[Series: AI Deep Dive]
With the rapid advancement of AI technology, AI-driven businesses are evolving at an unprecedented pace. What are the key trends to watch, and what competitive strategies should companies adopt in the AI era? Experts from Digital Garage, who specialize in AI both in Japan and globally, will provide insights and analysis based on their unique perspectives and expertise.
As AI technology rapidly evolves, businesses are being fundamentally reshaped across industries. Traditional business models, value propositions, organizational structures, and revenue models are being redefined. How should companies and professionals prepare for these changes and adapt to stay competitive?
This article explores five key transformations in the AI era—shifts in customer behavior and value delivery, customer touchpoints and distribution channels, organizational structures, revenue models, and partnership strategies—and provides insights into strategic actions companies should take.
＊Writer
Yuto Saeki; Business Co-Creation Department, GII Segment Division, Digital Garage, Inc.
Previously held roles at Monitor Deloitte, ByteDance, and ALL STAR SAAS FUND, with extensive experience in strategic investments, partnerships, and business development. Specializes in AI, fintech, and enterprise software, focusing on investing in and supporting startups in Japan and globally.
1. AI Is Automating User and Customer Behavior
First and foremost, one of the most significant changes brought about by the integration of AI into business is the transformation of customer behavior. Traditionally, users actively searched for information and made decisions manually. However, this process is expected to evolve towards automation and optimization through AI. Companies must redesign their value propositions to align with this shift.
In consumer-facing businesses (B2C), users have historically conducted searches and comparisons independently using search engines. Moving forward, AI will learn user preferences and contextual information in the background and proactively present optimized choices. This shift will enable users to achieve their goals more smoothly, increasing both brand loyalty and customer satisfaction.
choices themselves, to a future where information is optimized and curated, enabling more streamlined decision-making.
（Reference： Transforming AI from Novelty to Necessity for Consumers）
A relevant example is Please (formerly MultiOn), a startup developing an AI agent that autonomously searches for products on Amazon, adds them to the cart, and completes the checkout process. By selectively curating and presenting the most relevant options, this AI agent enhances the user’s decision-making process, making it more efficient and personalized.
On the enterprise side (B2B), AI-driven automation and optimization are fundamentally reshaping internal workflows. For instance, Klarna has developed its own AI-powered workflow system, reportedly leading to the cancellation of third-party tool subscriptions. For traditional digital tool providers, this shift highlights the urgent need to integrate deeper into workflows or to position their AI as a bridge between different tools, thereby redefining their value proposition.
Early-stage AI startups are also successfully transforming enterprise workflows. Pactum, an AI-powered autonomous negotiation platform, secured Walmart as a client in its initial phase. According to Harvard Business Review, Pactum minimized potential conflicts and ensured early adoption success by aligning its pilot program with clear business objectives and involving both internal and external stakeholders from the outset.
For B2B AI startups, it is crucial not only to validate new AI technologies but also to clearly define business outcomes and engage stakeholders strategically. Pactum, for example, successfully expanded from its initial pilot in Canada to Walmart’s North America, South America, and Africa operations.
As customer behavior and corporate workflows continue to evolve, solution providers must accurately identify what customers truly need and what behavioral changes are occurring, and then design new value propositions accordingly.
2. Hyper-Personalization is Becoming Essential in Marketing
The adoption of AI is transforming customer touchpoints and distribution channels, demanding more personalized and context-aware marketing strategies.
In B2B, AI tools must integrate seamlessly into existing business environments, requiring detailed implementation support that considers data ecosystems and API connectivity. The traditional approach of simply subscribing to a SaaS product and starting usage is no longer sufficient. Instead, specialized personnel, such as Sales Engineers and Implementation Consultants, are necessary to understand business processes, address technical challenges, and ensure smooth deployment. Startups like Miro and research firms like Gartner emphasize the critical role of tailored implementation strategies.
In B2C, marketing is becoming increasingly complex and dynamic. In an era where AI enables user-level personalization, traditional mass-targeted advertising and uniform campaigns are losing effectiveness. Instead, leveraging demographic data, behavioral history, and contextual insights allows businesses to generate and deliver real-time, highly personalized messaging tailored to each individual consumer. This shift demands strong data science capabilities and a hybrid distribution strategy, where technology and marketing functions are deeply integrated.
According to ADWEEK, the NFL (National Football League) utilizes AI to analyze player popularity trends in different countries, optimizing social media content and creative materials accordingly. By combining AI-driven analytics with automated execution, companies can significantly improve both efficiency and speed, ultimately enhancing customer engagement and satisfaction.
3. Strong Technical Leadership Will Define Future Success
As AI becomes the core driver of business, organizations must fundamentally rethink their talent strategy. Securing a strong CTO and technical leadership is paramount, as companies require expertise in model selection, pipeline development, MLOps implementation, and continuous optimization. Without a top-level strategic vision, even a company with a team of talented engineers may struggle to establish a competitive advantage.
The race for elite AI talent is intensifying, as businesses seek individuals who can keep pace with rapid technological advancements and develop high-value AI-driven products. For example, Luo Fuli, the lead researcher behind the open-source LLM “DeepSeek-V2,” was reportedly recruited with an exceptional compensation package by Lei Jun, CEO of Xiaomi, reflecting the growing demand for top-tier AI specialists.
Within organizations, AI assistants are increasingly supporting day-to-day operations, accelerating human-AI collaboration in decision-making. This trend is expected to drive a shift toward leaner, more efficient organizational structures, where a small team or even a single individual can build businesses at an unprecedented scale. The idea of a unicorn startup with a handful of employees is no longer far-fetched. Notably, Salesforce recently announced a halt in engineering hires, signaling the arrival of an era where AI-driven automation is reducing the need for large development teams.
Additionally, B2B sales roles are evolving, with technical literacy becoming a critical competency. As enterprises incorporate AI into their internal workflows, sales representatives must possess deep technical expertise to effectively diagnose customer challenges and provide tailored AI solutions. For instance, explicitly requires AI and data literacy for its Account Executive roles, underscoring the broader shift toward a more tech-driven sales function.
4. Revenue Models are Shifting from Subscriptions to Performance-Based Pricing
AI adoption is fundamentally reshaping revenue models and profitability structures. While traditional SaaS businesses rely on fixed subscription pricing based on user count or monthly fees, AI-driven companies are transitioning to usage-based and outcome-driven pricing models.
By linking pricing directly to the value AI delivers—such as improved productivity, cost reduction, or revenue growth—businesses can provide customers with clear ROI while capturing a larger share of the economic benefits their solutions create. Companies like Palantir exemplify this strategy, embedding themselves deeply in enterprise workflows and maintaining long-term contracts that extend beyond the initial software deployment phase.
However, AI-driven businesses face unique challenges, including the cost of high-quality data acquisition, model training and infrastructure expenses, security and privacy concerns, and lifecycle management complexities. Additionally, customers may seek to develop in-house AI ecosystems, reducing their reliance on external vendors. Furthermore, competition and evolving regulations add another layer of risk.
To maximize profitability, AI companies must carefully balance growth with risk management, ensuring that their pricing and revenue models remain both sustainable and scalable. Ultimately, customer satisfaction and the quality of value delivery will be the determining factors in long-term success.
5. Partnership Strategies Will Drive Competitive Advantage
In the AI era, no single company can dominate independently, making ecosystem-driven partnership strategies more critical than ever. As NVIDIA has demonstrated, building a strong network of partners and fostering AI startup ecosystems enables companies to establish themselves as industry leaders while reinforcing their platform positioning.
By adopting an ecosystem-first strategy, companies can integrate their products with third-party services, datasets, and specialized hardware, delivering a comprehensive and differentiated value proposition. This approach allows businesses to offer bundled solutions that maximize customer benefits while creating lock-in effects, reducing customer churn. The more partners involved, the greater the overall network value, making it increasingly difficult for new entrants to replicate the ecosystem.
Additionally, partnerships enable cost and risk-sharing, allowing companies to respond swiftly to changing customer needs. Open innovation facilitates the continuous integration of external expertise and cutting-edge technology, ensuring that products remain competitive and relevant. This ecosystem-driven strategy will be a key lever for AI-driven business growth.
Final Thoughts: Preparing for AI-Driven Business Transformation
AI is reshaping every aspect of business, from customer engagement and value creation to distribution, organizational structure, revenue models, and partnership strategies. While AI presents challenges, it also offers unparalleled opportunities for expansion and differentiation.
Companies that proactively embrace AI, iterate small success cases, and align stakeholders early on will secure a significant competitive edge. The key to long-term success lies in early adoption, continuous adaptation, and leveraging AI’s full potential to drive innovation and growth.
About Digital Garage & Open Network Lab
As AI reshapes industries, its adoption is becoming a critical factor in maintaining a competitive edge. Digital Garage is committed to supporting AI startups and emerging technologies through strategic investments and accelerator programs. Open Network Lab (Onlab), Japan’s first accelerator, provides funding and growth support to foster innovation and scale high-potential startups.
