How to get the most out of generative AI
Given the early maturity level of generative AI, business leaders should ignore the buzz and pay attention to what they want to achieve for their organizations. A measured approach would focus on goals, protect and responsibly use data, and ensure trusted and reliable output.
Lead with goals.
Instead of letting the technology determine what you do with it, be disciplined in examining if generative AI is even the right solution for the objective or problem at hand. A well-defined business case is the right place to start.
Clearly documenting the business problem and expected outcomes is essential for evaluating the potential gains, risks and scale of investment—and gaining stakeholder commitment. The business case gives development teams details for defining technical requirements, understanding what data is needed and tailoring a solution that syncs with your organization’s resources.
There’s growing confidence that generative AI can create amazing things with the content it’s trained on, but it’s too soon to leave it completely on its own in enterprise applications.
Key to protecting data is having humans in the loop to avoid the misuse of sensitive or biased input data and monitor for distorted outputs.
Shielding sensitive personal customer or employee information, intellectual property and intellectual capital should be a paramount concern. Cloud providers and other vendors are improving safeguards in this space, but public large language models may offer limited protection for sensitive or confidential information.
Ensure reliable output.
Confirmation transformers and ongoing testing can verify that generated content doesn’t contain any inaccurate or discriminatory elements that might cause harm.
This transparency improves explain-ability and fosters confidence in both the process and the finished product. Unless the objective is pure research, there will be an expectation for any generative AI initiative to justify its cost. Organizations are wise to take steps to avoid putting more into a project than they get out of it.
While it’s feasible to stand up proof-of-concept prototypes with an initial limited budget, rolling out a production solution may not scale well over time. Smaller, specialized models tailored to specific business cases may yield better results and require a smaller funding commitment for ongoing maintenance.
Keep your guard up.
A responsible AI framework should include privacy and security principles.
Start with strong data.
Costs can balloon quickly, especially if overall organizational data maturity is low. Investing in foundational data governance, quality and observation strategies as a precursor to generative AI business strategy development will optimize the performance, security and ROI of any initiative.
Edd Pineda is U.S. Head Data Scientist at Kyndryl