By Carmen Taglienti / 5 Jun 2024 / Topics: Generative AI Data and AI Analytics
With many AI tools on the market and more coming, knowing which best fits your needs is key to gaining the value this new initiative can offer.
In this blog, we’ll dive into three top AI companions, Copilot, ChatGPT and Gemini, and how businesses can select the right companion for their business case.
ChatGPT-4 is a low-cost, large language model (LLM) accessible for public use through Open AI. The tool introduced AI-powered experiences to the masses, and anyone can leverage it for various applications
For a lot of organizational leaders, while we're infatuated with this business AI concept, it’s more important to understand what you have, how you are going to use it and what you expect to get out of it. Only until we have answers to those questions can we pick the best tools and approaches.
With the right prompt engineering, ChatGPT-4 can be used to answer questions with impressive (but not always perfect) degrees of accuracy. The goal of ChatGPT-4 is to facilitate and eliminate everyday tedious tasks so users can focus their efforts on more important matters.
ChatGPT was created and released to the masses with the intention of public use. One of the key objectives of ChatGPT is to acquire new and more data to continue to improve the capabilities and fidelity of the model.
In turn, users have their information captured, posing potential risks when considering the sensitivity of said data. For this reason, businesses are less likely to use this platform on the chance that private business information is collected and used for ChatGPT’s enhancements. Even at the individual level, users should be careful not to plug in personal information.
As a publicized investor in Open AI, Microsoft built an internal version of ChatGPT tech for large-scale enterprises called Microsoft Copilot.
However, Copilot is more than just an internal integration of ChatGPT. Microsoft 365 Copilot is deemed the next-gen AI tool serving as a secure and responsible AI for businesses operating on Microsoft 365. Copilot works alongside users as a workforce productivity tool across Word, Excel, PowerPoint, Outlook, Teams and more.
Learn how Microsoft Copilot aims to unite the organization and increase productivity.
Microsoft Copilot was distinctively built for the enterprise in mind. As a paid internal service, Microsoft Copilot’s functionality securely integrates with your business data to offer tailored, custom-fit solutions based on your company’s and employee’s biggest needs.
Finally, Gemini is Google’s AI companion to improve productivity and creativity. Gemini is free to use for Google accounts and integrates with existing Google Workspace applications. Gemini brings Google Workspace customers Google’s most capable AI models, built into their familiar Workplace environment. Recently, Google extended its AI model to a wider audience with Gemini for Business and Gemini for Enterprise.
While Gemini Workspace is for anyone using Google Workspace, Gemini for Business and Gemini for Enterprise help organizations get started with leveraging generative AI for daily work. The AI model helps users create documents, fill out smart sheets, generate images for Google Slides and more.
Like Copilot, Gemini Workplace for business and enterprise aims to leverage internal data and the AI models are trained to provide helpful, contextual outputs.
Now that we’ve covered some of the top AI models on the market, leaders may wonder how to further identify which is best for them. Finding the right AI tool to leverage is based on your specific set of needs.
For example, if you are a business considering an enterprise-wide AI companion, it’s important to understand the advantages and limitations of various AI models. Similar to challenges in selecting cloud providers, businesses adopting AI can experience vendor lock-in with specific providers, lack of portability and more.
Check out the chart below outlining potential needs and AI companions that can meet them.
| Need | AI companion |
|---|---|
| Data flexibility and portability | Microsoft Copilot Gemini, Gemini for Business & Enterprises |
| Low Switching Costs | Microsoft Copilot Gemini for Business & Enterprises |
| Interoperability (communication between systems) | Microsoft Copilot Gemini for Business & Enterprises |
| Compatibility with M365 operating system | Microsoft Copilot |
| Compatibility with Google Workspace | Gemini, Gemini for Business & Enterprises |
| Quick, one-off brainstorming or information search | Microsoft Copilot ChatGPT, Gemini |
| Customer AI-enabled applications (chatbots, services, mobile/web AI apps) | Microsoft Copilot Vertex AI with Gemini (Google) |
For example, differences in data portability may be important to note before making your choice. Lack of portability can mean your data is harder to transfer in the event of a switch to another provider. Switching to a new AI companion can also be expensive. Like with switching to a new cloud provider, additional costs can be accrued during the move which can rack up expenses quickly.
So how can leaders navigate challenges and avoid vendor lock-in for the AI tools? For most businesses, it’s best to start with the end in mind.
Having built-in aspects in your AI initiatives to allow for future pivots will save you money in the long run. Planning holistically about costs — from the beginning — can keep you from having to make expensive pivots if you decide later on to switch vendors.
Leaning on a partner like Insight can help you holistically throughout your AI journey and connect you to the best-fit partner for your needs. Our tailored approach to AI helps businesses better understand their needs and match solutions that will provide sustainable value.
Chief Data Officer & Data/AI Portfolio Director and Distinguished Technologist, Insight
Carmen has more than 25 years of experience as a cloud computing, data science, data analytics and data management expert. As a principal cloud and AI architect for Insight, he focuses on cloud-based business solutions across multiple industries and technical domains. He also serves as an adjunct professor at Northeastern University Khoury College of Computer Sciences and the College of Professional Studies teaching graduate courses in data science, cloud computing and analytics. Carmen is a published author and speaks at industry and technology events.
