Introduction

When driving value from generative AI (GenAI) it’s important to choose the right approach in order to be able to get a return on investment. This page attempts at explaining possible approaches and required resources.

There seems to be 3 major categories of GenAI adopters:

McKinseyGartnerDescription
TakersQuick WinsFocus on utilizing existing GenAI tools and models for productivity improvements with minimal customization.

These initiatives typically have short time to value and are task-specific, aiming for immediate efficiency gains in routine tasks.
ShapersDifferentiating Use CasesEngage in integrating GenAI tools with proprietary data or adapting them for specific applications.

These initiatives aim to achieve competitive advantages, involving medium time to value with higher costs and risks than quick wins.

They leverage GenAI to extend current processes and create unique value propositions.
MakersTransformative InitiativesConcentrate on developing new GenAI models or tools for specialized applications, with the potential to transform business models and markets.

These are the most ambitious initiatives, characterized by high cost, complexity, and risk, and a long time to value.

They aim for strategic benefits that may be difficult to quantify initially.

TCO/ROI

The Total Cost of Ownership (TCO) and Return on Investment (ROI) for GenAI adoption across takers, shapers, and makers categories involve several considerations, including hidden costs, strategic implications, and potential benefits.

Gartner offers insights on measuring GenAI ROI, advocating for a business case approach that simulates potential cost and value realization across GenAI activities. This approach categorizes investments into quick wins, differentiating use cases, and transformational initiatives. Quick wins focus on immediate productivity improvements with short time to value, differentiating use cases aim at competitive advantage with medium time to value, and transformative initiatives have the potential to upend business models with longer time to value but higher costs and complexity. The guide emphasizes the importance of balancing financial returns with strategic benefits, which might be difficult to quantify initially.

https://www.gartner.com/en/articles/take-this-view-to-assess-roi-for-generative-ai red box is added by me, see conclusion below.

AI Builders

I’m introducing an extra “Builders” category into the GenAI adoption landscape beyond merely adopting or adapting, Builders take a step further by crafting bespoke extensions and plugins for GenAI platforms. This initiative is driven by the ambition to tackle intricate, multi-step workflows that typically demand considerable human intervention. The essence of being a Builder lies in their ability to not just work with GenAI but to enhance its core capabilities, enabling solutions that seamlessly bridge various systems and processes. This approach demands a blend of creativity, technical prowess, and a deep understanding of both the technology and the problem domain.

CategoryDescriptionRequired People Resources/SkillsTools
TakersUtilize existing GenAI tools for productivity improvements with minimal customization.

Aimed at immediate efficiency gains in routine tasks with short time to value.		Basic understanding of AI/ML conceptsSkills in integrating and configuring APIs

Ability to adapt third-party GenAI tools to existing workflows		Microsoft Copilot

Microsoft Copilot Plugins

Enterprise “Chat”-GPTs
ShapersIntegrate GenAI tools with proprietary data or adapt them for specific applications to achieve competitive advantages, involving medium time to value with higher costs and risks.Low/No-code developers

Domain experts for data interpretation

Project managers with a technical background		Retrieval Augmented Generation (RAG)

Microsoft Copilot Studio

Microsoft Azure AI Studio
BuildersDevelop custom solutions or extensions to GenAI platforms to solve complex, multi-step processes that usually require significant human effort.Advanced programming skills in relevant languages

Data scientists for model tuning

Experience with GenAI frameworks

Systems integration expertise

Creative problem-solving abilities		Microsoft Copilot Extensions

Microsoft PromptFlow

LangChain

LlamaIndex

AutoGen

CrewAI

LLM Function Calling
MakersDevelop new GenAI models or tools for specialized applications with the potential to transform business models and markets.

Characterized by high cost, complexity, and risk, with a long time to value.		Expertise in deep learning and neural networks

Experience in building and training large-scale AI modelsStrong research and development background

Ability to work with high-performance computing resources		LLM Frameworks

The “Builders” category fills the gap between “Shapers,” who mainly adapt existing models for their unique needs, and “Makers,” who create new GenAI models from scratch. Builders leverage powerful frameworks and platforms to create bespoke solutions that automate complex workflows, potentially revolutionizing how businesses approach process automation and efficiency. This distinction underscores the evolving landscape of GenAI adoption, highlighting the increasing sophistication and customization capabilities available to organizations.

Conclusion

The red box on the image above indicates that solutions made in the Takers and lower Shapers category are likely to be overtaken by standard solutions from vendors and the plethora of SaaS AI offerings appearing on a daily basis. Caution should be used when choosing to invest in solutions in this area unless quick wins are important.

Clearly it’s important to have a strategic, well-planned approach to integrating GenAI with emphasis on organizational readiness, skill development, and a focus on applications that offer a competitive advantage – otherwise GenAI just becomes a technology looking for a problem like Blockchain.

References