By Ramani Natarajan –
The release of the generative AI engine, Chat GPT, by OpenAI in November 22, 2022, heralded a new era in conversational computing in the public domain. ChatGPT and its Artificial Intelligence (AI) cousins have already embedded themselves deeply in everyday lives. Bloomberg estimates that generative AI will become a 1.3 Trillion market by 2032, a scant nine years away. AI has begun to profoundly affect a wide spectrum of applications such as flight bookings, cuisine suggestions, vacation recommendations, art and music generation, financial investing, and other pursuits in which we humans are engaged. It is a matter of certainty that the high potency of AI will create and uncover new and novel areas to influence.
This rapid shift to a more mainstream technology has undoubtedly brought new opportunities—such as the potential for increased automation, improved efficiency, cost savings—but also the potential for unintended consequences, risks and fears. This worrisome triumvirate of affects both individuals and organizations of all stripes.
Organizationally, the best way to mitigate the ill effects of AI is to openly recognize this reality and develop strategies and tactics to confront the risks and concerns surrounding AI and lay the groundwork for its safe and effective use.
Considerations for AI Implementation in Your Operations
As AI continues its rapid integration into various aspects of operations, there are several considerations:
Undefined Objectives and Goals
Implementing AI without a clear enterprise strategy and defined purposes can lead to inconsistent, redundant, and ineffective use cases. This lack of direction can result in AI systems not addressing the intended business needs effectively.
Incomplete or Inadequate Documentation
Insufficient documentation of AI model development (or understanding of it) can create challenges in determining if its algorithms and data sources are appropriate and optimized for your business use cases. It can also become difficult to troubleshoot issues or update the AI model, potentially leading to additional poor performance or unexpected outcomes.
Unaddressed Unreliable Data or Bias in AI Algorithms
Failing to understand the role of bias in AI algorithms can result in skewed inputs or outputs involved in decision-making. For example, the failure to draw from diverse and representative training data might cause an AI system to perpetuate biases present in the data3, leading to misleading outcomes for certain situations. This could erode trust in the system and hinder its adoption as a net benefit for organizational productivity.
Lack of Incorporation of User Feedback
Isolating the AI implementation from collaboration with stakeholders can result in a system that does not align with real-world requirements. By neglecting user feedback, an AI solution may fail to meet user expectations, resulting in inconsistent adoption or rejection of the technology altogether.
5 Foundational Elements to Shape Your Organization’s Use of AI
To help guide your organization's use of AI and make it as effective, integrated, and responsible as possible, we recommend considering the implementation of these five foundational elements:
1. Establish an AI Governance Framework
Create a comprehensive AI governance framework that incorporates clear guidelines and principles in how AI can be used in your organization to ensure consistent implementation4. This can be facilitated through the use of a governing body responsible for overseeing the implementation, evaluation, and planned changes in AI integration throughout workflows and services, ensuring the responsible use of AI technologies and investment budgets.
2. Develop Robust AI Risk Management Strategies
Formulate effective AI risk management strategies by conducting regular risk assessments and impact analyses relating to the use of the technology. This can include:
- Identifying potential risks associated with AI implementations.
- Recognizing and understanding the various risks associated with using AI technology, such as data quality issues.
- Examining how AI implementation will affect existing processes, workflows, and resource allocation.
- Understanding how AI will influence end-users, customers, and stakeholders, and ensuring that the AI system enhances user experiences.
- Developing mitigation plans to address and minimize risks.
3. Emphasize Data Privacy and Security
Prioritize data privacy and security by implementing strong controls for data protection that are in line with relevant privacy and security regulations, cybersecurity recommendations, and data handling best practices.
4. Educate and Train Employees on the Effective Use of AI
Implementing AI in an organization requires not only technical expertise but also a strong foundation in AI safety and ethics. This can involve training on the foundational technologies behind AI as well as teaching employees how to interact effectively with AI systems so they can better recognize issues.
5. Continuous Monitoring, Auditing, and Improvement
Paired with an enterprise-wide governance model, consider instituting a system to regularly monitor and audit AI systems to evaluate their performance, identify potential issues, detect any anomalies, and plan for future AI integration.
Bringing It All Together
As AI continues to weave its way deeper into our personal and professional lives, it is crucial for organizations to begin to think more holistically about how to use it responsibly and effectively.
Our hope is that by embracing these foundational elements, organizations help to ensure that the use of AI enhances user trust, drives innovation, and paves the way for new and sustainable competitive advantages.
Want to continue your journey toward more sustainable and efficient use of AI? Then make sure to check out our related resource, Beyond the Hype: 9 Secrets to Get Your AI Game On!, now.
Works Cited
1. Reuters (2023, Feb 2) "ChatGPT sets record for fastest-growing user base - analyst note" Retrieved from https://www.reuters.com/technology/chatgpt-sets-record-fastest-growing-user-base-analyst-note-2023-02-01/
2. Forbes (2023, Jul 28) "AI Productivity Gains Are Not Limited To Big Tech" Retrieved from https://www.forbes.com/sites/greatspeculations/2023/07/28/ai-productivity-gains-are-not-limited-to-big-tech/?sh=57abf8c71c46
3. Wired (2019, Nov 21) "Researchers Want Guardrails to Help Prevent Bias in AI" Retrieved from https://www.wired.com/story/researchers-guardrails-prevent-bias-ai/
4. Harvard Business Review (2022, Mar 4) "How to Scale AI in Your Organization" Retrieved from https://hbr.org/2022/03/how-to-scale-ai-in-your-organization
