AI-First Companies

Summary: AI-First means massive automation to repetitive tasks, rapid iteration over perfection, ubiquitous AI integration across workflows, and workforce transformation requiring AI literacy. As organizations automate routine work, humans shift to oversight roles, creating new UX challenges around maintaining vigilance over increasingly autonomous systems.

This year, many companies have declared themselves to be “AI First.” This means artificial intelligence is now the company’s top strategic priority, guiding everything from product development to internal operations.

A growing list of CEOs has explicitly announced AI-first transformations. In April 2025, Duolingo (a language-learning app) told employees it would “stop using contractors to do work AI can handle” and that “AI is becoming the default starting point” for every task. CEO Luis von Ahn said the company would only increase headcount after teams have “maximized all possible automation” using AI. Around the same time, Shopify CEO Tobias Lütke declared that using AI is “now a fundamental expectation” for every employee’s daily work, and teams “must demonstrate why they cannot get what they want done using AI” before asking to hire anyone new. Zoom even rebranded in late 2024 (dropping “Video” from its name) to underscore an AI-centric future, and enterprise cloud provider Box is similarly “100% focused” on being an AI-first company.

In an AI-First company, the expectation is that more and more tasks will be performed by AI, leaving humans in oversight roles. (GPT Image-1)

The first well-known company to publicly declare an AI First strategy was Google. In October 2016, CEO Sundar Pichai announced a strategic shift from a “Mobile-First” world (prioritizing smartphone accessibility) to an “AI-First” world (prioritizing machine learning as the primary method for solving user problems). This marked a transition from the device form factor (the smartphone) to the underlying intelligence layer (machine learning). Pichai’s 2016 and 2017 public remarks outlined a vision of Google’s future centered on artificial intelligence, with computing becoming an “omnipresent intelligent assistant” rather than something confined to mobile screens. This was a pioneering moment: a tech titan openly declaring that AI would drive its roadmap and products and Google’s AI-first pronouncement at Google I/O 2017 (and creation of Google.ai) is widely regarded as the first high-profile “AI-First” pledge by a tech giant.

Chinese search engine Baidu was an early pioneer that arguably implemented an AI-first approach even earlier (mid-2010s), albeit with less fanfare outside China.

AI-First Products vs. AI-First Company

There’s a difference between early uses of the term “AI First” by companies like Google and Baidu in the 2010s and the way modern companies use the term in the mid-2020s. Now, AI is no longer a research goal; it’s a reality. AI First no longer means “let’s infuse some machine intelligence into products here and there.” It means building and operating the organization on AI.

An AI-First Company is an organizational strategy. It means prioritizing AI in resource allocation and operational processes across the entire organization: HR, finance, legal, and product development. It is a cultural and operational transformation.

In contrast, an AI-First Product (or AI-Native Product) is a design orientation. It is designed from the outset with AI as the core value proposition and primary interaction modality. If you removed the AI component, the product wouldn’t work or wouldn’t deliver the intended value to users. This often involves a shift from command-based interaction (users explicitly telling the computer what to do via menus) to intent-based interaction (users stating a goal, and the AI determining the steps).

To illustrate the difference: A bank could decide to become AI-first as a company by integrating AI into many processes (risk assessment, customer service bots, fraud detection, etc.), but that bank still offers some traditional banking services that don’t inherently require AI. Meanwhile, a specific offering from that bank, such as a mobile app feature that provides customers with financial advice via an AI chatbot, might be considered an AI-first product because the feature itself is fundamentally an AI interaction.

Characteristics of AI-First Companies

AI-First companies across industries share core objectives that define their transformation strategies. The primary goal is massive automation for efficiency and scale. These organizations systematically replace human-performed repetitive tasks with AI systems wherever quality thresholds are met. Duolingo exemplifies this approach, explicitly deciding to “gradually stop using contractors to do work that AI can handle,” from generating lesson content to answering support queries. The underlying principle: if AI can perform acceptably, human effort should be minimized, enabling companies to serve exponentially more customers without proportional headcount increases.

A fundamental management principle in many AI-First companies is to make AI use the default and not hire humans for a job unless it’s proven that AI can’t currently do it. (GPT Image-1)

Speed and innovation represent another crucial priority. AI-First companies embrace rapid iteration over cautious perfection. As Duolingo’s CEO stated, “We can’t wait until the technology is 100% perfect... We'd rather move with urgency and take occasional small hits on quality than move slowly and miss the moment.” This philosophy enables companies like Spotify to deliver real-time personalized experiences and allows Zoom to rapidly deploy AI features like live meeting summaries. Speed of learning becomes a competitive advantage.

The third universal theme involves making AI ubiquitous across workflows. Employees are expected to start every task with AI: marketers draft copy with AI, engineers use code assistants, recruiters screen résumés algorithmically. Shopify exemplifies this cultural shift, requiring teams to “demonstrate why they cannot get what they want done using AI” before requesting additional resources. AI transitions from a specialized tool to the default starting point across all departments.

Workforce transformation accompanies this shift. Companies make AI proficiency a hiring criterion and performance metric. Duolingo evaluates employees partly on AI usage, while those ignoring automation tools risk being viewed as underperformers. Organizations invest heavily in training, running internal AI bootcamps and providing specific guidelines for AI integration in daily work.

Data and infrastructure readiness become a strategic imperative. AI-first firms treat data as critical assets, investing in quality collection, labeling, cloud infrastructure, and ML platforms. They simultaneously emphasize governance and ethics, establishing review boards to address algorithmic bias and ensure transparency, recognizing that trust underpins AI-dependent operations.

Most importantly, these companies pursue entirely new capabilities previously impossible. AI-first thinking isn’t about acceleration alone, but about enabling unprecedented services, such as 24/7 multilingual support, personalized experiences for millions, and one-on-one AI tutoring at scale. Companies redesign around AI's unique possibilities rather than simply optimizing existing processes.

Creative internal applications are emerging, with some firms using AI as strategic advisors, analyzing data to suggest business strategies. Media companies target specific percentages of AI-generated content, though quality concerns limit widespread adoption.

These varied approaches demonstrate how companies tailor AI-first concepts to their identities. The common thread is clear: AI-first companies systematically integrate artificial intelligence into every aspect of operations, treating it not as an enhancement but as fundamental to how modern businesses compete and innovate.

Skills Employees Need in an AI-First Company

As organizations evolve into AI-first entities, employees across all functions, not just technical roles, must develop new competencies. The transformation demands both skill development and cultural adaptation to ensure successful integration of AI technologies.

AI literacy forms the foundation of the modern workforce. Employees need a working understanding of current AI capabilities and limitations. (Since these change constantly, continuing training is needed.) Marketing professionals should grasp how AI segmentation tools function, while HR staff must understand the mechanics behind AI-powered résumé screening.

Practical AI tool usage has become critical. Employees must master prompt engineering for generative AI and integrate various AI solutions into their workflows. JPMorgan Asset Management exemplifies this approach, providing prompt engineering training to all staff. The ability to partner effectively with AI involves constructing and refining outputs, correcting errors, and optimizing commands to improve outcomes iteratively.

Adaptability and continuous learning are essential in rapidly evolving AI environments. Unlike traditional systems used for years, AI tools will change every few months. Success requires embracing experimentation, viewing AI as an augmentation rather than replacement, and maintaining curiosity about new capabilities. Employees who thrive demonstrate resilience, tweaking approaches when initial attempts fail rather than abandoning the technology.

Critical thinking becomes more important as AI handles routine tasks. Employees must evaluate AI recommendations and assess their validity. When AI flags priorities or generates analyses, human judgment determines whether these align with reality. Employees must sharpen problem-solving skills, ask probing questions, and understand current AI limitations, like hallucination, to provide essential oversight.

Collaboration and communication skills remain vital in AI-augmented workplaces. Teams increasingly work with and through AI systems, requiring clear, objective communication with AI tools and the interpretation of AI outputs. As AI handles coordination tasks, team structures will flatten, demanding greater initiative and collaborative flexibility. Success means focusing on human skills that complement AI rather than compete with it, especially agency, judgment, and persuasion.

“AI First” doesn’t mean “AI Only.” We still expect to have humans around and assign them roles requiring agency, judgment, and persuasion. (GPT Image-1)

Workforce Transformation Strategies

Successful AI-first transformations require comprehensive upskilling programs. These programs typically begin with foundational concepts before advancing to role-specific applications. Despite this need, many companies still underinvest in AI training. The most effective approach is learning by doing, integrating AI training directly into the employee’s workflow.

It's not enough for the CEO to press that “AI-First” button. Top-down initiative is essential to get things moving, but must be supplemented with bottom-up workforce transformation. (GPT Image-1)

Effective transformation combines top-down support with bottom-up empowerment. Leadership must communicate AI training as a priority, allocate resources, and reward adoption. Simultaneously, organizations should encourage grassroots experimentation and celebrate internal “AI champions” who automate tasks and assist colleagues. This dual approach builds ownership rather than imposing change.

Successful AI cultures encourage experimentation and measured risk-taking. Consider creating sandboxes where employees can explore AI tools without operational consequences. Such hands-on practice builds confidence and overcomes resistance to change.

A sandbox is a contained computer environment where users can execute features without them having any effect on the real system. Ideal for learning when you don’t have to worry about doing something wrong. (GPT Image-1)

Training must be accessible through multiple formats, relevant to actual job tasks, and continuous rather than one-time events. Embedding AI experts within business units provides ongoing coaching support. Less successful efforts tend toward generic content, lack leadership emphasis, or fail to provide infrastructure for applying learned skills.

Culturally winning organizations foster curiosity-driven atmospheres where teams share AI tips openly and leaders discuss both successes and challenges, modeling growth mindsets. This transparency transforms AI from a mysterious threat to a collective learning opportunity.

Building an AI-first company is equally a human and tech journey. Companies focusing on upskilling and communication find engaged workforces embracing AI. Those neglecting the people side encounter resistance and underutilized investments. AI is only as good as the staff’s ability to use it.

AI Native Companies

An AI-native company is one whose product, operations, and business model are fundamentally reliant on artificial intelligence from the outset. In contrast, an AI-first company (as I’ve been discussing so far) is typically a legacy organization that prioritizes the integration of AI into everything it does, even if AI wasn’t its original foundation.

AI-Native companies will usually accelerate faster than AI-First companies. A third category, AI Forward, is not discussed in this article since it does not involve a company-wide AI program, but rather leaves AI adoption up to individual employees and teams. Allowing a budget for such less ambitious AI efforts can be a starting point for companies with timid leadership. (GPT Image-1)

With respect to AI, a “legacy” company is basically any firm founded before 2023. Even if they do attempt to go AI-First, it’s doubtful that most such legacy companies will ever become as successful as newly founded AI-Native companies that don’t carry the heavy legacy baggage.

Retrofitting AI onto a legacy company inherits a heavy burden that will rarely make the revamped organization as well-suited for the future as an AI-native firm. (GPT Image-1)

The workforce composition differs dramatically between these models. AI-native companies employ almost exclusively AI specialists in fast-moving, experimental environments where employees wear multiple hats: developing models one day, meeting customers the next. There’s no legacy playbook; they're inventing processes as they go. These firms aggressively recruit AI researchers as their core talent, operating with a research lab culture that embraces experimentation and rapid pivoting.

Conversely, established companies becoming AI-first must retrofit AI into existing structures while managing larger workforces and legacy systems. Employees experience a transitional period where roles get redefined. These organizations must spend heavily on retraining, often running parallel workflows (old and new) during transformation. Legacy does bring advantages like domain expertise, established customer bases, and decades of accumulated data, but such companies face challenges orchestrating change across siloed departments.

Cultural differences are stark. AI-native firms embrace a “move fast and break things” ethos with a focus on solving specific problems exceptionally well through AI. Traditional companies balance innovation with stability, brand reputation, and regulatory compliance, making overnight transformation impossible.

My career advice for ambitious young or middle-aged staff: If at all possible, get a job at an AI-Native company since this will position you best for the future, rather than spending years compensating for a legacy company’s inertia. Older staff, in contrast, may be better off staying put in their current legacy firm if there’s hope that leadership will embrace an AI-First strategy. Staying allows older professionals to leverage their many years of hard-won experience within that organization. Staying will not position people well for working in a radically different AI world in 20 years, but if you plan to retire before then, it's better to optimize your shorter-term career.

Learning AI skills is beneficial for any career. Whether to seek out a new company depends on the individual employee’s career stage: more than 10 years until retirement? Move to an AI Native firm if you can. (GPT Image-1)

UX in AI-First Companies

UX design is even more crucial in AI-First (and AI-Native) companies than in traditional settings, because AI introduces new complexities and possibilities in how users interact with products. They can’t just copy the thousands of design patterns we know and love for traditional design. AI-first companies need to rethink UX in light of AI’s capabilities and quirks.

Even though we need new design patterns for AI, basic UX principles (say, the 10 usability heuristics) still apply in AI-first companies. But the design challenges change. AI systems often produce dynamic, unpredictable outputs. For example, an AI writing assistant might generate different content each time, or an AI in a medical app might give varying recommendations based on subtle differences in input. The UX team’s job is to shape an experience around this variability in a way that users feel in control and informed.

AI is probabilistic: the same prompt may sometimes generate a brilliant business idea and other times something deadly. (GPT Image-1)

One major consideration is building user trust and understanding. Users often don’t trust an AI’s output if they don’t understand how it was derived or have no way to verify it. So UX designers in AI-First companies should incorporate features to explain or contextualize AI decisions. For instance, an AI-driven finance app might label a recommendation with, “Based on your last 3 months of spending” to clue the user into why the AI suggests a certain budget move. Design elements like confidence indicators, explanation tooltips, and feedback buttons should be considered for AI-heavy interfaces.

Many users remain concerned about AI and lack trust in it. It’s the job of UX to rectify this situation by making AI more understandable. (GPT Image-1)

UX designers in AI-first environments also find themselves collaborating even more with engineers and data scientists. The line between product design and the AI’s behavior is blurrier than the line between design and traditional software engineering. In a traditional app, designers specify flows and the software largely follows those scripts. In an AI-driven app, designers specify guidelines and guardrails, but the AI’s learned behavior fills in a lot of the details. This means designers must iterate closely with developers tuning the AI model. A lot of UX work is about fine-tuning the interaction, for instance, adjusting how “sensitive” an AI chatbot is to off-topic questions (too strict and it’s unhelpful, too loose and it rambles). These decisions sit at the intersection of UX and AI model design.

For UX professionals , working in an AI-First company often means expanding their skill set. They need to understand basic AI principles to design better. They might also engage in conversation design for chatbots, designing how an AI assistant should respond in a conversational UI. That’s a relatively new facet of UX work.

Despite all the changes, one thing remains constant: the UX team’s job is to advocate for the user. In AI-First companies, that sometimes means pumping the brakes on technology for technology’s sake. UX designers should ask, “Is this AI feature actually helping users, or is it just cool?” If it’s not genuinely useful, recommend cutting it or improving it until it is. For instance, if an AI feature in a writing app produces text that needs so much editing that users find it easier to write from scratch, user research should communicate these findings such that the feature is be reworked or removed, not simply presented with a fresh cost of UI paint on top of the same useless functionality.

In traditional firms with strong UX programs, UX teams often had well-established guidelines and could rely on patterns that were known to work. In AI-first companies, UX is a field of experimentation. Designers are forming new best practices (e.g., how to indicate to the user that content is AI-generated, how to allow user control over AI suggestions, etc.). They are also often the bridge between skeptical users and ambitious AI engineers, translating user concerns into design changes and pushing engineers to adjust the AI’s behavior for improved usability.

Overall, the role of UX design in AI-first companies is to humanize and harness AI. UX professionals ensure that advanced AI features are understandable, trustworthy, and actually solve user problems. This differs from traditional UX in the unpredictability and opacity of AI systems, but the goal is the same: deliver a great experience.

The Future of AI-First

It’s an old saying that the only constant is change. But with AI, the speed of change itself isn’t constant, but accelerating. What’s a good AI-First company today may be a bad one tomorrow.

Looking ahead, AI-First initiatives must become even more ambitious. Today, a strong AI-First company is doing things like automating many processes, requiring employees to leverage AI tools, and embedding AI in most products. But these changes will be pushed much further with the next generation of AI, arriving in about two years:

• AI in every decision loop: Aggressive AI-First firms might insist that all significant decisions are informed by AI analysis. For instance, before launching a new product or entering a market, the company runs AI-driven simulations and predictions to guide the strategy. AI wouldn’t replace executives, but it would be a constant advisor at the table, crunching numbers and surfacing insights humans might miss.

• No human does routine work: A very strong AI-first stance could be that any task that is repetitive or data-heavy is handed to AI. For example, a company might automate basic legal contract reviews, data entry, quality checks on assembly lines, and so on, with humans only handling exceptions or more complex cases. This would maximize efficiency (and indeed some companies are aiming for this, setting targets like automating X% of workflows).

• AI “co-pilots” for everyone: Many AI-first organizations are already giving employees AI assistants (like coding assistants, writing assistants, etc.). In an extreme form, every single employee might have a personalized AI that learns their job and helps them continuously with scheduling meetings, drafting communications, summarizing data, generating ideas, and more. Some companies, like Microsoft with its CoPilots, are heading in this direction. This trend could become standard: the entry-level “assistant” for any role could literally be an AI program at your side.

• AI-first product design taken to new heights: Companies could start designing products that assume AI on the user’s end. For example, content might be created in forms that an AI on the user’s device will personalize or read out. Or software might leave certain configurations to be done by the user’s AI. This is speculative, but essentially products might become platforms for the user’s own AI to operate. It’s a twist on AI-first: not only is the company AI-first, but the products themselves expect an AI-augmented user.

If we project further into the future (say 2030, when we’re likely to achieve superintelligence), we can imagine the ultimate AI-first company that takes today’s trends to an extreme:

• Minimal human workforce: A truly AI-First company of the future could operate with a skeleton crew of humans overseeing a legion of AI agents and robots. Entire departments (accounting, customer support, even middle management) might be mostly AI-driven. Humans would be in strategic or creative roles, and AI handles the grind. We’re already seeing early signs: some startups in 2025 have only a handful of employees but serve thousands of customers with AI-run services. As AI becomes more capable, this “lean staff, heavy AI” model will scale to larger businesses.

• Fully AI-generated products and services: The ultimate AI-first company might have AI not just assist in making products, but design and iterate products almost independently. For instance, an AI-first game studio in 2030 might have AI systems generating game environments, characters, even plotting storylines, with minimal human input beyond setting high-level direction. Or an AI-first media company might release customized AI-generated shows for each demographic. We see hints of this now (like AI-generated news articles, AI-created advertisement variations), and it could become far more sophisticated.

• Hyper-personalization at scale: Future AI-first companies could offer products that are tailored on the fly to each user by AI. Imagine an e-commerce company where every user sees a different store optimized by AI for that user’s preferences, with pricing, recommendations, and even product variations (perhaps 3D printed or digitally created) uniquely generated. AI would handle this level of complexity; the company’s job would be to set the overall parameters and ensure quality. Some companies are already moving toward mass personalization, but with advanced AI, the “segment of one” marketing becomes more feasible.

“I used AI to personalize our feed: now the cow gets low-fat hay, and the pig’s on a bacon-free diet!” (GPT Image-1)

• AI-managed, self-improving organization: Hierarchies will flatten as AI systems take over routine coordination and reporting. Instead of many layers of managers aggregating information and making minor decisions, an AI could do a lot of that instantly, freeing managers to focus on strategy. Moreover, the AI systems themselves might start to self-optimize. We already see early versions: AI monitoring software performance and adjusting server loads. In an ultimate scenario, an AI-First company’s operations could be like a closed-loop system: AI monitors all key metrics (production, sales, customer satisfaction) in real time and adjusts processes (like supply chain flows, pricing, content output) continuously to optimize those metrics, with minimal human intervention.

• The Lean Elite: The distinction between “employee” and “manager” blurs as everyone manages AI agents as the way they get their work done.

Most employees will become virtual managers, overseeing teams of more or less autonomous AI agents. (See section below for the ladder of AI autonomy.) (GPT Image-1)

• The Human as Arbiter: The primary human role will be high-level judgment, ethical arbitration, defining long-term vision, and managing the most complex edge cases where AI fails.

Humans need the agency to provide the vision, while AI will provide the foundation of all the tasks that used to be considered “work.” (GPT Image-1)

Levels of AI Autonomy

Start by classifying work according to the level of AI autonomy you will allow today and the checkpoints that would justify a higher level of autonomy tomorrow. With self-driving cars as an analogy, consider this sequence of defined autonomy levels for AI in business:

• A0, Advisory: Keep Artificial Intelligence (AI) in draft mode: the system proposes, the human writes, and remains fully accountable. A0 is appropriate for new domains, sensitive writing, or any case where a misstep is costly and context is thin.

• A1, Copilot: Let the system complete tightly scoped steps that a person must approve one by one; think code suggestions, summarizing a meeting you just had, or drafting a vendor email you will sign.

AI autonomy levels need to be adjusted depending on AI capabilities at any given time plus the critical nature of each task. In this example, the A1 level seems appropriate. (Seedream 4)

• A2, Bounded Autonomy: Move from steps to outcomes inside hard guardrails; for example, an agent that prepares monthly supplier reminders using pre-approved templates and a whitelisted data source, with humans reviewing samples and handling outliers.

• A3, Managed Autonomy: Treat the system like a junior team with explicit Service Level Objectives (SLOs): cycle time targets, error budgets, quality uplift vs. human baseline, cost per task, and escalation rules if quality dips. Humans audit these metrics and handle escalations.

• A4, Dark Launch Autonomy: Run the AI in parallel to the human process in production and promote it to A5 only when it beats the baseline on pre-registered metrics over sustained load.

• A5, Self-Optimizing Autonomy: At this top end, the system improves itself (within policy) using regression tests, canary models, and tripwires that force rollback when behavior drifts. Humans set these objectives and constraints.

Humans should serve as referees to check on autonomous AI agents and determine whether they are ready to be advanced to a higher level of autonomy. (GPT Image-1)

Autonomous AI agents are not all-or-nothing. AI-First companies can progress through these defined autonomy levels as the underlying AI models improve and as their internal understanding deepens of how to adapt AI and invent new workflows for their domain.

Climbing this ladder of AI autonomy requires two new human job roles, though not necessarily actual job titles, since these roles will exist across traditional functional lines. First, super‑users: the pragmatic tinkerers who turn messy processes into reliable prompts, tools, and policies. Give them air cover to refactor workflows across organizational boundaries and reward them for shrinking cycle times, not for writing manifestos. Second, auditors: the skeptics who hunt failure patterns, bias, and drift. Arm them with trace tools and authority to pull the plug. These roles make adoption safe and fast; any company lacking them is still doing demos.

Human Factors: The Oversight Problem

As the human role shifts from execution to oversight, a critical usability problem emerges: the “Boredom Problem.” Humans are notoriously poor at vigilance tasks, such as monitoring highly automated systems for rare errors. When AI performs correctly more than 99% of the time, as expected in a few years, human operators become complacent and inattentive, reducing their ability to intervene effectively when failures inevitably occur. Designing interfaces that maintain human engagement and cognitive readiness during oversight is a crucial, unresolved challenge.

Vigilance tasks are boring, and it’s impossible to keep up full and detailed attention for extended periods when nothing happens that requires intervention. It will be challenging to design solutions to this fundamental problem. (GPT Image-1)

About the Author

Jakob Nielsen, Ph.D., is a usability pioneer with 42 years experience in UX and the Founder of UX Tigers. He founded the discount usability movement for fast and cheap iterative design, including heuristic evaluation and the 10 usability heuristics. He formulated the eponymous Jakob’s Law of the Internet User Experience. Named “the king of usability” by Internet Magazine, “the guru of Web page usability” by The New York Times, and “the next best thing to a true time machine” by USA Today.

Previously, Dr. Nielsen was a Sun Microsystems Distinguished Engineer and a Member of Research Staff at Bell Communications Research, the branch of Bell Labs owned by the Regional Bell Operating Companies. He is the author of 8 books, including the best-selling Designing Web Usability: The Practice of Simplicity (published in 22 languages), the foundational Usability Engineering (28,941 citations in Google Scholar), and the pioneering Hypertext and Hypermedia (published two years before the Web launched).

Dr. Nielsen holds 79 United States patents, mainly on making the Internet easier to use. He received the Lifetime Achievement Award for Human–Computer Interaction Practice from ACM SIGCHI and was named a “Titan of Human Factors” by the Human Factors and Ergonomics Society.

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