Generative AI

AI in Business: The Value of an Orchestrated Approach

Long before the advent of ChatGPT, we’ve advocated for AI’s potential to transform any business — even in highly regulated industries like healthcare and finance. Optimization algorithms, new training paradigms, summarization tools, and copilots can enhance the experience of our teams, clients, and patients.

Artificial Intelligence Will Impact Every Facet of Our Organizations

We’re confident in the value that AI tools offer, as long as we ensure they’re safe, flexible, and scalable.

We also know firsthand the pressure facing cross-industry CIOs and CTOs as they navigate more recent advances in generative artificial intelligence: pressure to find early wins, pressure to move quickly, pressure to find the right focus areas, and of course, pressure to keep data safe and secure.

As we help our healthcare partners and cross-industry clients navigate and deliver in this complex environment, here are a few common, but critical questions we’ve encountered:

1. How can we stay nimble in such a rapidly changing environment?

Every day, businesses discover new ways to use AI — from chatbots to content generation to copilots and more. Almost every month, major players in the AI market release more powerful models and technologies.

What if the tools you invest in today are obsolete in a year, or even sooner? How can you avoid locking your solutions into specific vendors or specific models?

As you scope out your AI use case portfolio, aim to avoid vendor lock-in. Ensure your AI apps are built with core components (large language models, vector databases, etc.) that are exchangeable based on which models (OpenAI, Gemini, Llama, Anthropic, Med-PaLM, etc.) best serve a particular need.

2. Should we build or buy our AI capabilities?

The simple answer is yes, and yes. Every organization brings unique business processes, workflows, content, and culture that can drive some of the most compelling ideas for using AI — some of which will require more custom solutions and others that will align with out-of-the-box, templated offerings.

Let’s first consider built solutions. We’ve had the opportunity to work with our healthcare partners on these types of projects, like using voice tech and automation to alleviate staffing shortages and burnout.

Remember that this “build-AND-buy” strategy applies to any industry: beyond health and wellness, we’ve built impactful, bespoke experiences within highly regulated environments, such as this secure conversational AI assistant in the financial services industry.

On the other hand, consider what might be possible with more templated solutions. There are common use cases that span organizations and industries, such as natural language processing capabilities like text-to-voice or voice-to-text.

Take our work on SPOCBot as an example of an out-of-the-box AI app. It’s a generative AI-driven chatbot that responds to inquiries from IT specialists as they resolve a wide variety of customer service inquiries and employee requests — from changing passwords to requesting new hardware.

This tool combines a wide range of sources (internal websites, portals, potentially disorganized collections of PDFs, Word Docs, etc.) into a single experience. We have used and stress-tested this tool across TELUS, with over 100,000 global employees, and there are clear cross-industry use cases.

The path to optimizing value in AI is making sure you’re ready to build AND buy.

3. How can I ensure security and safety across use cases?

Once you have both out-of-the-box and custom solutions deployed across use cases, perhaps the most daunting challenge is how to create common tools and frameworks that ensure your AI services meet compliance, regulatory, and governance requirements — as well as your organization’s objectives.  

As you consider your governance framework or content moderation policies, it’s critical to ensure that security and monitoring are not implemented differently across 20, 30, or even more AI applications.

Safe, secure AI experiences must enact these standards in their enablement layers — their foundation — and at the application layer.

With tools implemented at an enterprise level, you can effectively manage your generative AI services over time, such as benchmarking and monitoring to proactively mitigate risks from hallucinations and drift. (If you are interested in going deeper into how we approach AI security, check out our defense-in-depth approach as well as other insights from our AI Knowledge Hub.)

The solution? Deploy a flexible AI management layer

While each of these challenges requires a range of solutions, we’ve seen one common theme emerge: flexibility and orchestration are the foundation of any successful AI strategy.

Here at WillowTree, we’ve developed a foundational AI management layer that provides the adaptability and scalability needed in the long term. We’re seeing tremendous value both internally and with our clients — and we’d love to share our learnings with you.

Let’s connect at ViVE

I’m excited to be at ViVE this coming week and would love to explore this topic with you and hear how your organization is approaching AI as a competitive advantage.

Whether you’re looking to deploy flexible AI infrastructure, rapidly ideate and launch a generative AI-driven product through our GenAI Jumpstart Accelerator, or want to learn how to scale your AI efforts, let’s connect.

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