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.

From a scalability perspective, it’s important to ensure your generative AI platform can handle large-scale deployments with ease. The best solutions should be able to process vast amounts of data, serve multiple concurrent users, and scale dynamically based on demand.

Depending on your needs, you may consider a platform with advanced performance optimization techniques. For instance, some solutions offer model compression, quantization, and caching features. It may also be helpful to think about flexibility in implementation options. For example, will your model support both on-premises and cloud environments?

Step 5: Compare Generative AI Tools Based on UX

Generative AI solutions can only have a positive impact on your business if people actually want to use it. Although adoption of AI is increasing (with three quarters of knowledge workers using AI at work), focusing on UX is still important. For employee-facing tools, look for user-friendly interfaces, and no-code drag-and-drop workflow builders to enable customization.

Check for built-in features that allow employees to use their tools more effectively. For instance, the Gen AI prompt templates you create in some Copilot-style tools can be easily shared with other team members. This reduces the need for individual prompt engineering.

For customer-facing solutions, prioritize tools that are easy to navigate, and use straightforward language in their responses. In both cases, make sure you search for a platform vendor with an excellent approach to customer success, and technical support.

Step 6: Priortize Compliance, Governance and Security

Finally, focusing on compliance, security, privacy, and governance is crucial when you compare generative AI tools. That’s particularly true now that new AI governance standards are emerging in regions all over the world. To avoid issues with ethical and data privacy mandates, look for a platform that’s explainable, transparent, and controllable.

Make sure you can easily secure the data shared with your AI model at all times, and fine tune responses when necessary to mitigate AI bias and discrimination. Look for advanced model evaluation tools built into the system. This will help you to easily analyze the performance of your technology across applications.

Additionally, ensure your platform vendor adheres to all the security standards relevant to your business. This could include searching for compliance with PCI, GDPR, HIPAA, SOC 2, and more.

Choosing your Generative AI Path

Demand for generative AI will only continue to increase in the years ahead. McKinsey believes that generative AI’s impact on productivity alone could add trillions of dollars in value to the economy. However, for companies keen to tap into the benefits of generative AI, choosing the right solution is crucial.

Think carefully about your use cases, priorities, deployment options, and customization requirements. Ensure your models are scalable and efficient, and don’t forget to prioritize security, compliance, and governance.

 



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