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Take This View to Assess ROI for Generative AI

By Jackie Wiles | 4-minute read | August 15, 2023

Big Picture

Rethink how to measure ROI of your GenAI investments

Generative AI promises unprecedented productivity improvements and business transformation opportunities, but calculating the value of new investments in GenAI requires you to build a business case by simulating potential cost and value realization across a range of GenAI activities aiming for a mix of:

  • Quick wins

  • Differentiating use cases

  • Transformational initiatives

GenAI quick wins focus on potential productivity improvements

  • These will likely come from productivity assistants, such as Microsoft 365 Copilot and Google Workspace. 

  • Such activities are easy to get started, pilot and buy but are usually task-specific, so measure and value time saved for both those specific tasks and across aggregate tasks related to specific processes — within specific time periods.

  • Productivity improvements alone may be a diminishing source of differentiation over time, but integrating these capabilities into other business processes can help enterprises maintain a competitive edge.

  • Time to value is short (less than one year).

Differentiating GenAI drives competitive advantage

  • Lean on use cases that leverage generative AI within industry or custom applications that allow you to leverage enterprise data in unique ways to extend current processes.

  • These differentiating initiatives provide a more defensible competitive advantage than quick wins but come with higher and more unpredictable costs and risk.

  • Direct and indirect financial benefits, including the potential for revenue generation, can generally offset costs, assuming effective underlying process redesign, upskilling and risk management.

  • Time to value is medium (between one and two years).

By 2025, growth in 90% of enterprise deployments of GenAI will slow as costs exceed value.

Source: Gartner

Transformative GenAI initiatives can upend business models and markets

  • Transformative use cases come with higher cost, complexity, risk and potential for technical debt. 

  • Ongoing innovations in GenAI are refining models and techniques and bringing down adoption costs; however, until lower-cost options emerge, innovators may have to accept difficult-to-quantify hard financial returns and higher cost, complexity and risk in exchange for first-mover advantage.

  • Investment decision criteria should prioritize strategic benefits that may be difficult to quantify in financial terms over immediately identifiable task- or process-specific financial benefits.

  • Time to value is long (more than two years).

By 2028, more than 50% of enterprises that have built large AI models from scratch will abandon their efforts due to costs, complexity and technical debt in their deployments.

Source: Gartner

The story behind the research

From the desk of Rita Sallam, Distinguished VP Analyst

“Business leaders need to build a portfolio of generative AI quick wins, differentiating and transformation use cases. Combine initiatives with hard ROI with loss leaders and those delivering transformation benefits and competitive advantages that are difficult to initially quantify directly in financial terms.”

3 things to tell your peers


Rank and prioritize GenAI quick wins by potential impact, cost and complexity, but know that most can be funded through cost savings and incremental productivity. 


For differentiating use cases to improve specific business processes, assess potential incremental costs and capture both financial benefits and strategic outcomes. 


Transformational GenAI initiatives require executives and boards to tolerate more risk and weigh strategic, competitive and market-level impacts in their investment decisions.

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Rita Sallam is a Distinguished VP analyst and Gartner Fellow in the Data and Analytics team. Mrs. Sallam's focus includes tracking market trends, vendor assessment and selection, and identifying best practices for realizing business value from data, analytics and AI investments. Of particular interest is how leaders can leverage disruptions in AI to create sustainable competitive advantage. She is also focused on building frameworks for selecting and valuing data and analytics portfolios including AI investments, including Generative AI.

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