AI in Business: Aligning Best Practices

Artificial intelligence (AI) is becoming the cornerstone of strategic advantage. As author Geoff Woods mentions in The AI-Driven Leader: Harnessing AI to Make Faster, Smarter Decisions, leaders who successfully integrate AI into their strategic decision-making processes can gain a significant competitive edge. This integration, combined with proper governance and framework implementation, can form the foundation of intentional and well-planned AI in business. As organizations work to harness the potential of AI, a critical question emerges: How can businesses implement AI both effectively and responsibly?

An In-Depth Approach to AI Implementation

Current frameworks and best practices point to several essential elements that can create an in-depth approach to AI implementation. These include security measures, adaptability strategies, accuracy controls, and ethical guidelines. Let’s explore how these elements can work together.

Strategic Decision Making in the AI Era

AI in business requires a thoughtful approach to leadership and decision making. Key principles include:

Shifting focus from operational tasks to strategic initiatives
Developing robust, data-driven decision processes
Enhancing team productivity through appropriate and approved AI tools

Implementation Example: A midsize technology company improved their decision-making process by:

Creating clear protocols for AI-assisted decisions
Establishing regular review cycles for AI systems
Developing metrics to measure AI impact on productivity

Core Implementation Principles

Security: Building Trust Through Protection

In an era where data protection is critical, security should be a business imperative. Organizations need robust system security measures and data privacy standards that go beyond basic compliance.

Practical Approach

Implement multi-layer authentication systems
Establish continual monitoring protocols
Create clear data handling procedures
Conduct regular security audits

Adaptability: Staying Ahead of Change

The only constant in AI is change itself. A best practice is to recognize this reality and encourage organizations using AI to create a culture of continual learning, where both leaders and team members are consistently upskilled to work alongside AI tools effectively.

Application Example: A manufacturing company implemented an adaptable AI strategy by:

Creating cross-functional AI teams that meet weekly to assess and adjust algorithms
Implementing a monthly review cycle for the AI model’s performance
Developing a rapid response process for when AI predictions deviate from expected patterns
Establishing a continual learning program where employees spend up to four hours per week on AI-related training

Factual Accuracy: The Foundation of Trust

Checking for accuracy in AI-generated results is a best practice and, in business, may necessitate systematic oversight and verification processes.

Implementation Strategy

Establish verification protocols for AI outputs
Create clear, documented procedures
Employ regular accuracy audits
Maintain human oversight of critical decisions

Ethical Implementation: Leading With Values

AI ethics encompasses the guidelines that direct the development and application of AI systems. These principles strive to promote accountability, reflect societal values, and reduce harm to people. While ethical perspectives may vary among different groups, certain fundamental standards are universally important.

Organizations need clear frameworks and audits to help AI responses be ethical. To verify that AI tools meet essential standards, businesses should test outputs and review the training parameters of the large language models (LLMs) used.

Implementation Components

Clear ethical guidelines for AI use
Regular bias testing procedures
Diverse input in LLM and AI development
Transparent decision-making processes

Getting Started

Building an effective AI implementation strategy requires a balanced approach that combines clear governance structures, strong security measures, regular monitoring and updates, and continual learning and adaptation. Here are some quick tips for getting started:

Assess your current AI capabilities
Identify key areas for improvement
Develop a phased implementation plan
Create clear metrics for success
Establish regular review cycles

Making It Work: Best Practices

The path to effective AI deployment requires more than just understanding these principles—it needs human leadership and oversight. Here is a detailed list of steps organizational leaders can take when implementing AI.

Start with strong governance:
- Establish clear oversight structures
- Define roles and responsibilities
- Create decision-making protocols
Build effective teams:
- Technical professionals
- Business leaders
- Ethics reviewers
- End-user representatives
Implement monitoring systems:
- Performance tracking
- Regular system audits
- Compliance checks
- User feedback collection
Build out training:
- Basic AI literacy for all employees
- Advanced training for technical teams
- Leadership development for AI oversight
- Regular skill updates and refreshers

Looking Ahead: The Future of AI in Business

As AI continues to evolve, organizations need to focus on creating sustainable, responsible AI processes. This includes:

Regular review and updating of AI strategies
Ongoing monitoring of regulatory developments
Continual improvement of governance structures
Regular assessment of AI usage and effectiveness

Implementation Example: A professional services organization built a sustainable AI program by:

Creating quarterly review cycles
Establishing clear key performance metrics
Developing feedback mechanisms
Building flexibility into their processes

The journey to effective AI implementation shouldn’t be rushed, but it also shouldn’t be ignored. Shadow IT may expand if AI is passed over—and this can present considerable risk to organizations.

By following the NIST Artificial Intelligence Risk Management Framework (AI RMF) to help manage AI risks systematically and using the SAFE AI Framework™ from Forvis Mazars to complement key points mentioned in The AI-Driven Leader: Harnessing AI to Make Faster, Smarter Decisions, these principles can provide a structured approach to implementing AI securely, adaptably, factually, and ethically.

The future belongs to organizations that can harness the potential of AI while maintaining high standards of security, adaptability, factual accuracy, and ethical deployment. Embracing AI in business is no longer a question—it’s a matter of how to do so responsibly and effectively.

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