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  • Digital Economy Dispatch #178 -- A New Strategy for Delivering AI-at-Scale: AI Technology Arbitrage

Digital Economy Dispatch #178 -- A New Strategy for Delivering AI-at-Scale: AI Technology Arbitrage

Digital Economy Dispatch #178 -- A New Strategy for Delivering AI-at-Scale: AI Technology Arbitrage
7th April 2024

AI is rapidly transforming businesses, impacting nearly every facet of operations and promising to revolutionize product and service delivery. However, as AI adoption accelerates, a critical question emerges: Is your organization strategically positioned to leverage AI at scale?

This inquiry delves beyond mere implementation strategies. While deploying AI tools across the organization is crucial, a more fundamental shift is necessary: Transitioning from a labour-centric approach toward an AI technology arbitrage strategy. This strategy prioritizes excellence in AI deployment to use AI for creating enhanced value, rather than simply replacing human labour.

Beyond Labour Arbitrage: Unlocking the True Potential of AI

In discussions with most enterprises today, the conversations surrounding AI’s benefits usually focus on ways to replace human activity with AI-based alternatives. In many ways these discussions represent a continuation of the labour arbitrage approach to management that has dominated digital technology adoption over the past 20 years or more. From that perspective, organizations see digital technology as a way to enhance their competitiveness by using IT-based capabilities to enable efforts to manage costs using strategies such as outsourcing, offshoring, and process automation.

In this age of AI, many enterprise strategies seem to be stuck in this mode of thinking: AI is another digital technology wave aimed at reducing costs by automating tasks and replacing human workers. While AI undoubtedly offers opportunities for streamlining operations and optimizing resource allocation, solely focusing on this aspect risks overlooking its broader potential.

As AI technology matures, organizations must explore new avenues for achieving true competitive advantage. What many people now see is that a major source of differentiation is organizations capable of rapidly deploying, managing, and evolving their AI technology stack. This requires a pivot towards AI technology arbitrage, harnessing AI's capabilities to drive innovation, enhance productivity, and create sustainable differentiation in a time of continued digital disruption.

The Generative AI Era: Untapped Potential…Maybe

There is no doubt that a new generation of intelligent solutions, services, and devices powered by AI technology is emerging. However, effectively applying this technology to generate value remains a challenge. This is evident with the current wave of Generative AI (GenAI) tools such as ChatGPT, Gemini, and Claude. Their deceptively simple usage model obscures an important challenge: Applying AI to deliver enterprise value requires deep understanding and investment in acquiring, managing, and deploying a complex AI technology stack.

Every organization is experimenting with GenAI's capabilities. Yet, as noted by Karthic Krishnamurthy, simply deploying GenAI won't magically enhance customer service or optimize healthcare diagnosis. These tools require seamless integration with existing technologies, compliance frameworks, data systems, and workflows – many of which are fragile outdated, and complex.

Further complicating AI adoption is a recognition that the present-day AI stack is undergoing swift evolution, driven by changes in market structure and technology. Despite this rapid transformation, certain key components and digital leaders have already begun to surface. The emergence of these frontrunners signifies the unfolding narrative of an evolving AI landscape, diverging notably from the conventional machine learning development trajectory. At least 4 trends can be recognized in these more advanced AI deployments:

  1. Focus on Inference over Training: Most AI spending goes towards running pre-trained models (inference) rather than training new ones. This is because large, domain-specific models are expensive to train and maintain.

  2. Embrace Multi-Model Systems: Don't rely on a single model for everything. Use an ensemble of models to handle different tasks and improve controllability.

  3. Leverage Retrieval Augmented Generation (RAG): Enhance large language models (LLMs) with domain-specific knowledge using RAG. This technique utilizes external data sources to improve the LLM's performance.

  4. Democratize AI Development: Powerful pre-trained models allow regular developers to build AI applications without needing years of specialized ML training. This shift empowers full-stack engineers to work with data pipelines and integrate pre-trained models into applications.

The Service Team's Role in Orchestrating the AI Ecosystem

The responsibility for implementing this AI stack falls on service teams, encompassing both internal units and external partners. Their core mission is to leverage digital technologies for positive economic impact, driving innovation and expansion. Achieving this requires delivering economies of scale and expertise, ensuring efficient and cost-effective execution of high-quality work at scale.

This necessitates not only securing skilled talent at optimal costs but also maximizing workforce potential at a reduced expense. Mercer’s 2024 Global Talent Trends survey underscores these challenges. Surveying over 12,000 individuals worldwide, the report highlights a profound shift in the world of work, accelerated by GenAI. This new era brings both apprehension and excitement, with organizations grappling with AI's impact on productivity, risk, and competitiveness.

Leading the Way: Prioritizing People in the AI Landscape

The Mercer survey identifies a crucial focus as AI adoption expands: Prioritizing people to unlock the potential of this evolving landscape. Leading digital organizations are already aligning AI with their workforce, emphasizing four key priorities:

  1. Human-Centric Productivity: Recognizing the changing nature of work, these organizations prioritize understanding their workforce's skills and motivations. They view AI as a tool to augment human capabilities, optimizing human potential through redesigned work processes.

  2. Building Trust Through Transparency: Trust is the cornerstone of effective collaboration and innovation. Leading organizations cultivate transparency and equitable practices, fostering open dialogue and trust among stakeholders. Demystifying AI technologies and involving employees in decision-making processes instils confidence and fosters a culture of innovation.

  3. Enhancing Workforce Resilience: In this era of rapid technological advancement and uncertainty, building a resilient workforce is paramount. Organizations must equip employees with the skills and mindset necessary to adapt to evolving challenges and seize opportunities presented by AI.

  4. Simplifying for a Digital Future: Leading organizations recognize the inherent complexities of digital transformation and prioritize simplification and agility. By streamlining processes, fostering employee engagement, and promoting continuous learning, they can navigate the complexities of AI adoption and achieve long-term success.

Beyond Internal Realignment: The Rise of AI Technology Arbitrage

Achieving AI-at-scale necessitates a broader shift in perspective, moving towards AI technology arbitrage. This strategic approach utilizes AI to drive value creation across the entire business ecosystem. In this era, success hinges on leveraging AI not just as a cost-cutting tool but as a catalyst for innovation and growth.

Generative AI represents a significant shift in this regard, offering unprecedented opportunities for value creation and differentiation. As executives recognize its pivotal role in shaping the future of business, organizations must align their strategies and workforce development initiatives to effectively harness the transformative power of Generative AI.

Orchestrating the Ecosystem: The Role of Service Providers

Central to AI technology arbitrage is the orchestration of a dynamic ecosystem of AI technologies and stakeholders. While enterprises possess internal expertise, the complexity and rapid pace of AI innovation necessitate strategic partnerships with external service providers. These partners play a crucial role in orchestrating the Generative AI ecosystem, enabling enterprises to capitalize on emerging opportunities and stay ahead of the innovation curve.

Forcing this change is a recognition that the focus has shifted from mere cost reduction to value creation, with predictive analytics and personalized experiences emerging as key drivers of competitive advantage. Service providers that can effectively orchestrate the technology ecosystem and unlock the power of prediction and personalization will be the true winners in the AI Technology Arbitrage era.

A Strategic Strategy Shift

Despite the allure of Generative AI tools such as ChatGPT, Gemini, and Claude, enterprises are finding that adopting AI-at-scale is neither simple nor quick. The journey towards AI-at-scale demands a strategic shift towards a new approach based on AI technology arbitrage. By leveraging AI to drive innovation, enhance productivity, and create sustainable value, organizations can differentiate their offerings and position themselves for success in this new landscape. By aligning their priorities with the evolving nature of work and fostering a culture of innovation and collaboration, digital leaders can ensure their organizations thrive in an increasingly AI-driven world.