The rapid expansion of generative AI that began in 2023 marked a significant shift in the world of AI. As organizations raced to leverage AI for its potential productivity benefits, they encountered challenges reminiscent of those seen in traditional software development — like technical debt.
Technical debt is the hidden costs of making fast and convenient short-term decisions without regard for the long-term, downstream impact. Technical debt is like taking out a high-interest loan: initially it may accelerate development, but over time, it can accrue interest, complicating future modifications and inflating operational costs.
The risk of generating AI-related technical debt highlights the importance of adopting responsible AI practices early in the development process. Responsible AI is comprised of three critical components:
- Symbiotic AI: Enhances human capabilities safely.
- Secure AI: Prioritizes data protection and regulatory compliance.
- Sustainable AI: Encourages transparent and unbiased systems for a more equitable future.
By adhering to these principles, organizations can deploy AI in ways that drive positive innovation and provide lasting value while minimizing the problem of creating technical debt.
Embrace responsible AI best practices
Historically, technical debt has been prevalent in software development, due to challenges such as postponing bug fixes, using outdated frameworks, or skipping proper testing and documentation. In 2022, McKinsey reported that 30% of CIOs believed more than 20% of their budget for new product development was diverted to manage technical debt.
One example of creating technical debt is found in the rapid adoption of cloud services. Largely attracted by the prospect of lower costs, many organizations lifted and shifted their applications from private cloud to public cloud. However, they failed to realize that their code needed to be refactored to gain public cloud benefits. By bypassing application optimization, they created technical debt that required future investments to fix.
With software development, technical debt can be created when developers cut corners. Similarly, the technical debt risks of rapidly adopting generative AI without best practices in place include:
- Prioritizing speed over thorough evaluations
- Making poor decisions due to a lack of experience
- Making compromises when integrating generative AI with legacy systems
- Choosing a generative AI model that quickly becomes obsolete
An example of how easy it can be to fall into the generative AI technical debt trap is related to choosing an AI model. Multiple companies raced into the AI model marketplace. While there were a few name-brand players, there was also a multitude of other parties. But many of the companies won’t be around for the long haul. And the organizations that use their models, perhaps to save money, may see technical debt down the road in the form of the costs required to change their AI model.
The FAIR™ approach to responsible AI
Organizations that want to avoid creating technical debt during AI development will establish best practices that mitigate the threat. Foundry for AI by Rackspace (FAIR™) advocates for this approach, which includes this three-step framework.
- Responsible implementation: The foundation for minimizing technical debt in generative AI development lies in addressing secure, symbiotic and sustainable practices:
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- Secure: Having a safe environment in which to develop generative AI initiatives requires consideration of key issues, such as explainability, accountability and transparency. The rewards of taking a secure approach include strengthening user trust, enhancing AI systems reliability and making regulatory compliance easier to uphold.
- Symbiotic: Ethics are a vital consideration when designing and deploying generative AI. Ethical initiatives help ensure that AI systems support humanity, without, for example, introducing or perpetuating biases. The results of implementing best practices around fairness include augmenting human intelligence and making us better at our jobs.
- Sustainable: Achieving sustainable AI covers the spectrum from environmental challenges to inclusivity, ethics and collaboration. AI has the potential to make a positive contribution to a greener, healthier world. It also offers the ability to create more equitable, resilient and socially sustainable societies. The rewards of addressing sustainable AI development is empowering individuals and communities to thrive in an increasingly technology-driven world.
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- Choosing the right generative AI model: With a variety of AI model options available, selecting the most suitable model from the outset is crucial for long-term success.
- Mitigating implementation complexity: While generative AI can transform business operations, careful planning and meticulous execution can prevent potential disruptions and overcome complexities.
By taking the proactive approach of embracing responsible AI, organizations will not only create a more productive environment for generative AI development in the short term, but also they’ll avoid the risk of creating technical debt that negatively impacts their AI initiatives in the long term.