How Regulation May Impact Innovation
In the U.S., California legislation has taken a first stab at defining guardrails domestically, following the footsteps of the EU Artificial Intelligence Act. However, these early regulation attempts will take time to enact and be vetted for successes, failures, and inevitable adjustments.
We expect AI to be governed differently according to three broad categories:
- AI creators: OpenAI and hyperscalers creating AI models from scratch. These entities face unique regulatory challenges related to responsible and demonstrable data sourcing.
- AI adapters: Fine-tuners of the creators' models that embed them along with retrieval-augmented generation and similar technologies, adapting them for specific business application development. Enterprises must ensure they are sourcing models that can be attested to on intellectual property (IP) infringement or have some sort of protection against IP infringement.
- AI consumers: Most businesses taking advantage of the adapters' AI applications in their day-to-day operations. These organizations must ensure their data sets are cleansed and compliant with regulations.
The Perils of AI Missteps
We've seen a lot of enthusiasm from CIOs trying hundreds of different uses for generative AI. However, this rush to adopt AI has led to past mistakes, such as the adoption of cloud services without proper planning. Avoid being swayed by the allure of new technology without assessing its implications.
Methodically approach AI as you would any other enterprise tool. Focus first on internal applications to optimize workflows, automate processes, and reduce risks. Starting with internal applications allows organizations the grace to learn and adapt before expanding to applications that impact more stakeholders, including customers.
6 Practical Steps to AI
Establish a solid AI standard by following these steps:
- First look inward: Focus on internal applications to optimize workflows, automate processes, and reduce risks. They are easier to identify—and safer because you're not exposing yourself to external vulnerabilities.
- Ensure data integrity and compliance: Data integrity and compliance are critical for all three AI use case categories. For creators, ensuring responsible sourcing of data is essential. Adapters need to cleanse and comply with data sets, while consumers must vet software-as-a-service providers and confirm proper data management.
- Follow the lead: Learning from state-level regulations, such as California's, can offer insights about future federal frameworks. Businesses should learn from how others adapt accordingly.
- Avoid past mistakes: The current rush to adopt AI mirrors past technology adoption cycles. Avoid being swayed by the allure of new technology without assessing its implications.
- Surround yourself with knowledgeable teams: Leaders should surround themselves with knowledgeable teams to navigate AI's complexities and understand their business's true needs. Establishing an AI center of excellence unites cross-functional teams, including business functional areas addressing specific challenges, development, data science, IT, and FinOps.
- Adopt ethical AI: Implementing responsible practices is imperative to navigate the regulatory landscape. Business leaders and technologists should prioritize transparency, data privacy, compliance, and continuous learning in their AI programs, along with flexibility to adapt to new or changing regulations and technologies.
The goal is to find the real value in the challenges it can solve for your business. Prioritize practical solutions over grand innovations. Focus first on the unsexy work that frees your employees from the mundane tasks that no one loves.
Conclusion
Moving beyond merely trying AI to doing AI requires starting with sound processes and practical applications that not only will insulate your organization from future uncertainties, but drive it forward. Returning to IT fundamentals is the key to making AI a reality.
Juan Orlandini is CTO, North America of Insight Enterprises.