Feature
As artificial intelligence (AI) continues to reshape industries, companies are grappling with a pivotal question: Does adopting an AI-first strategy unlock meaningful opportunities for your business?
For some, the answer is straightforward, as they integrate AI into their DNA, like Zoom rebranding itself as an “AI-first company.” For others, it’s a more complex calculation, balancing technical capabilities, market demand and brand identity. Whether adding AI layers to existing products or building entirely AI-driven offerings, businesses must evaluate how to position themselves in an AI-dominated future.
What factors should enterprise leaders consider when deciding to embrace AI as a core focus? And how can they tackle technical strategy, product innovation and branding implications amid the shift?
What Does It Mean to Be an AI Company?
The rapid evolution of AI is redefining business strategy across industries. Companies are increasingly leveraging AI to automate processes, enhance customer experiences and unlock new revenue streams, making it a critical driver of innovation and competitive advantage.
Ryan Gray, CEO of SGW Designworks, pointed out the ambiguity of the term “AI company,” noting the broad spectrum of its interpretations. "The problem with a term like 'AI company' is that there isn't yet any consensus on how to define it narrowly. It can mean anything, from a company that actually creates AI to one that simply incorporates AI features in its products."
Zoom has embraced an AI-first identity by integrating generative AI features into its platform to enhance user productivity and collaboration. Key innovations include automated meeting summaries that provide concise recaps and real-time language translation to bridge communication gaps across diverse teams. These features not only improve the user experience but also reflect Zoom’s commitment to embedding AI as a core element of its offerings.
This transition represents a strategic move to remain competitive in a market where AI capabilities are quickly becoming essential for technology leaders. While it aligns with broader industry trends, some experts view it as less of a groundbreaking transformation and more as a necessary step to stay relevant in a space increasingly dominated by early AI adopters.
"As for Zoom, rebranding into an AI-driven company isn't such a radical shift, in my opinion,” said Gray. “However, it will certainly help keep it competitive against companies like Google and Microsoft, which have entered the game a bit earlier, albeit without a corresponding flashy rebrand.”
The Spectrum of AI Adoption
AI adoption exists across a spectrum. AI-native startups such as OpenAI or Stability AI build their offerings entirely around advanced machine learning (ML) models, while legacy businesses like IBM and Salesforce are adding AI layers to enhance existing products and transform their operations. Regardless of where a company falls on this spectrum, the goal is the same: to use AI as a competitive differentiator.
Successful AI companies share key traits. They prioritize innovation, often leveraging cutting-edge tools to stay ahead of market trends. They exhibit adaptability, continuously refining their models and processes as AI evolves. And, above all, they maintain a customer-centric focus, using AI to solve real-world problems and enhance user experiences, ensuring that the technology serves meaningful business objectives.
An AI-first company integrates AI into its core products, services and operations, using the technology as a foundation for decision-making, customer interactions and business processes. This goes beyond occasional AI features — it involves embedding AI at every level to drive innovation and create value.
Jim Lundy, CEO and lead analyst at Aragon Research, emphasized that AI-first businesses thrive by embedding innovation into their DNA. “The real AI companies are those that create ecosystems where AI enhances every touchpoint.”
Related Article: Cultivating a Culture of Innovation in the GenAI Era
Evaluating the Business Case for Pivoting to AI
Having the desire to pivot to an AI-first or AI-centric organization isn’t enough. It’s necessary to evaluate if your business is ready for the challenge.
Technical Capabilities
Before transitioning to an AI-first approach, businesses must evaluate their technical capabilities. This includes assessing whether they have the necessary infrastructure, such as data pipelines and scalable cloud solutions, as well as the talent to build and manage AI systems. Without a strong foundation, even the most ambitious AI initiatives risk underperformance.
Strategic Capabilities
Seth Geftic, VP of product marketing at Huntress, told VKTR that although AI is hugely exciting, businesses should think carefully about whether shifting to become AI-first makes sense for their strategic goals.
“For example, if existing AI technologies don’t deliver huge improvements over your current approach, reshaping your company around this technology could lead to bigger problems in the future,” said Geftic. “While it’s easy to think AI will constantly improve, understanding your core objectives and whether the technology delivers tangible difference is key to evaluating your position."
Cultural Alignment
Businesses also need to evaluate whether or not they are ready to make the shift to being an AI-centric organization. While technical infrastructure often takes center stage in discussions about AI readiness, Taka Ariga, chief AI officer at the US Office of Personnel Management, emphasized that “AI readiness is as much about cultural alignment as it is about technology,” and advised businesses to assess not only their data infrastructure but also their willingness to embrace cross-functional collaboration, a suggestion that is even more vital for companies considering a pivot to becoming an AI company.
Data Infrastructure
In addition, in an environment where data silos are prominent and often debilitating to cross-departmental collaboration, data readiness is a vital consideration.
“Even if the business case for becoming an AI-first organization makes sense, the company might not have the necessary data readiness to make the move anytime soon,” said Geftic. “AI is only as good as the data it’s trained on, so if the organization fails to build and maintain a robust data collection, storage and processing system, it won’t be able to capitalize on its new approach.”
Market Demand
Market demand is another critical factor. Understanding customer expectations and the competitive environment can help determine whether an AI pivot aligns with business goals. Companies in industries such as healthcare, finance or retail, where AI is driving significant innovation, may find greater urgency to adapt. More importantly, will its customers be supportive of the very technology around which the company will be built?
“You can’t ignore your competitors when considering a pivot to AI-first operations,” said Geftic. “Their actions might help you feel more confident in your decision or give you a reason to pause. Similarly, you must research your customers and gauge their interest in AI-driven products and services.” A business may find that many consumers aren’t interested in using AI to interact with its business, he added. “If that’s the case, doubling down on human-led practices could actually give your business a competitive advantage.”
Financial State
Financial considerations also play a decisive role. AI adoption requires substantial investment in research and development, tools and ongoing operational costs. However, the potential return — ranging from efficiency gains to entirely new revenue streams — can far outweigh these expenses if the pivot is well-executed and strategically aligned with market opportunities. Evaluating these factors holistically ensures that the decision to embrace AI is grounded in both opportunity and feasibility.
Company Mission
Ben Carle, CEO at FullStack, emphasized the importance of aligning AI capabilities with a company's mission rather than treating AI as a standalone identity. He reiterated that AI adoption requires a strategy grounded in iterative learning and readiness to adapt.
“AI isn’t a plug-and-play solution — it requires a clear strategy and a commitment to iterative learning,” he explained. “The real question isn’t if AI should play a role, but how prepared the company is to adapt and integrate it effectively.”
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Strategic Approaches to Becoming an AI Company
According to Mike Szczesny, VP of EDCO Awards & Specialties, companies need to clearly define their objectives before adopting AI.
"Management has to specify clearly what business problem they want to solve by adopting AI,” he said. “Sequential targets are necessary in order to facilitate congruence throughout the teams."
One option is building AI-native products, where AI is central to the offering, such as AI-powered analytics tools or autonomous systems. Alternatively, businesses can enhance existing products with AI layers, like integrating predictive analytics or automation features, providing immediate value without starting from scratch.
Partnerships with established AI platforms or innovative startups can accelerate adoption. Collaborations with companies like OpenAI, Google Cloud or industry-specific AI providers offer access to advanced tools and expertise, reducing development timelines and costs.
Investing in AI talent is equally crucial. Hiring data scientists, ML engineers and AI specialists lays the technical groundwork, but establishing a culture of innovation ensures long-term success. Encouraging experimentation and cross-functional collaboration allows teams to identify novel AI applications, driving both operational efficiency and market differentiation. By balancing these approaches, companies can adopt AI in a way that aligns with their capabilities and aspirations.
Branding and Market Positioning in the AI Era
Effectively communicating an AI-first pivot requires clear and authentic messaging to both stakeholders and customers. Transparency is key — articulate how AI will enhance your offerings and align with customer needs, while addressing any concerns about privacy or ethics. For instance, companies like Zoom successfully positioned themselves as AI-driven by showcasing tangible benefits such as automated meeting tools, emphasizing usability over hype.
However, there are risks. Overpromising AI capabilities or misaligning your brand identity can damage trust and credibility. Companies must balance ambition with reality, ensuring that their AI messaging reflects their actual capabilities and long-term vision.
Edward Tian, CEO of GPTZero, underscored the importance of audience perception when pivoting to an AI-first strategy, cautioning businesses to carefully evaluate customer reactions, stating that “at the end of the day, your audience determines whether or not your business succeeds.”
Adobe is an example of a company that has successfully integrated AI into its Creative Cloud suite through Adobe Sensei, which powers features such as automated image cropping and enhanced search tools. These capabilities save users significant time and elevate creative workflows. Adobe’s approach shows how AI can enhance existing products rather than create entirely new offerings, catering to its established user base while attracting new audiences.
Similarly, Microsoft's integration of AI through tools like Copilot in Microsoft 365 exemplifies how AI can redefine productivity. Copilot enables users to generate documents, presentations and code with minimal input, taking advantage of AI to complement human efforts. This demonstrates Microsoft’s focus on embedding AI into everyday tools, ensuring adoption feels seamless and accessible for users.
On the other hand, IBM’s ambitious attempt to apply Watson’s AI to revolutionize healthcare met with significant challenges. Despite high expectations, the initiative faced criticism for underdelivering on its promises, largely due to the complexity of medical data and unrealistic projections of AI’s capabilities. Watson’s struggle is a lesson in the dangers of overhyping AI without adequate domain expertise or feasibility assessments.
Challenges and Risks of the AI Pivot
Pivoting to an AI-first strategy comes with challenges that demand careful navigation. Ethical considerations like bias in AI, data privacy and transparency are paramount. Customers and regulators alike expect companies to address these issues proactively, ensuring their AI systems are fair, secure and explainable. Failure to do so can erode trust and draw scrutiny.
Avoiding “AI washing” is another critical concern. Overstating AI capabilities or rebranding without substantive changes risks alienating customers and stakeholders. Authenticity in AI claims is essential — businesses must ensure their AI initiatives deliver meaningful value rather than serving as marketing buzzwords.
James Evans, director of product management at Amplitude, said that AI-washing worked in early 2023, when all you had to do to show that you were a “with-it, happening” company was post a GIF of your product thinly wrapping the GPT API. "This kind of 'ingredient marketing' has quickly lost its appeal as users have now actually used those features — and found most of them to be gimmicks."
Evans explained that there is a clear division between companies leaning into bold AI strategies that could actually be transformative for their business, and those that are just paying lip service to it. “The question I think most businesses should ask is: If we couldn't tell our users this was 'powered by AI,' would we still ship it and put as much marketing muscle into it? If the answer is yes, full speed ahead.”
In addition, companies must overcome organizational inertia and resistance to change. Transforming into an AI company often requires shifts in culture, processes and talent strategies, which can incur pushback from teams accustomed to traditional workflows. Organizational resistance and the need for a mindset shift can be key barriers to AI integration.
Kelwin Fernandes, CEO of NILG.AI, explained that “AI implies a shift of power from those making decisions on a daily basis to those defining how the AI will decide. This process creates frustration and resistance.”
To navigate this challenge, leadership must prioritize clear communication, robust training programs and well-structured incentives to encourage alignment and enthusiasm across teams. By addressing these risks with intention and care, businesses can position themselves to make the AI pivot both successful and sustainable.
From Vision to Execution: Making AI Meaningful
Ultimately, the decision to pivot to an AI-first strategy is as much about execution as it is about vision. Businesses should focus on incremental, value-driven AI initiatives that align with their mission and adapt as their capabilities grow.
Those that anchor their AI efforts in authenticity, practical implementation and customer-focused value creation will not only adapt but thrive in an increasingly AI-driven market.
