There were two companies with different approaches to integrating AI enterprise-wide: from bottom to top and from top to bottom. The first company (tom-tailor.ru) uses a "digital ecosystem" approach to prevent "hyper-automation." Many small services are used to empower people. A common data bus integrates them, but the main focus is on independence and people. They work closely with marketplaces and monitor trends and competitors. This is how they achieve flexibility. They take 1.5 months to check if a process is successful.

Second (government corporation unirusgroup.ru) uses the motto: “Competence center” is not a “development center.” A competence center serves as a hub for expertise and training, and is used as the primary place to adopt AI. This center gathers experts from across the enterprise and integrates their knowledge in the center. The center has a robust sandbox for developing and using AI modules. This allows them to scale and share AI knowledge and avoid silos in technical innovation.

These companies balance “patchwork automation” versus “hyperautomation”. Other observations: - Dashboards do not help but instead overload humans; meanwhile, when humans create dashboards they share their own high understanding of data. - AI is used to predict fields with increasing risk that require attention to prevent critical failures; other fields will receive less attention. This allows the company to function with limited resources. - The bottom-up approach uses significantly less time to check if new modules are successful because they are smaller here. - Architecture is a bridge between humans and AI; its role is similar to artifacts in cognitive distance.

Diagrams mentioned: - The marketing chasm (“mure-abyss”) in diffusion of innovations shows a gap between 13% active and 70% passive users during project scaling. - The Smiling Curve of Marginality: high in R&D, low in production, high in selling. - There is a “Cognitive Distance” triangle between stakeholders: business, management, and developers. Project failure is likely without balance and respect. AI or artifacts can mediate between them.

Skolkovo Research Highlights: - Memory and decision-making are distributed; all components have local memory and some autonomy (Distributed Cognition, Noosphere). - Decision-making types: expert-driven, process-driven, data-driven. Each has its niche; data-driven is not flawless. Expert-driven is akin to a LLM as a compact black-box decision maker. - Data-driven approaches as a matere process, facilitating AI applications. - 90 days for prototype testing.

Steps for Enterprise AI Development (Skolkovo, SberService): 1) Locate areas with sufficiently mature processes 2) Define metrics and establish the “Data Story” 3) Develop prototypes as modules 4) Scale and monitor

TRL and MRL metrics (Technology and Manufacturing Process Readiness Levels) was suggested to find opportunities for innovation.