The Importance of Establishing an Enterprise AI Guideline

AI is fueling the next generation of business innovation, development and growth, and helping companies gain a competitive advantage.

Yet, it has already been proved that if left unstrained, AI also has a dark underbelly. AI built on corrupt data sets have shown bias towards certain genders, race and ethnicities.

Poorly chosen training data and ill-executed AI algorithms can subjugate certain individuals and groups. Therefore, constructing guardrails around the use of AI is mandatory.

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Standardising AI

Many regulatory entities are trying to standardise AI, but it’s a fleeting concept since rules vary with applications, domains and jurisdictions.

Some states are putting forth specific rules, such as Illinois’s Artificial Intelligence Video Interview Act. The ISO is in the process of developing ISO AI working groups and IEEE technology-focused standards.

Importance of Explainable and Trustworthy AI

Trusted, explainable AI will be critical to widespread adoption and use. The success of enterprise AI projects, including maintaining brand integrity and meeting regulatory compliance, is closely tied to explainable AI.

Trusted AI will also allow businesses to work with various stakeholders within their ecosystem, where creating transparency and visibility are critical.

Most organisations also believe that consumers are more likely to choose the services of a company that offers transparency and an ethical framework on how its data and AI models are built, managed, and used.

An IBM survey showed that 90% of businesses say that the ability to explain how the AI arrived at a decision is crucial to their business.

Trustworthy and explainable AI is critical for businesses to:


  • Maintain brand integrity and customer trust
  • Meet external regulatory and compliance obligations
  • Meet internal reporting obligations
  • Ability to monitor and govern data and AI across its lifecycle
  • Ensure applications and services minimise bias

Currently, businesses are facing barriers to developing trustworthy AI, including:


  • Lack of skills or training to build and manage trustworthy AI
  • AI governance and management tools that don’t work across all data environments
  • AI outcomes that are not explainable
  • Lack of regulatory guidance from governments or industry
  • Lack of company guidelines for developing trustworthy, ethical AI
  • Building models on data that has an inherent bias (social, economic)

Key practices to establishing AI guidelines


To create an AI compliance management program, organisations should address the relevant requirements from applicable AI authoritative rules as well as ones that are consistent with the company’s principles and values.

IBM recommends these key practices:


  • Handle AI compliance in a systematic way for a consistent compliance approach across the organisation


  • Adapting to new AI requirements may introduce complexities, be prepared to monitor and adapt


  • Map out different AI rules from different authorities and how they apply to specific areas, such as privacy or security


  • Develop appropriate process enablement and education activities to help employees understand AI compliance objectives and their roles


  • Promote trust and transparency rather than overemphasising verification — to enforce AI compliance positively

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