AI tools vs AI solutions: What’s the difference and why should you care?

The rise of generative AI in enterprise strategy: MIT research suggests CIOs should adopt a two-tier approach to generative AI implementation, distinguishing between AI tools for productivity enhancement and AI solutions for strategic business transformation.

• MIT’s Center for Information Systems Research (CISR) emphasizes the need for separate strategies when deploying productivity-enhancing AI tools versus business-case-driven AI solutions.
• The research was prompted by IT leaders questioning why they weren’t seeing the same value from generative AI as they had from previous data and analytics technologies.
• A one-size-fits-all approach to AI implementation may be limiting the potential benefits for organizations.

AI tools as gateways to broader adoption: Productivity-enhancing AI tools like ChatGPT and Microsoft Copilot serve as important stepping stones for organizations to build familiarity and comfort with AI technologies.

• These tools help employees become more comfortable and creative in using AI, potentially leading to increased innovation in more complex AI solutions.
• AI tools are seen as mechanisms for building “data democracy” within organizations, uplifting employee skills and fostering innovation.
• However, guidelines and training are crucial to ensure safe and effective use of AI tools, especially considering potential data privacy concerns.

The challenge of measuring AI tool impact: CIOs have found it difficult to fully quantify the benefits of AI tools, as time savings are often distributed across multiple small tasks.

• One executive described AI tools as productivity “shaves,” saving users a few minutes on each task by summarizing documents or assisting with email drafting.
• The cumulative effect of these small time savings can be significant but may be challenging to measure accurately.

Strategic implementation of AI solutions: In contrast to AI tools, AI solutions address specific business needs and require a more formal and structured approach to implementation.

• Examples include large language models (LLMs) used in contact centers to analyze conversation content and tone, providing real-time coaching to agents.
• Organizations deploying AI solutions should create formal AI innovation processes and prioritize competitive differentiation through customization.
• Clear governance structures, early stakeholder engagement, and a focus on scalable solutions are essential for successful AI solution deployment.

Industry perspectives on the two-tier approach: Technology leaders in various sectors agree with the distinction between AI tools and solutions, emphasizing the need for different implementation strategies.

• Dhaval Gajjar, CTO of Textdrip, highlights the importance of user training for AI tools and a structured, cross-functional approach for more complex AI solutions.
• Moe Asgharnia, CIO of BPM, notes that while the underlying technology is the same, the application and use cases differ between AI tools and solutions.
• Both leaders agree that AI tools can serve as stepping stones for more complex AI implementations in the future.

Measuring success and aligning with business goals: Organizations should evaluate the success of both AI tools and solutions based on their immediate impact and alignment with long-term business objectives.

• The scope and complexity of each AI implementation directly influence how they should be rolled out and measured.
• A balance between short-term productivity gains and long-term strategic alignment is crucial for maximizing the value of generative AI in the enterprise.

Looking ahead: The evolving role of AI in enterprise strategy: As organizations continue to explore and implement generative AI technologies, the two-tier approach may evolve to address new challenges and opportunities.

• CIOs and IT leaders will need to stay adaptable, continuously refining their AI strategies to balance immediate productivity gains with long-term transformational goals.
• The interplay between AI tools and solutions may lead to new insights and use cases, driving further innovation in enterprise AI adoption.