Executive summary
The business world stands at yet another major turning point. While most organisations tinker with AI tools around the edges of their operations, a new breed of company has emerged that places AI at the very centre of how they operate, compete, and grow. These AI-first companies aren’t just using technology differently – they’re redefining what’s possible in terms of scale, efficiency, and innovation.
- AI-first companies achieve 25-35x higher revenue per employee than traditional organisations
- 42% of enterprise-scale companies now actively deploy AI, with early adopters accelerating investment
- Leading organisations earn US$3.50 for every US$1 invested in AI technology
- Companies must implement AI within 12 months to remain competitive, according to industry leaders
- Autonomous business operations will emerge between 2025-2028 across multiple sectors
- Market consolidation will favour AI-first companies as competitive gaps become insurmountable
The evidence suggests we’re witnessing the emergence of a fundamentally different type of organisation. The question facing every leader today isn’t whether to embrace AI, but whether they can afford not to become AI-first before their competitors do.
Every industrial revolution has its converts and its natives. When electricity transformed manufacturing, some factories simply swapped steam engines for electric motors and plowed onward, preserving their old layouts and workflows. Others rebuilt from scratch, reimagining production around electricity’s unique properties – continuous power, precise control, and distributed energy. You could argue that the latter group didn’t just adopt electricity; they thought electrically. Today, we’re watching a similar divide emerge with AI.
The release of ChatGPT to the public in late 2022 did more than demonstrate AI’s capabilities. Rather, it raised a mirror to every organisation and asked an uncomfortable question: are you built for a world where machines can think? For most established companies, the honest answer revealed layers of human processes designed for an era when intelligence was scarce and commanded a premium. But for a vanguard of new organisations, this moment represented validation of a bet they’d already placed: that competitive advantage would flow to those who built their companies as if AI had always existed.
The implications ripple outward in unexpected ways. AI-first companies measure productivity differently, hire for different skills, and compete on different dimensions. They treat data not as exhaust from business operations, but as the fuel that powers every decision. Most tellingly, they view the current moment not as a period of disruption to be weathered, but as the early days of the new normal – a normal they’re actively building. Understanding these organisations isn’t just academic curiosity. They’re writing the rules that others will eventually follow, pioneering the practices that will seem obvious in hindsight, and demonstrating what becomes possible when we stop asking AI to fit into our world and instead rebuild our world around AI’s capabilities.
The great acceleration
A growing number of companies are moving from tentative experiments with AI to being truly AI-first.
The journey toward AI-first organisations unfolded across three distinct phases. The foundation phase saw companies experimenting with pilot programmes while simultaneously grappling with basic questions about integration and governance. Traditional rule-based systems began giving way to sophisticated applications powered by large language models, but most companies remained tentative in their approach, treating AI as more of an interesting experiment than a strategic imperative. During the experimental phase, companies moved beyond simple automation to explore AI’s potential for creating wholly new business models and revenue streams. Many established their own AI research and development centres and introduced dedicated leadership roles, signalling that AI had moved from the IT department to the boardroom.
The transformation phase represents our current reality. A growing number of companies are now embedding AI into core business operations, shifting from AI-enhanced to something approximating a true AI-first approach. This phase is the bumpiest, demanding fundamental changes to operating models, workforce structures, and competitive strategies. IBM’s Global AI Adoption Index reveals that 42% of enterprise-scale organisations now have AI actively deployed in their businesses, with an additional 40% actively exploring the technology. Meanwhile, early adopters aren’t slowing down – 59% are actually accelerating their AI rollout and investments, suggesting they’re experiencing tangible benefits that justify the continued investment.
What makes a company truly AI-first
Is your company merely AI-enabled or truly AI-first? Here’s how you can tell and why it matters.
So, what exactly is the distinction between AI-enabled and AI-first companies? While AI-enabled companies integrate AI into existing processes, allowing them to achieve incremental improvements in productivity and capability, AI-first companies rebuild their entire operating models around intelligent automation, creating entirely new possibilities for scale, speed, and competitive advantage. AI-first companies are characterised by pervasive integration, whereby AI is woven into every business function rather than remaining confined to specific departments. This comprehensive approach ensures AI capabilities enhance all aspects of operations, from product development and human resources to customer service and finance.
Perhaps the most significant cultural shift involves the adoption of data-driven decision making. AI-first companies use vast amounts of data to inform their strategic decisions, optimise operations, and predict market trends with singular accuracy. This approach moves beyond intuition-based leadership to evidence-based strategies that enable companies to better adapt to changing conditions in real time. Thanks to autonomous workflows, AI-first companies can handle routine processes with minimal human intervention, but it’s important that automation extends beyond simple task completion to complex decision-making scenarios. This enables AI-first companies to dramatically reduce their costs and improve response speeds in a way that traditional companies often struggle to match.
Shopify’s Chief Executive, Tobi Lütke, perhaps best exemplifies this thinking with his company-wide AI-first mandate. “Before asking for more headcount and resources, teams must demonstrate why they cannot get what they want done using AI,” he says. This approach fundamentally reshapes how teams approach problem solving and resource allocation, forcing innovation and ensuring AI adoption becomes embedded in daily workflows rather than remaining a separate initiative.
The anatomy of AI-first architecture
What are the five foundational elements that enable the transformational capabilities of AI-first companies?
Successful AI-first companies construct their operations on five foundational elements that work together to create transformational capabilities. Intelligent data infrastructure forms the backbone, including real-time data pipelines, robust security frameworks, servers, and scalable storage solutions that handle the massive data requirements of modern AI systems. The second element involves AI-native applications, which represent a fundamental departure from traditional software retrofitted with AI capabilities. These applications evolve and improve autonomously, learning from user interactions and market changes.
For instance, Netflix uses AI-driven personalisation that analyses millions of user actions to create sophisticated preference profiles. By 2024, more than 80% of what users watched on Netflix was discovered through personalised recommendations. Besides content recommendation, Netflix also uses AI for production decisions, analysing viewing patterns and market trends to inform investment decisions about new shows and movies. The company’s AI systems help predict which content will resonate with specific audience segments, enabling more targeted and successful content development.
Next, adaptive governance models provide necessary oversight while remaining flexible enough to accommodate the dynamic nature of AI systems. These frameworks balance innovation with responsibility, ensuring that AI deployment across organisations remains ethical. The challenge lies in creating governance structures that enable rapid experimentation, while maintaining clear risk management standards. Just as importantly, human-AI collaboration frameworks define how employees and AI systems work together most effectively. Rather than replacing human workers, these frameworks should be designed to maximise human capabilities while automating routine tasks. Tesla’s approach demonstrates how this integration might work in practice, using AI for manufacturing optimisation, quality control, and product development acceleration while humans focus on strategic decisions, high-level design and engineering work, and creative problem solving.
Finally, continuous learning systems enable companies to evolve constantly, incorporating new data, feedback, and technological advances to improve their performance over time. Amazon’s evolution shows how AI can become foundational to business operations through systematic integration across multiple domains: personalised customer experiences, operational efficiency through AI-powered logistics, and new business models through AI-enabled services.
How to build an AI-first company
Real-world examples reveal the blueprint for comprehensive AI-powered transformation.
Microsoft’s comprehensive AI transformation demonstrates how even established enterprises can successfully pivot to become AI-first while leveraging their existing customer relationships and infrastructure advantages. Under the leadership of Chief Executive Satya Nadella, Microsoft invested over US$13bn in OpenAI while embedding AI capabilities across its entire product portfolio. The company established dedicated AI research divisions, restructured teams around AI-first principles, and implemented new performance metrics that prioritise AI-driven value creation. As a result, Microsoft became one of the world’s most valuable companies, with AI serving as the primary growth driver. The company’s approach demonstrates how AI-first mandates can drive both efficiency and innovation when implemented with proper leadership commitment and organisational alignment.
Spain’s BBVA became the first European bank with a production-wide ChatGPT Enterprise rollout, creating an in-house-developed GPT Store with 700 vetted bots for legal, credit, and marketing tasks. For example, the bank’s Retail Banking Legal Assistant GPT handles 40,000 client queries annually, while Credit Analysis Pro GPT auto-extracts financials and ESG data, claiming a 70% reduction in analyst preparation time. Overall, the AI saved each user an average of 2.8 hours per week, with internal surveys showing near-maximum satisfaction scores. “We spread these capabilities across the whole bank because if we wanted it to be the spark for a bigger transformation, we needed to let everyone touch the technology,” says Ricardo Martín Manjón, Global Head of Data at BBVA. “And people see its value. It’s not a transformation that we need to push. Everyone wants to be part of it.”
Similarly, pharmaceutical company Moderna rolled out ChatGPT Enterprise to thousands of employees with an ambitious goal: achieving 100% adoption and proficiency in generative AI within six months. Just two months into the programme, employees created 750 custom GPTs across the company, with 40% of weekly active users creating their own models for daily use, and each user averaging around 120 conversations per week. These metrics demonstrate how quickly companies can scale AI adoption when leadership commits fully to transformation rather than gradual, department-by-department rollouts. The success required comprehensive training programmes, dedicated AI ‘champions’ in each department, and clear guidelines for AI use across different business functions.
The competitive edge
In today’s increasingly competitive landscape, going AI-first may no longer be an option but essential to survival.
The transition to AI-first operations demands more than technological adoption – it requires substantial changes in leadership thinking, organisational culture, and workforce management. The most successful transformations combine top-down mandates with bottom-up innovation, creating environments where AI adoption becomes natural rather than forced. Workable’s 2024 survey reveals that 71.9% of workers are comfortable using AI tools at work, contradicting earlier assumptions about employee resistance. Nearly one-third report being very comfortable, while another 40.1% are somewhat comfortable. Construction workers, surprisingly enough, show the highest comfort level at 85.1%, followed by IT at 77.5% and finance at 75.9%. Healthcare (57.9%) and education (61.1%) show lower comfort levels, with significantly more workers reporting neutrality or discomfort – likely due to the human-centric nature of these fields.
The performance gains achieved by AI-first companies provide competitive advantages that traditional companies may struggle to bridge. BCG’s 2025 analysis reveals that AI-first companies demonstrate 25-35x higher revenue per employee compared to traditional companies. They typically employ 50-70% fewer people, but pay almost double the usual compensation for their top talent, indicating a shift toward specialised, high-value human roles while AI handles routine tasks. Similarly, Microsoft’s study of over 2,000 business leaders shows that companies can realise returns on AI investments within 14 months, with every US$1 invested generating an average return of US$3.50.
Marc Benioff, Chief Executive of Salesforce, reports deep integration: “AI is doing 30 to 50% of the work at Salesforce now for engineering, coding, and customer service tasks.” The company’s AI agent platform, Agentforce, has achieved 93% accuracy in customer interactions, with over 5,000 customers deploying AI agents. Benioff emphasises that this transformation enables employees to “move on to do higher value work”. Paul Daugherty, Accenture’s Chief Technology and Innovation Officer, argues that “the playing field is poised to become a lot more competitive, and businesses that don’t deploy AI and data to help them innovate in everything they do will be at a disadvantage”. His assessment reflects the view that AI adoption extends beyond operational efficiency to fundamental innovation capabilities.
The timeline for action continues to compress. Emad Mostaque, Founder and Chief Executive of Stability AI, offers a stark warning: “We see the wave coming. Now this time next year, every company has to implement it – not even have a strategy. Implement it.” This urgent timeline reflects both the accelerating AI development and competitive pressure facing companies across industries. The window for gradual adoption is closing rapidly, with companies needing to move beyond planning phases to active deployment.
The shape of things to come
What leaders need to understand about the path ahead.
Industry analysts predict significant market consolidation as AI-first companies gain sustainable competitive advantages over their traditional counterparts. PwC’s 2025 AI Business Predictions suggest that “top performing companies will move from chasing AI use cases to using AI to fulfil business strategy.” This shift indicates that successful AI implementation will become a primary driver of market share concentration. Morgan Stanley’s technology analysis predicts significant advancement in autonomous business operations between 2025-2028, with AI systems taking on increasingly complex decision-making responsibilities. The development of reasoning-capable AI models will eventually enable automated operations in areas that previously required human judgment.
Time Magazine’s AI predictions emphasise the shift toward ‘agentic’ systems that can act autonomously to complete complex tasks. This technological evolution will enable entirely new business models that were previously impossible with human-only operations. Platform-based business models will likely dominate, with AI-first companies creating ecosystems that connect autonomous agents, human users and traditional organisations. The regulatory landscape will mature significantly. TechInsights’ 2025 AI Outlook Report predicts the emergence of comprehensive AI regulations and safety measures ensuring ethical, responsible deployments. These frameworks will address bias, transparency, and accountability in AI models while creating compliance requirements that favour larger companies with resources for comprehensive governance.
Learnings
The evidence points toward a future where AI-first organisations don’t just outcompete traditional companies, but operate in fundamentally different economic realities. The performance gaps we’re seeing today represent early indicators of a broader transformation that will reshape industries, employment, and competitive dynamics throughout the rest of the decade. For leaders, the choice is quite clear: either they become AI-first or risk becoming irrelevant. The window for gradual adoption is closing as competitive advantages compound and market dynamics shift toward companies that can use AI for sustained innovation and efficiency gains. The companies that act decisively today will define tomorrow’s competitive landscape.
