Prepare to value your mix of GenAI investments based on outcomes and risk. Leverage pilots to assess value and cost in addition to technical feasibility.
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 uses cases and transformational initiatives.
Quick wins for GenAI
To ensure quick wins for GenAI, 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 the 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.
Differentiating GenAI drives competitive advantage
Gartner predicts that by 2025, growth in 90% of enterprise deployments of GenAI will slow as costs exceed value. Lean on using 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 risks.
Direct and indirect financial benefits, including the potential for revenue generation, can generally offset costs, assuming effective underlying process redesign, upskilling and risk management.
Transformative GenAI initiatives can upend business models and markets
Gartner Predicts 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. 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.
Business leaders need to build a portfolio of generative AI quick wins, differentiation 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.
(This article is authored by Rita Sallam, VP Analyst at Gartner)
