Semih Asil
Industry Valley
- Thread Author
- #1
AI vendors often focus on the product itself, its promotion, and the commercial agreement. This is understandable; the product needs to perform, the value proposition needs to be clear, and the buyer needs to believe the investment is justified.
But the real test begins after the contract is signed. This is where many AI products – including agents, copilots, large language model applications, and embedded AI features – struggle to deliver the expected business outcomes.
A successful AI product isn't just installed. It must be adopted, learned, integrated, trusted, and used with discipline. Time-to-value depends on how quickly customers move from interest to repeated use, and from repeated use to measurable results. Vendors who leave this transition to chance create avoidable risks for themselves and their customers.
🎯 Create an AI Adoption Protocol
This protocol defines how the customer will introduce the solution, who will use it, which workflows will change, and how success will be measured. It should also clarify the technology's limitations, the expected role of human judgment, and when users should escalate or validate an AI output.
The protocol should include a small number of practical success metrics. These might include active users, frequency of use, completion of targeted workflows, time savings, quality of responses, error reduction, customer experience, or financial impact.
The metrics should be tied to why the customer bought the solution. A general usage goal is insufficient. The vendor and customer must agree on what productive and responsible use looks like.
This is especially important for AI agents and LLM-based solutions. Users may misunderstand what the system can do, overuse it, underuse it, or use it outside of the intended process.
A clear adoption protocol helps customers use the solution fully, correctly, and consistently. It also creates an early warning system when adoption begins to slow.
🚀 Build Onboarding for Competence, Not Just Access
The second requirement is a structured onboarding program. Traditional software onboarding often focuses on account setup, permissions, configuration, and basic training. AI onboarding must go further. It needs to build user confidence and competence as quickly as possible.
The program should be role-based:
- Executives: Should understand value, risk, governance, and success metrics.
- Managers: Should know how workflows and responsibilities will change.
- End Users: Should have hands-on practice with realistic tasks, examples, prompts, agent instructions, review steps, and escalation rules.
- Administrators: Should receive technical training on configuration, security, data access, monitoring, and controls.
Onboarding may also require integration into the customer's technology stack. The AI solution may need access to CRM data, service platforms, knowledge bases, collaboration tools, identity systems, or workflow applications.
These integrations should not be treated as a technical afterthought. They are often fundamental to adoption and value. If users have to leave their normal workflows, manually move data, or repeat tasks across systems, adoption will suffer.
A robust onboarding program should have milestones for the first 30, 60, and 90 days. The goal is not to complete training. The goal is to achieve productive use and demonstrate initial business results.
📦 Prepare the Vendor Qualification Package Before the Customer Asks
The third requirement is a complete vendor qualification package. Enterprise customers are increasingly demanding detailed reviews before an AI solution is approved. These reviews can involve cybersecurity, IT architecture, data privacy, responsible AI, legal, procurement, risk, and compliance teams. They can take up to 6 months!
Vendors should prepare these materials in advance. The package might include security certifications, penetration test summaries, data flow diagrams, sub-processors, data retention policies, model and hosting information, access controls, incident response procedures, business continuity plans, privacy documentation, responsible AI principles, model limitations, monitoring practices, and compliance attestations.
The goal is not to overwhelm the customer with documentation. The goal is to reduce friction, answer predictable questions, and demonstrate that the vendor is ready for enterprise deployment. A weak qualification package can delay implementation by weeks or months. A strong one protects time-to-value before onboarding even begins.
🤝 Customer Success Becomes a Core Product Capability
These three requirements reinforce the critical role of customer success. Customer success teams should not be brought into the process after implementation. They should be involved in the sales cycle, adoption planning, onboarding design, success measurement, and ongoing value reviews.
For AI products, customer success is also a feedback system for product management, engineering, pricing, and marketing. The team sees where users hesitate, which workflows create value, what integrations are missing, and which success metrics matter most. This information should influence the roadmap, packaging, pricing, and future product design.
The winners in AI will not be the vendors with the most impressive demos. They will be the vendors who help customers rapidly achieve value, use the solution correctly, and sustain results over time.
Adoption, onboarding, and qualification are not support activities. They are part of the product. This operational discipline also drives renewals, expansion, references, and trust, because customers can connect usage to results and see a clear path from initial deployment to scaled value.
