Breakneck innovation in artificial intelligence has made it harder than ever to compare generative AI tools and make the right choice for your organization. Today, we have Gen AI solutions for virtually every use case.
There are solutions that can create content, from blog posts, to code and videos. Gen AI systems can enhance the customer experience, powering innovative chatbots. Some companies are even designing Gen AI solutions for specific tasks and industries, such as “security copilots”.
As the market for generative AI accelerates towards a projected value of $1.3 trillion by 2032, the options for business leaders will only increase. Here’s how you can compare your options and make the best decision for your company.
Step 1: Compare Generative AI Use Cases
Before you can start to compare generative AI tools, you need to determine what you want to accomplish with the technology. As mentioned above, the use cases for Gen AI are practically endless. AI solutions can enhance cross-functional collaboration, transform project management, and even optimize customer service. Some common use cases to consider include:
- Team productivity: Using Gen AI to enhance staff productivity and efficiency, with automated solutions, and assistants like Microsoft Copilot.
- Content generation: AI solutions for developing, optimizing, and enhancing written, audio, visual, and other forms of content.
- Customer service: Generative and conversational AI solutions for chatbots, virtual assistants, 24/7 self-service, and IVR enhancements.
- Insights and analysis: Using Gen AI tools to collect, analyze, and learn from vast amounts of data about customers, market trends, and competitors.
- Product development: Leveraging generative AI to assess product options, create blueprints, and design new solutions for customers.
Step 2: Explore Deployment Options
There are countless ways to tap into the benefits of generative AI tools today, depending on your specific needs, and available resources. The most affordable (and easiest) option is usually to explore public pre-built solutions, like Google Gemini.
These solutions are easily accessible and generally require minimal coding knowledge. Some solutions, like Microsoft Copilot, can even be infused with your own data. However, they usually give companies limited control over their models.
Alternatively, open-source solutions allow companies to build proprietary AI tools specific to their needs. Companies can also work with developers to create solutions from scratch. While this offers more control, it also requires additional resources, time, and expertise.
On top of that, today’s enterprises can also explore a range of pre-built, and somewhat customizable models intended for specific industry verticals, such as Gen AI tools built for the healthcare, finance, or security landscape.
Step 3: Compare Generative AI Tools Based on Customization
Regardless of which deployment method you consider when you compare generative AI tools, ensuring there’s some scope for customization is crucial. Generative AI solutions are more secure, effective, and valuable when they can be fine-tuned and enhanced with unique data.
Looking for robust enterprise-grade generative AI models that support customization, like Microsoft’s Copilot Studio, ensures you can tailor your solution to your use cases. Some more advanced solution even come with unique orchestration systems and workflow builders that make it easier to create custom bots with minimal code.
In addition to ensuring you can customize the data your model uses, look for other personalization options. Check to see whether you can change the tone or language output of your model. Find out whether your system can integrate seamlessly with the tools you use. An AI system that can connect seamlessly with project management, customer service, and collaboration apps, delivers higher ROI.
Step 4: Explore Scalability and Performance
It can be difficult to compare generative AI tools based on performance. How well your solution runs depends on numerous factors, including its access to data and computing power. Many vendors, like Microsoft, Google, and OpenAI do share reports with benchmarking data to help you make your decisions. However, it’s worth remembering these tools are constantly evolving.
