In late 2022, the release of ChatGPT triggered a massive wave of AI adoption across various sectors, quickly drawing over 100 million users worldwide. While this surge in user interest led to rapid top-of-funnel growth for consumer AI apps, these same applications encountered significant challenges in retaining users, resulting in a "leaky bucket" problem. Now, leaders in the realm of B2B AI applications must grapple with similar churn issues to ensure sustainable growth. Although enterprise AI adoption has been slower compared to the consumer sector, as corporations begin to experiment with these technologies, the potential for churn has become a critical concern that must be addressed.
The Unique Challenges of B2B AI Adoption
While consumer AI applications are struggling with retention, B2B AI apps are just entering the fray, facing unique challenges and opportunities. Enterprises have more rigorous requirements regarding governance, security, and integrations, which delay widespread adoption and mean the churn phenomenon observed in consumer AI hasn’t fully manifested in the B2B sector. Despite this, B2B AI companies need to remain vigilant. Businesses that wrap their services around large AI models like GPT without offering additional value are particularly at risk of facing significant churn. To mitigate this, first-movers in the B2B AI space need to employ robust product strategies designed to build defensible and sticky products that can retain their user base effectively.
Enterprise AI adoption is currently characterized by a cautious approach, with many corporations only beginning to experiment with these applications. Rigorous internal processes and higher stakes mean that B2B AI apps have to meet stringent standards for governance, security, and quality. This slower pace of adoption has so far spared B2B applications from the leaky bucket syndrome. However, the lessons from consumer AI’s retention struggles should serve as preemptive cautions. The key lies in creating solutions that are deeply embedded into existing workflows, providing significant, demonstrable value, and addressing the specific needs and challenges faced by enterprises.
Leverage Integrations and Partnerships
For B2B AI startups, beginning as point solutions with the aspiration to grow into full-fledged platforms is a common trajectory. However, deeply embedding their technologies into existing workflows remains essential to overcoming retention challenges. Forming strategic partnerships and creating seamless integrations with large incumbents can play a crucial role in this regard. By becoming integral parts of well-established systems, these startups enhance user retention and adoption by making it easier for users to incorporate the new technology without overhauling their current processes. For instance, Sixfold’s integration into Policy Administration Systems (PAS) helps insurance underwriters use their AI features without disrupting their established workflows.
Such integrations not only simplify user adoption but also add a layer of security and reliability that enterprises seek. When B2B AI applications are integrated into pre-existing platforms and workflows, the resistance often accompanying new technology adoption significantly diminishes. Partnering with established players in the industry ensures that the AI applications meet the security and governance standards required by enterprises. This approach not only alleviates initial barriers to adoption but also seeds long-term retention by embedding the AI into daily operations, making it indispensable to users and their workflows.
Meet Users Where They Are
Reducing friction and increasing engagement can be achieved by ensuring that B2B AI products are directly accessible within platforms already part of users’ daily workflows. By embedding AI solutions into commonly used tools, B2B AI applications can maximize user retention through enhanced convenience and accessibility. Companies like Databook, Rilla, Datasnipper, and Scribe have successfully implemented this strategy by making their solutions available via platforms such as Slack, mobile apps, plug-ins for Excel, and browser extensions, thereby integrating effortlessly into familiar user interfaces.
Being accessible where users already spend a significant portion of their time streamlines the adoption process and reduces the learning curve. This makes immediate benefits more apparent and diminishes the time-to-value for users. By integrating AI functionalities into well-known tools and platforms, B2B AI companies can ensure that their solutions become part of the natural workflow of their users, thereby increasing engagement and reducing the chances of churn. This seamless integration approach aligns with the expectation of enterprise users for efficiency and user-friendliness.
Generate a Tangible Work Product
AI applications that facilitate the creation of tangible work products can drive higher user engagement and retention, as these products provide clear, demonstrable value. Tangible outputs such as reports, documents, or actionable insights validate the usefulness of the AI within enterprise workflows. For example, EvenUp leverages AI to automate the creation of demand letters for personal injury lawyers. By freeing up lawyers’ time and enabling them to handle more cases and generate additional revenue, EvenUp demonstrates the concrete value of their AI, encouraging continued use and reducing churn.
The tangible work product approach ensures that the AI solution is seen as an essential tool rather than a supplementary add-on. When users can directly see the benefits and improvements brought by the AI, it establishes trust and reliability, which are critical for long-term retention. This strategy not only proves the AI’s effectiveness but also integrates it more deeply into daily operations, turning it into an indispensable part of the users’ workflow. By producing concrete results, AI applications can build a loyal user base anchored in the tangible benefits they provide.
Build Across the Chain
Once B2B AI apps have established themselves in one part of the workflow, they can increase their defensibility by expanding horizontally across the value chain. By offering a broader suite of solutions catering to various user needs within the Ideal Customer Profile (ICP), these applications can enhance user relationships and fortify platform stickiness. An example of this strategy is illustrated by DeepL’s evolution from providing natural language processing (NLP)-based translation services to offering AI-writing tools. By addressing multiple aspects of users’ workflows, companies can deepen relationships, enhance platform defensibility, and create more value.
Expanding across the value chain allows B2B AI companies to offer comprehensive solutions that address a wider range of problems faced by their users. This horizontal growth not only captures more of the user’s interaction with the platform but also increases the perceived value and utility of the AI application. By diversifying their offerings, B2B AI companies can mitigate the risks associated with reliance on a single point of value, making their products indispensable across various facets of the user’s workflow. This multifaceted approach creates a more cohesive and integrated user experience, significantly boosting retention and satisfaction.
Create Data Moats
Creating defensible moats around B2B AI applications can be achieved by leveraging proprietary data or novel data techniques that enhance the accuracy and value of AI-generated outputs. Better data leads to superior decision-making capabilities, reinforcing user retention. Companies such as EvenUp and Databook use their unique data sets to improve the intelligence of their AI solutions, thus providing greater value to users and making it more challenging for competitors to replicate their offerings. This exclusivity creates a competitive edge and establishes a stronger bond between the application and its users.
Data moats not only enhance the quality of AI solutions but also protect against competitive encroachment. Proprietary data sources or innovative data techniques create a barrier that rivals find difficult to breach. By continuously improving their datasets and utilizing advanced data processing methods, B2B AI companies can maintain a lead in accuracy and reliability, which are key factors in user retention. This data-driven approach not only fortifies the platform’s effectiveness but also establishes trust and dependency, making users more likely to stick with the AI application over the long term.
Go Multi-Modal
Addressing the diverse range of data sources and formats present in enterprises can significantly bolster AI platforms. Multi-modal AI solutions that handle various data types can extend platform capabilities and value. For instance, Jasper’s acquisition of Clickdrop to expand from text to image generation and Coactive.ai’s approach to handling multiple forms of visual content illustrate how expanding into multi-modal offerings enhances platform utility and user retention. By catering to various data types, AI applications can broaden their scope, offering more comprehensive solutions that address multiple user needs.
Entering the multi-modal realm enables B2B AI platforms to leverage the full spectrum of enterprise data, thus providing a more versatile and valuable service. This expansiveness not only increases the relevance of the platform across different user scenarios but also enhances its adaptability. Multi-modal solutions accommodate the complexity of real-world data environments, making the AI applications more robust and indispensable to the users. By offering a wider array of functionalities, B2B AI companies can ensure that their platforms remain pertinent and useful across various enterprise requirements.
Maximize Network Effects
In late 2022, the launch of ChatGPT fueled a significant wave of AI adoption across numerous sectors, attracting over 100 million users globally in a short period. This boom in user interest led to remarkable growth at the top of the funnel for consumer AI applications. However, these applications faced notable challenges in retaining users, leading to what is often referred to as a "leaky bucket" problem. Users would try out the technology but often drift away, creating issues in maintaining steady engagement.
Now, leaders in the B2B AI space are encountering similar retention issues that threaten sustainable growth. Although enterprise-level AI adoption has trailed behind the consumer sector, companies are now starting to explore these technologies more earnestly. With this increased experimentation comes the heightened risk of user churn, a problem that, if left unaddressed, could hinder long-term success.
To counteract this risk, B2B AI leaders must develop strategies focused not just on attracting new users but also on keeping them engaged over time. This could involve enhancing user experience, providing continuous value through regular updates and personalized features, and offering robust customer support to help companies integrate AI effectively into their operations. Only by addressing these retention challenges can B2B AI applications hope to achieve lasting, sustainable growth in a rapidly evolving technological landscape.