Blog: Why Do AI Startups Succeed?
Why Do AI Initiatives Succeed?
AI Startups (and undergoing an applied AI transformation) succeed when decision makers like you try to better understand AI, including its benefits, opportunities, potential applications, and challenges. AI initiatives also succeed when the WHY behind them is clearly and concretely established, is aligned to goals for both people and business, and is leveraged as the north star that guides everything else.
Further, AI initiatives succeed when the right data and analytics organization is prioritized and built (some recommendations for which we cover in this book). This includes leadership, organizational structure, and talent that fill strategically appropriate analytics roles and responsibilities. This type of organization is able to:
- Identify and prioritize AI opportunities
- Help prioritize company-wide investment in AI
- Cultivate AI adoption and alignment
- Properly set expectations around AI initiatives
- Generate a shared vision and strategy around AI
- Help break down silos
- Democratize data and analytics
- Help continually advance the organization’s data and analytics competency
- Foster a cultural transition from a gut-driven, historical precedent-based organization to a data-driven and/or data-informed organization
- Build, deliver, and optimize successful AI solutions
Additionally, successful data and analytics organizations are able to properly assess their organizations AI readiness and maturity level, identify gaps, and develop a prioritized strategy for filling in those gaps. They are also able to analyze specific key considerations and any associated tradeoffs on a initiative-by-initiative basis, similarly identify gaps and prioritize filling them, and also make the right decisions as needed throughout the initiative’s life-cycle.
Data and analytics organization members must be able to work cross-functionally and collaboratively with experts from all functional areas of an organization in strategic ways, and as needed. AIPB uniquely defines a high level set of cross-functional experts that must work together during certain phases of AI initiatives to ensure successful outcomes.
Creating a real world deliverable that delivers on its intended benefits requires an effective sequence of iterative phases, which the AIPB framework uniquely defines in the context of AI. Each of these phases has a related output defined by AIPB as well, and all of which are key ingredients of successful AI solutions. Understanding concepts that we’ll discuss such as scientific innovation, particularly in the context of AI contribute to success as well.