Customer Enablement in AI
Onboarding Strategies for Enterprise AI Adoption
Hello! I work for a generative AI venture studio and partner with entrepreneurs and leaders around the world. My recent interests are AI, fintech, e-commerce, and software. Feel free to reach out to me at yuto-saeki@garage.co.jp for collaboration opportunities.
When considering the importance of onboarding in the AI era, its role has become more critical and increasingly complex. While the significance of onboarding has been emphasized since the SaaS era, adopting AI products, especially for enterprise use, presents a unique set of challenges.
Some of the key challenges enterprises face when implementing AI products include:
1) Data Preparation and Customization
To align AI models with a company’s specific operations, there is a need for robust data integration, cleaning, and structuring. This data preparation ensures that the AI solution can be customized to fit the unique workflows of each business.
2) Change Management
Training and upskilling client teams are essential for the successful adoption of AI solutions. Shifting from traditional methods to AI-driven processes often requires a significant cultural change, which can place a substantial burden on the customer.
There are various other challenges, but particularly in terms of change management, enabling clients to unlearn existing workflows, or introducing new tools as truly valuable additions—or even replacements—within organizations where digital tools are already deeply embedded in their operations requires a meticulous and strategic approach. How are AI startups addressing these challenges?
Many companies adopt two distinctive strategies to tackle these issues.
1) Building Industry-Specific Implementation Teams Early On
Companies like Hebbia (Series B) have adopted the strategy of hiring professionals with deep expertise in specific industries to ensure successful AI implementation. Hebbia, for example, has Engagement Managers with a deep understanding of workflows in sectors such as legal, real estate, and government, acting as trusted advisors and leaders in AI for senior stakeholders. Other companies like 11x.ai (Series A) and Norm AI (Series A) have also embraced similar positions to optimize their AI deployment strategies.
2) Creating Roles Focused on Delivering the “First Wow” Experience
Recently funded Series A companies are increasingly hiring Implementation Managers, Sales Engineers, and Solution Engineers to facilitate data migration and provide user training. These roles are designed to deliver a powerful initial impact for customers, ensuring a seamless onboarding experience. Notable examples include startups like Pallet (Series A), Operant AI (Series A) and Bland AI (Series A) which have prioritized these positions to drive early customer success.
As companies mature and scale, they tend to invest heavily in customer education and engagement, catering to a diverse client base ranging from enterprises to SMBs. For instance, Clari (Series F) has launched initiatives like “Digital Enablement,” leveraging Gainsight to build communities and share best practices. Similarly, Toast and Apollo.io offer user communities and training platforms that empower customers to maximize the value of their products.
The Importance of Enablement in AI Product Success
Education and support are crucial for the success of AI products, ensuring that customers can fully leverage the technology. Canva’s co-founder and CPO, Cameron Adams, highlighted this point in Lenny Rachitsky’s blog:
“All of this underscored to us that AI tools require a combination of intuitive product design and broader, ongoing education to support these behavior shifts. ”
In the AI era, onboarding goes beyond merely implementing a product; it is a strategic initiative aimed at helping customers adapt to new ways of working and unlocking the maximum value of AI. Companies must deeply understand their customers, build trust, and provide comprehensive support to ensure that AI delivers its full potential.
Scale AI’s Sales Nearly Quadrupled in First Half by The Information.
Why AI companies don’t always scale like traditional software startups by VentureBeat. AI startups may have lower gross margins, scaling challenges and weaker defensive moat.
AI start-ups generate money faster than past hyped tech companies by Financial Times.
Character AI Scraps Building LLMs After Google Deal by Enterprise Management 360.
Where is the Budget for AI Coming From? by Tomasz Tunguz.
