With the explosion of generative AI, and only more momentous change inevitable, it can feel like a mad dash to have all the answers. But in the face of both potential risks and benefits, the most important thing leaders can do is make a roadmap.
So says Hugh Cassidy, head of AI and chief data scientist at LeanTaaS, a company based in Santa Clara, California that provides AI-powered and SaaS-based capacity management, staffing, and patient flow software for health systems. He spoke with StrategicCIO360 about the steps to take.
Should IT leaders be prioritizing generative AI initiatives with all the excitement around the technology right now?
Yes, prioritizing generative AI initiatives is imperative for CIOs and other tech leaders in today’s fast-paced tech landscape.
Generative AI opens up a huge range of new opportunities for businesses. Whether it’s enhancing user experience, creating new products or optimizing existing processes, generative AI offers a multitude of benefits.
While it’s true that tech leaders don’t necessarily need to go all-in immediately, not having a strategy around generative AI would be an oversight. Certain opportunities such as cost reduction and improved productivity through task automation or semi-automation can and should be addressed immediately.
To stay relevant and ensure competitive advantage, tech leaders cannot afford unnecessary delays in defining and shaping a generative AI strategy. CIOs need to ensure they are responsibly and effectively harnessing the power of generative AI at scale.
If they do, what aspects of Gen AI do you think will be most important to focus on, to ensure success in execution?
For most companies it would be most beneficial to focus on the capabilities of large language models in particular, and identify the key use cases in both internal operations and the enhancement of their products and services. The efficiency of developers can be greatly improved by utilizing LLM functionalities such as code generation, code explanation and debugging.
AI and data science efforts can also be significantly streamlined. Model building tasks, which under normal circumstances can take weeks or months to narrow down the choice of model and to tune, can be greatly accelerated using LLMs that narrow down applicable techniques as well as generating and debugging code.
Chatbots and AI assistants can help businesses leverage their existing products and services. LLMs can be instrumental in creating interactive interfaces and guiding users through multifaceted systems, ensuring that customers have a seamless experience.
Like any emerging technology, generative AI comes with its own set of challenges and risks, particularly concerning ethics, privacy and intellectual property.
Organizations need to adopt updated cybersecurity practices to protect data and models, as well as build infrastructure to monitor performance of generative AI models and track changes in behavior as versions of LLMs are updated. Organizations also need to conduct proactive risk assessments to identify vulnerabilities, and establish ethical guidelines, ensuring that generative AI implementations don’t compromise user rights or propagate biases.
How can CIOs work cross-functionally with both IT and non-IT leaders and teams to advance AI development?
There’s three specific ways that CIOs can work with both technical and non-technical teams:
- Have a well thought out project framework that addresses the business needs driving a project and how that maps to technical goals.
- Ensure that all functions are aligned with the goals, milestones and success criteria of the project. This is something that should be debated and clarified at the outset. Define checkpoints throughout the project timeline to track critical changes and whether or not that affects the project.
- Host regular check-ins where functions report on their progress, blockers and milestones. There should be an atmosphere where stakeholders outside of the function can comfortably ask for clarification on key items.
What are some of the ways that CIOs can create a culture of innovation within their companies?
Internal hackathons: Organize regular hackathons where employees can work on self-chosen projects, experiment with new technologies and solve real business challenges. Encourage the use of cross functional teams from diverse departments to approach problems from various perspectives. This can lead to holistic and out-of-the-box solutions.
Continuous learning: Offer training, workshops, informational lunch and learn sessions and courses that help employees acquire new skills and knowledge. Leverage experts within your company to demystify new technologies and get employees up and running with the new tech. In addition to technology overview and explanation, this can include some of the important skills necessary to utilize generative AI such as prompt engineering.
Structured time and resources: Allocate a portion of employees’ time to work on their own innovative projects. Google’s “20 percent time” policy, which allowed employees to work on side projects, is a famous example.
Foster a safe environment for failure: Understand that innovation often comes with risks, and not all ideas will succeed. Instead of punishing failures, treat them as learning opportunities.
Could you share best practices for increasing customer engagement with new technology solutions?
Before implementing any solution, thoroughly understand who your customers are, their level of technical sophistication, what they need and how they behave. This helps in tailoring solutions that truly resonate with them.
Ensure the solution is user-friendly and intuitive. A steep learning curve can deter users. Apply principles of user-centered design and prioritize the user experience.
Introduce guided tours, walkthroughs, and tutorials for new users. These can be in-app guides, video tutorials, or even webinars.
Actively solicit feedback from users through surveys, feedback forms or one-on-one interviews. Use this feedback for model improvement and continuous improvement of the general functionality.
Share testimonials, case studies, ROI metrics and success stories of users who benefited from the technology. This provides social proof and encourages others to engage.
