As Artificial Intelligence (AI) continues to evolve, organizations across industries are eager to adopt these technologies—often with a sense of urgency—to gain a competitive edge. But does this mean your organization should immediately join the AI adoption wave?
This four-part series explores the essential areas organizations must carefully evaluate and prepare for before implementing AI. By addressing these areas through structured planning and internal discussions, your organization can embrace AI with clarity and confidence.
If you have not yet reviewed the earlier parts of this series, please refer to:
The four critical focus areas are:
a) Data
b) Platform
c) Applications
d) Infrastructure
This article highlights the key considerations for Application Readiness that organizations should actively discuss with stakeholders.
Application Readiness: Key Considerations
a) Documentation of Current Processes
Organizations should ensure that all processes related to modernizing applications with AI capabilities are properly documented. Clear responsibility must be assigned for maintaining this documentation, while accountability should be established to guarantee that it remains accurate and up to date. In addition, a regular audit frequency should be defined to verify compliance and consistency.
b) Vendor Transparency and Data Usage
It is essential to confirm that vendors hosting or providing applications are transparent in disclosing how and where organizational data is being used. Organizations must also define what constitutes “acceptable use” of their data within these vendor relationships to safeguard compliance and trust.
c) Change Management Preparedness
The existing Change Management process, including the Change Advisory Board and relevant stakeholders, must be capable of understanding, accommodating, and effectively managing AI-enabled applications. This ensures a seamless transition when AI-driven changes are introduced into the organization.
d) Application Development and Control Processes
Both in-house and outsourced application development processes should incorporate the necessary modifications and controls to address AI-enabled applications. These controls, along with the associated reports, should be reviewed on a regular basis to ensure adequacy and to support continuous improvement.
e) Business User Training and Competency
Business users must receive adequate training to understand, interpret, and communicate the results produced by AI-enabled applications. A Training Needs Analysis (TNA) should be conducted to assess requirements, and ongoing updates to training programs should be implemented to keep pace with technological advancements.
f) Performance Metrics and Monitoring
Appropriate control mechanisms and operational metrics should be established to measure and monitor the performance of AI-enabled processes. These metrics ensure that AI adoption delivers consistent and reliable outcomes aligned with business objectives.
g) Mitigation of AI Bias
Organizations must implement clear steps to identify, monitor, and mitigate bias within AI models. Proactive bias management ensures fairness, transparency, and accountability in AI-driven decision-making.
Conclusion
To fully realize the benefits of AI adoption, organizations must approach readiness holistically. Application Readiness is a critical step, ensuring that processes, governance, and people are aligned to support AI integration responsibly.
Continue with us in Part 4 – Infrastructure Readiness, where we will address the final pillar in this series.
Interested in learning more?
Contact our team of experienced consultants at info@cybiant.com for further insights or to discuss how AI readiness applies to your organization.
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