish detection and prevention mechanisms—such as network monitoring, access controls, and endpoint security—while also defining clear policies and conducting awareness programs to mitigate associated risks.

b) User Experience Measurement
The success of any AI platform is closely tied to the end-user experience. Without systematic measurement, it is difficult to determine whether the platform enables adoption or inhibits productivity. Regular evaluation of user satisfaction and usability should be institutionalized.

c) Integration and API Oversight
Integrations and APIs are often introduced to connect disparate systems. If not consistently reviewed, maintained, and documented, these linkages may be compromised due to staff turnover, system upgrades, or the introduction of new technologies. Ongoing compliance monitoring is therefore critical.

d) Security and Regulatory Compliance
For organizations in regulated industries, such as financial services, compliance with industry-specific standards is mandatory. This requires regular security testing, vulnerability scanning, and compliance reporting, aligned with regulatory requirements and internal risk controls.

e) Performance and Reliability
AI platforms must meet defined standards for availability, performance, and reliability. For mission-critical systems, expectations may include near-continuous uptime (e.g., 99.99%). Service-level objectives should be clearly documented and monitored.

f) Cost Transparency
A structured and itemized view of operating costs is essential. Regular cost reviews should be conducted and communicated in a format that is accessible and transparent to all relevant stakeholders.

g) Return on Investment (ROI)
Clear metrics and timeframes should be defined for assessing ROI. Evaluations should take into account both direct and indirect benefits and be reported at regular intervals to ensure accountability.

h) Sustainability and Corporate Responsibility
Platform operations should align with the organization’s environmental, social, and governance (ESG) objectives. Tracking relevant sustainability metrics and reporting progress is vital to demonstrate alignment with broader corporate commitments.

i) Risk Controls for AI Model Lifecycle
When AI models are updated, replaced, or retired, formal governance processes must be in place to document these transitions and ensure that risk controls remain effective throughout the model lifecycle.

Platform readiness is not solely a technical exercise; it is a business imperative that underpins responsible and sustainable AI adoption. Addressing the considerations outlined above will enable organizations to minimize risks, optimize investments, and ensure alignment with strategic objectives.

The next article in this series will focus on Applications Readiness.

Interested in learning more?
Contact our team of experienced consultants at info@cybiant.com to explore how we can support your AI transformation journey.

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