With a strategy centered around AI, corporations like Apple, Nvidia, Google, Microsoft, Meta, Amazon, and Tesla have achieved valuations surpassing a trillion dollars. This AI-centric approach is also benefiting emerging companies like OpenAI and Perplexity, leading to spectacular valuations in the billions. Salesforce has reported a remarkable 30% boost in developer productivity, while Klarna, a Swedish fintech company, has noted a 20% increase in workforce productivity due to AI and the subsequent decreases in hiring requirements.
The return on investment for AI is evident, showcasing transformational advantages. As a universal “General Purpose technology”, AI is adding contextual, cognitive, and reasoning capabilities to numerous businesses.
It is no surprise that investments in AI rank among the top three priorities for CEOs, according to a recent survey of over 100 CEOs spanning more than 15 industries.
Understanding the AI-First Strategy
The AI-First strategy can be characterized as the continuous strategic ambition to harness data, algorithmic prowess, and execution strengths based on AI technology
Access to reliable, high-quality data reflecting the transactional and operational dimensions of a business, as well as the behavior of customers, is key to establishing a data advantage. This quality serves as the groundwork for training AI models. The superior the data quality, the greater the algorithmic edge.
The execution benefits gained by an AI-First organization include significant productivity enhancements, transformational experiences for customers, employees, and partners, along with fresh business models.
Thus, organizations striving to be AI-First endeavor to embed AI throughout their processes, products, and services.
Core Elements of an AI-First Enterprise
Over fifty percent of AI projects targeting business functions such as finance are anticipated to face delays or cancellation. Beyond a quarter of generative AI projects fail to progress past the Proof-of-Concept (PoC) phase.
The successful implementation of AI initiatives across enterprises depends on the alignment of the AI strategy with business goals and objectives. This requires diverse capabilities that encompass Processes, Technology, and People.
The following are the foundational elements upon which successful AI-First companies establish their capabilities:
The 3 A’s for Use-Case Categorization
The selection of use cases for integrating AI significantly influences the potential for an AI initiative to scale across the enterprise beyond the PoC stage. It’s crucial to determine how AI will be applicable to the end-user persona of the use case. Will it automate processes or tasks performed by the end-user? Will it enhance those tasks? Or will it facilitate entirely autonomous processes?
AI use cases could be classified into three categories: Automation, Augmentation, and Autonomous use cases.
Examples of AI automation use cases include intelligent document processing, text extraction, and chatbots. Many enterprises are comfortable with AI for automation.
Augmentation use cases necessitate collaboration between humans and machines, which brings about changes in “Ways-of-Work”. Consequently, AI literacy and a cultural transformation are essential for widespread acceptance across the enterprise.
There is significant buzz surrounding AI Agents—systems or programs capable of performing tasks independently on behalf of a user or system.
AI Agent use cases are most effective when applied to tasks or processes that can function autonomously, requiring no human input. Multi-Agent AI use cases pose additional technological complexity. Businesses are currently in the nascent stages concerning the implementation of Agentic AI, but we anticipate a rise in adoption and maturation within the coming years.
Expanding Beyond Large Language Models (LLMs)
Traditional AI requires a well-defined strategy for structured data, robust data quality governance, and effective tools and processes—alongside a cloud data platform—in addition to automation infrastructure. Amidst the excitement surrounding LLMs and their perceived ease of use, numerous businesses have come to assume that employing just an LLM will suffice to fulfill their generative AI needs.
A fruitful AI-First strategy must include a comprehensive Data Strategy that addresses the management of both structured and unstructured data, appropriate architectural patterns, as well as compute and storage strategies, including considerations around vector databases and Small Language Models, not forgetting Multi-Modal LLMs.
The AI-Flywheel Effect:
The AI-Flywheel effect refers to how data and AI can create a self-reinforcing cycle that fosters continual enhancement of outcomes.
The principle indicates that the more data utilized for training AI models, the improved their accuracy becomes. Higher accuracy enhances adoption, which in turn leads to an enriched user experience.
Successful AI-First companies develop systems and processes that capture every relevant aspect of user transactions, thus generating data to enhance AI Models.
Digital product and service firms construct their AI-Flywheels by instituting comprehensive logging mechanisms, while businesses offering physical products can develop their AI-Flywheels using data from sensors and Internet of Things (IoT) technologies.
Shifting from Hierarchy to Heterarchy:
AI-First organizations are nimble in responding to the dynamic needs of their environments. Both humans and AI agents collaborate towards shared objectives, resulting in numerous Human-AI agent interactions that necessitate intensive communications between humans, human-to-AI agent, and AI Agent-to-AI Agent.
A hierarchical organizational setup can slow decision-making and hinder agility. For this reason, an increasing number of AI-First companies are transitioning towards “Networks of excellence” and adopting a “Heterarchical” structure. According to Britannica, “heterarchy” describes a management style where any unit can rule or be governed by others depending on circumstances, thus avoiding the domination of any single unit. A heterarchical setup is characterized by flexibility and interdependent units with shared or distributed authority.
Cost and Risk-aware Framework
AI workloads demand expensive GPU-based computing infrastructure in addition to token-based usage of LLMs. AI Model outputs are probabilistic rather than deterministic, and they may carry regulatory risks. Thus, an AI strategy should encompass capabilities for cost and risk management. Mature AI-First enterprises maintain well-defined FinOps and AI Governance frameworks to ensure AI-related costs and risks remain optimal.
An AI-First enterprise could be a technology hyperscaler, a domain-specific niche company, or even a traditional corporation. The avenues through which AI can generate value will vary among enterprises. Nevertheless, strategies aligned with the aforementioned pillars can assist in establishing an AI-First endeavor, regardless of the enterprise’s type or dimension.
The author is Balaji Raghunathan, Data & AI Engineering Business Unit Leader at Sigmoid.
Disclaimer: The views expressed are solely those of the author, and ETCIO does not necessarily endorse them. ETCIO shall not be liable for any damage caused to any individual or organization, directly or indirectly.
