4 Key Initiatives to Prepare Your Enterprise for AI

‍1. Set Clear AI Ambitions and Spot Opportunities

Over 60% of CIOs incorporate AI into their innovation plans, yet fewer than half feel confident managing the associated risks. Bridge this gap by clearly defining your AI ambitions. Determine where and how AI will be deployed within your organization, whether for internal operations or customer-facing activities. A well-defined ambition guides your AI strategy and highlights valuable opportunities.

2. Understand AI Deployment Options and Trade-offs

AI deployment can range from using public, off-the-shelf models to building custom algorithms. Each option varies in cost, speed of deployment, and customization potential. Embedding AI in existing applications offers a quick, cost-effective solution, while custom-built models provide higher accuracy and control but require more investment and time.

3. Assess and Mitigate AI Risks

AI introduces risks such as unreliable outputs, data privacy concerns, and cybersecurity threats. Organizations must define their risk appetite and implement robust measures to mitigate these risks. This includes ensuring AI models are transparent, secure, and compliant with emerging regulations. Establishing a clear AI risk management framework safeguards your organization from potential pitfalls.‍

4. Articulate AI Risk Tolerance Across Business Units

Different departments have varying levels of risk tolerance. For instance, HR might prioritize data privacy, while customer service may focus on automation that enhances user experience. Collaborate with executive leaders to define acceptable AI risk levels for each department, balancing the degree of automation and explainability required for their specific processes.

‍Leverage Custom AI Solutions for Optimal Results

Cogwise goes beyond standard AI implementations by offering custom AI models tailored to your specific needs. Our AI solutions are trained on your existing repositories, providing highly relevant suggestions and optimizations. Additionally, we develop conversational bots for code reusability and integrate AI models with CI/CD pipelines, continuously improving your codebase.

AI Opportunity Ambition

To maximize AI's potential, engage your executive team in selecting the right AI opportunities. AI falls into two broad categories:

1. Everyday AI: Enhances productivity by enabling faster, more efficient work.
2. Game-changing AI: Sparks creativity through new products, services, or capabilities, disrupting business models and industries.

Both types can be applied internally or externally. Defining your AI ambition means choosing the right mix of these use cases.

Investment expectations are crucial. While 73% of CIOs plan to increase AI spending in 2024, 67% of CFOs remain skeptical about digital investments' returns. Define your AI ambitions with three scenarios:

1. Defend Your Position: Invest in quick wins that improve specific tasks with everyday AI tools. This is cost-effective but won't provide a sustainable competitive edge.
2. Extend Your Position: Invest in tailored applications for a competitive advantage. These are more expensive and time-consuming but more valuable.
3. Upend Your Position: Create new AI-powered products and business models. These are high-risk and costly but offer substantial rewards and industry disruption potential.

Most enterprises focus on optimizing existing capabilities rather than transformative game-changing AI. Ensure your team understands the feasibility of AI opportunities. The Gartner AI Opportunity Radar helps map AI ambition in terms of opportunity and feasibility, highlighting the potential for disruptive innovations.

Leverage the radar to balance ambition with practicality, ensuring your AI strategy aligns with your organization's readiness and technological capabilities.

‍AI Deployment Options and Trade-Offs

The AI market is booming with new models and tools, and many large software vendors are integrating AI into their existing applications. This competitive landscape offers a variety of deployment options:

1. Consume Embedded AI: Use AI capabilities within existing applications (e.g., Adobe Firefly for image generation).
2. Embed AI APIs: Integrate GenAI APIs into custom applications for tailored AI functionalities.
3. Extend AI Models via Data Retrieval: Use retrieval augmented generation (RAG) to improve AI accuracy with external data.
4. Fine-Tune AI Models: Customize pretrained models with new datasets for domain-specific tasks.
5. Build Custom AI Models: Develop models from scratch tailored to your data and business needs.

Each approach has trade-offs:

• Costs: Embedded applications and APIs are cost-effective, while building custom models is expensive. Fine-tuning costs vary.
• Knowledge: General models require domain-specific data for improved accuracy, necessitating retrieval, fine-tuning, or custom-building.
• Security and Privacy: Custom models offer better control over security and privacy.
• Model Output Control: Customization helps mitigate risks like biased or harmful behavior in AI outputs.
• Implementation Simplicity: Embedded applications and APIs are simpler and quicker to deploy without disrupting workflows.

Choosing the right approach depends on balancing these trade-offs with your organization's goals and resources.

Balancing AI Risk Tolerance Across Business Units

To finalize AI opportunities, business leaders must articulate their risk tolerance regarding AI reliability, privacy, explainability, and security:

AI Reliability

AI systems can be prone to:

• Factual inaccuracies
• Hallucinations (fabricated outputs)
• Outdated information
• Biased outputs due to training data

AI Privacy

Privacy concerns include:

• Sharing user data with third parties without notice
• Processing identifiable data
• Leaking sensitive or personal data
• Proprietary information becoming part of the AI's knowledge base

AI Explainability

Machine learning models are often opaque, leading to:

• Unpredictable outputs
• Unverifiable results
• Unaccountable decisions

AI Security

AI systems can be targeted by malicious actors, leading to:

• Unauthorized access to sensitive information
• Manipulation of AI outputs
• Creation of malicious code by AI

Defining AI Risk Ambition

CIOs must work with executive leaders to balance AI risks and opportunities. This involves determining the degree of automation (from fully automated to "human in the loop") and the level of explainability (from opaque to fully explainable).

Each executive (CxO) must establish acceptable risk levels for their departments in line with their AI opportunities. For instance, HR might prioritize safety due to sensitive data, while customer service might focus on "responsible automation" to maintain transparency with customers.

‍Conclusion

Preparing your enterprise for AI is a strategic imperative for IT leaders. By defining AI ambitions, understanding deployment options, and managing risks, you can position your organization to capture AI opportunities and drive innovation. Contact us to embark on your journey towards a smarter, more efficient future.