Adopting C3 AI successfully requires a structured approach that balances internal capacity-building with external expertise. Based on our experience working with a major US power company, we have identified key principles that drive effective adoption. The decision to engage external resources should be based on three key factors: complexity of the use case, the speed at which the client wants to capture value, and the internal team’s capability to scale.
Organizations do not necessarily need to begin with simple diagnostic dashboards, but with our client, that was the natural progression. Over time, the complexity of requests increased, transitioning from diagnostic analytics to predictive analytics, necessitating a shift in team composition and skill sets. The right approach involves strategically phasing in external expertise while building internal competency to sustain long-term success.
Understanding when to use internal vs. external resources
Key considerations:
- Complexity of the work: Some aspects of C3 AI (ex. back-end data integration) can be learned internally over time, while others (ex. front-end UI development, deep C3 AI platform expertise) typically require more time could therefore benefit from external support.
- Speed to value: If rapid deployment is required, leveraging external expertise can accelerate progress. If the organization has a longer runway, internal upskilling may be a viable strategy.
- Ongoing platform support: Regardless of team composition, ongoing C3 AI platform support is essential to address unexpected challenges and maintain system performance.
Best practices:
- Use internal resources for back-end development where there is existing expertise in JavaScript and C3 AI data modeling, leverage external resources to address gaps.
- Leverage external experts for UI development due to the complexity of custom front-end work in React. Knowledge transfer in UI can be executed in parallel
- Maintain a mix of internal and external resources for data science depending on the predictive complexity and time sensitivity of the use case.
Structuring teams for different phases of adoption
Phase 1: Initial Setup & Diagnostic Analytics
- Focus on simple dashboards and reporting screens.
- Internal teams can manage most of the back-end work.
- External experts can help navigate initial C3 AI platform complexities.
Phase 2: Expansion into predictive & prescriptive analytics
- Requires the introduction of data science expertise.
- Complexity increases, necessitating a blend of internal and external capabilities.
- Front-end UI limitations may require custom development.
Phase 3: Scaling & long-term sustainability
- As internal teams gain expertise, external reliance should decrease except for specialized skills.
- Governance structures should be in place to ensure sustainable knowledge transfer and best practices.
Case in point: Our client’s team composition over time
Resource mix example
| Phase | Internal Team | External Support (Fractal, C3 AI, West Monroe) |
|---|---|---|
| Early stages (diagnostic dashboards) | Back-end engineers, business analysts | C3 AI platform support, Fractal UI / BI developers, Data Architects |
| Predictive analytics expansion | Data scientists, software engineers | Fractal UI developers, Supplemental Fractal DS and Data Architects, C3 AI support for complex components |
| Scaling & operationalization | Increased internal ownership, expanded data science team | Ongoing C3 AI support, selective external UI / DS / DA expertise |
This evolution highlights the necessity of flexible staffing models, adjusting the mix of internal and external teams based on workload, skill gaps, and required speed to market.
Managing knowledge transfer for long-term independence
One challenge we observed with the client was the difficulty of structured knowledge transfer, particularly in UI development. While back-end development knowledge was gained organically through hands-on work, UI development remained a gap due to limited internal expertise and time constraints.
Best practices for knowledge transfer:
- Formal training sessions must be scheduled and protected from operational distractions.
- Shadowing and hands-on practice are critical—learning cannot be solely theoretical.
- Dedicated internal ownership of key C3 AI components is necessary to avoid over-reliance on external vendors.
- Encourage standardization of reusable UI components to minimize complexity and ensure maintainability.
- Recognize workload constraints—Knowledge transfer is often deprioritized due to high demand for development resources.
The role of external partners in maximizing ROI
External resources are not just a staffing solution—they provide a strategic advantage when used effectively.
Key value drivers for external expertise:
- Accelerated time-to-value: Faster implementation of new use cases and iterations.
- Deep C3 AI platform knowledge: Mitigates roadblocks that could slow down internal teams.
- UI & data science specialization: Enables custom solutions beyond C3 AI’s default components.
- Adaptability to increasing workload demands: External teams can supplement internal staff during peak development periods.
Conclusion: A balanced approach to adoption
Adopting C3 AI is not just about implementing a platform—it’s about creating a scalable, sustainable, and high-impact analytics capability.
Organizations must:
- Assess internal skill gaps honestly.
- Determine how quickly they need to deliver value.
- Engage external partners strategically to supplement internal efforts.
- Address knowledge transfer systematically, particularly in more complex areas such as UI development or predictive analytics.
By following these principles, companies can navigate the complexities of C3 AI adoption while ensuring they build long-term competency and maximize their return on investment.
If you’d like help integrating C3 AI into your company’s technology stack, contact us today.
