Exploring AI Technology Types

The world of artificial intelligence (AI) is multifaceted and will continue to expand with many new technologies coming to market. In reflecting back on the evolution of on-premise technologies and then later SaaS systems, we anticipate the rise of three predominant types of 3rd party AI applications: Purpose-built AI technology, Embedded AI, and AI Platforms. In general, each has its own benefits and drawbacks, providing businesses with tools to leverage AI's full potential. Understanding these types of technologies will help CFOs and IT leaders develop their AI strategies and plans. Let's delve into the specifics of these AI types, evaluating their benefits and drawbacks.

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1. Purpose-Built AI Technology

Definition:

These AI tools are tailored for specific uses like digital marketing, accounting, or data analytics, streamlining tasks and boosting automation in areas that were traditionally manual or cumbersome.

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Benefits:

• Speed to Market: Since they're designed for specific tasks, they can be developed, tested, and rolled out faster.
• Best Point Solutions: Offers tailor-made solutions for precise business requirements.
• Simplicity & Efficiency: Easily implemented and straightforward to use.
• Cost-Effective: Focused design means they might be cheaper than broader AI systems.

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Drawbacks:

• Narrow Scope: their applications will be more limited, especially at the outset
• AI Application Proliferation: a strategy of using these tools could result in a surge of these AI tools. Integrating data and processes becomes very challenging.
• Reduced long-term value: The disjointed nature of having many systems might hinder AI's long-term potential, which is being able to drive value across an organization’s functions and systems.

These AI tools will allow companies to quickly adopt AI and start reaping the rewards at a reasonable cost. However, these tools may limit AI’s long-term value if companies adopt too many of these point solutions.

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2. Embedded AI

Definition:

These AI tools are integrated into existing SaaS applications like Salesforce or NetSuite, enhancing their capabilities and streamlining operations.

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Benefits:

• Low Cost: companies will likely get access to these AI enhancements either freely or at a minimal cost.
• Compatibility: Designed for pre-existing systems, ensuring swift implementation.
• Quick Adoption: Being part of a familiar environment, users can quickly adapt to these tools.
• Added ROI to Current Tech: added AI embedded in current systems increases the value and uses of existing technology
• Limit New Systems: Adding AI to existing systems means fewer systems are added to the tech stack.

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Drawbacks:

• Value Limitation: The AI's utility is restricted to its parent system.
• Lack of Consolidation: Even though it doesn't add to the system's proliferation, it doesn't consolidate systems and data either, potentially limiting AI's value.
• Dependency on Existing Systems: Placing reliance on existing SaaS providers likely means AI capabilities will take longer to be available and may not match the user's needs.

These AI tools will allow companies to add AI to their existing tech stack and realize more value from their existing systems. They will also make the adoption of AI the easiest. However, it will likely take longer to get access to meaningful AI capabilities, and AI may be highly constrained as it will sit within the walls of pre-built systems with predefined uses.

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3. AI Platforms

Definition:

These are versatile AI systems that come with many capabilities and without a predefined use. They can be molded to fit across various organizational functions and processes.

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Benefits:

• Adaptability: The most flexible AI type, reshaping as per organizational needs.
• Holistic Application: This can be employed across multiple functions, making it a centralized powerhouse.
• Tech Stack Simplification: Might reduce or eliminate other systems, simplifying the technical landscape.
• Highest Long-Term Value: Having a single (or few) AI platforms means companies will be able to yield the full value of AI by leveraging many capabilities and data across the organization.

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Drawbacks:

• Higher Costs: The initial licensing might be pricier, although other expenses might be offset.
• Complex Implementation: With its broad scope, its integration might require more time, effort, and expertise.
• Time-to-Market: Due to its comprehensive nature, it’s expected that powerful AI platforms will take the longest to develop and bring to market.
• Depth vs. Breadth: Might not be as effective in specific areas as the purpose-built counterparts.

These AI tools will give companies the broadest and most expansive AI capabilities with the power to leverage the value of AI across the organization fully. However, these tools are some time away from being available and, when available, will likely require fairly large and complex programs to truly derive value from the capabilities offered.

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Conclusion

AI's future landscape is bound to be rich and varied, with each of the three types catering to different business needs. As CFOs and IT leaders develop their AI strategy, they should consider the above to help determine the path they should take to avoid creating an environment rich with AI but poor in automation and value. Executives should also create plans that are adaptable so they can course correct as new AI technology becomes available.

In navigating the dynamic landscape of artificial intelligence, understanding the nuances of Purpose-built AI, Embedded AI, and AI Platforms is crucial for informed decision-making. Each of these AI types offers a unique set of advantages and challenges, empowering businesses in distinct ways. To embark on this transformative journey, we encourage CFOs and IT leaders to seek expertise and guidance from specialists well-versed in these AI domains. At Connor Group, our AI subject matter experts stand ready to collaborate with you, providing practical AI solutions tailored to your organization's needs. By leveraging their insights and expertise, you can not only stay ahead of the AI curve but also build a powerful technology strategy that maximizes automation and value. As AI continues to evolve, let's embark on this exciting journey together, adapting and thriving in a world of boundless possibilities.

If you're seeking to develop a strategy or explore AI's benefits, contact Connor Group. We'll share our experiences and help you create a practical AI and automation roadmap.

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Authors

Jason Pikoos

Managing Partner, Client Experience

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Lauren Bowe

Partner, Automation and Analytics Leader