With an AI-First strategy, companies like Apple, Nvidia, Google, Microsoft, Meta, Amazon, Tesla etc. have achieved Trillion dollar plus valuations. AI-First strategy is also helping newer companies like OpenAI, Perplexity command eye-popping valuation running into Billions of dollars. Salesforce has announced a stunning 30% improvement in developer productivity and Klarna, a Swedish Fintech, has announced 20% improvements in Workforce productivity due to AI and the resultant reduction in their hiring needs.

AI has proven its ROI when it comes to transformational benefits. Being a horizontal “General Purpose technology”, AI is adding contextual, cognitive and reasoning capabilities to businesses.

No wonder, investments in AI feature among the top 3 CEO priority areas as per a recent CEO pulse survey covering 100 + CEOs cutting across 15+ industries,

According to Mika Ruokonen and Paavo Ritala's article in Emerald Insights 'Journal of Business Strategy ', AI-first strategy can be defined as the persistent strategic intent to seek data, algorithmic and execution advantages built on AI technology

Trusted, high-quality data capturing the transactional and operational aspects of the business and behavioral aspects of the customers provide the data advantage. They serve as the underlying foundation for training AI Models. Better the quality of data, the higher the Algorithmic advantage.

The execution advantage achieved by an AI-First enterprise include outsized productivity benefits, experience transformation for customers, employees and partners as well as new business models.

Thus, businesses focused on becoming AI-First, look at embedding AI across their processes, products and services.

More than half of AI projects targeted for Business Functions like Finance are expected to get delayed or shelved. More than a quarter of Gen AI projects do not go beyond the PoC (Proof-of-Concept) stage.

Implementing AI initiatives successfully across the Enterprise is dependent on how aligned AI strategy is with business objectives and goals. It needs multi-dimensional capabilities cutting across Process, Technology and People.

The following are the pillars around which successful AI-First companies build their capabilities:

Choice of the use case for embedding AI goes a long way in deciding the likelihood of the AI initiative scaling across the enterprise beyond PoC stage. It is important to decide how AI will be relevant to the end user persona of the use case. Will it automate processes or tasks performed by the end user persona? Will it augment the tasks which the end user persona performs? Will it support autonomous processes and tasks?

AI use cases can be categorized into 3As, namely, Automation, Augmentation and Autonomous use cases.

Intelligent document processing, text extraction and chatbots serve as examples of AI automation use cases. Most enterprises are comfortable with AI for Automation.

Augmentation use cases requires Human-Machine collaboration. This requires changing “Ways-of-Work”. Hence AI Literacy, and culture change are pre-requisites for adoption across the enterprise.

There is a lot of hype today w.r.t AI Agents, a system or program that can perform tasks autonomously on behalf of a user or another system.

AI Agent use cases are successful when applied to tasks and/or processes which can be autonomous, i.e. where no human intervention is needed. Multi-Agent AI use cases are also technologically complex. Enterprises are in early-stage w.r.t Agentic AI implementation. However, we should see more enterprises adopt agents and mature over the next couple of years.

Traditional AI needs a well-defined structured data strategy, data quality and governance tools and processes, cloud data platform apart from automation infrastructure. Due to the hype around LLMs and their ease of use, many businesses tend to believe that just using an LLM is sufficient to address their Generative AI needs.

Successful AI-First strategies need a Data Strategy revolving around structured and unstructured data management, the right architectural patterns, compute and storage strategy inclusive of vector databases and Small Language Model choices apart from Multi-Modal LLMs.

AI-Flywheel effect is about how data and AI create a self-reinforcing loop for continuous improvement of outcomes.

The idea is that more the data available for training AI models, better their accuracy. Better accuracy leads to better adoption. Better adoption leads to better end user experience.

Successful AI-First businesses create Systems and Processes which persist every relevant portion of end user transactions, thereby generating data to train the AI Models.

The digital product and services companies create their AI-Flywheels by setting up enough logging mechanisms. The enterprises selling physical products can create their AI-Flywheels leveraging data generated by sensors and internet of things (IoT) technology

AI-First businesses are agile to evolving needs of their environment. There will be both humans and AI agents working together to achieve a common goal. They will have a high number of Human-AI Agent touchpoints which require intensive human-to-human, human-to-AI agent and AI Agent-to-AI Agent interactions.

A hierarchical organizational structure delays decisions and restricts agility. This is why an increasing number of AI-First enterprises are moving to a “Networks of excellence” and “Heterarchical” structure. As defined by Britannica, “heterarchy” is a form of management or rule in which any unit can govern or be governed by others, depending on circumstances, and, hence, no one unit dominates the rest. A heterarchy is a flexible structure made up of interdependent units and authority within a heterarchy is distributed6.

AI workloads need costly GPU-based compute infrastructure apart from usage of LLMs which are token-based. AI Model outputs are probabilistic rather than deterministic and can have associated regulatory risks. Thus, the AI strategy should also include capabilities for cost and risk monitoring. Mature AI-First enterprises have well defined FinOps and AI Governance setups and frameworks to keep the AI-associated cost and risk optimal.

An AI-First enterprise can be a technology hyperscaler or a domain-focused niche company or even an old-economy company. The processes, products and service where AI can realize value for each enterprise will thus differ. However, a strategy revolving around the above pillars can help implement an AI-First business irrespective of their type or size.