Assessing the effectiveness of AI solutions implementation at SILA Union

Measuring the economic return on technological solutions is a challenging business practice in 2026. How can one assess whether the implementation of artificial intelligence will yield real results for a specific business? Where can AI truly cut costs, optimize routine tasks, and dramatically increase margins? At the III International Forum "AI – The Future Today" on the sidelines of SPIEF 2026, SILA Union founder Elena Silkina addressed the main pain point for corporations and presented a structured approach that allows organizations to make informed decisions about the use of neural networks and predict the impact before investing.

The scale of the forum's discussions reflected the pervasive penetration of technology into all sectors of the economy. The forum brought together leading experts and developers, hosting 18 thematic sessions covering the entire spectrum of industries—from traditional fuel and energy, agriculture, manufacturing, and construction to finance, healthcare, robotics, and creative industries. Particular attention was paid to attracting investment in AI projects and the industrial integration of RPA solutions, while discussions on the role of algorithms in creation extended to specialized cultural formats. For businesses, artificial intelligence is already becoming an applied tool in all areas of activity.

A systems approach instead of individual solutions

The development of artificial intelligence is setting new standards for competitiveness. As Russian President Vladimir Putin noted, neural networks, along with digital platforms, are shaping a fundamentally new economic landscape, and a country's ability to adapt to these changes directly impacts its sovereignty and position in the global market.

However, in practice, organizations regularly face the same situation: expectations for AI implementation outpace the maturity of internal processes and data quality. As a result, technologies fail to realize their potential, and fundamental management issues remain unresolved. The "AI – The Future Today" forum was dedicated to precisely this problem—the transition to a conscious, economically sound use of neural networks.

The key conclusion of the expert discussions: the success of AI projects is determined not by the power of algorithms, but by the quality of corporate architecture, the alignment of business processes, and the availability of data.

Three-step methodology: from analysis to measurable results

During the forum's business program, Elena Silkina presented a practical methodology that allows organizations to consistently prepare for AI implementation and assess the expected impact in advance. The approach consists of three logically interconnected stages.

Stage 1. Forming a holistic picture of the current state of the organization

Before implementing new tools, it's essential to gain a clear understanding of how the company is structured today. At this stage, business processes, corporate architecture, IT landscape, performance metrics, and interrelationships between business elements are analyzed. This analysis allows us to identify processes that truly benefit from automation and pinpoint application areas for AI solutions.

Stage 2. Modeling the future state and assessing the effect

The second stage is dedicated to change design. The organization determines which processes need to be transformed, which tasks can be automated, how the structure will change, and what metrics need to be achieved. The SILA Union platform allows for the creation of a company's corporate architecture and the simulation of the impact of AI implementation in a secure environment—before actual resources are deployed. This enables early assessment of the economic impact and the adjustment of the implementation plan.

Step 3: Informed Selection of AI Tools

Only after completing the first two stages is specific technological solutions selected. This approach allows for targeted AI application—specifically in areas where modeling has confirmed the achievability of target indicators. The organization understands what tasks will be solved, what changes will occur, and how the final results will be measured.

Digital twin as a basis for management decisions

The presented approach is based on the SILA Union platform, a solution for modeling corporate architecture and digital transformation. The platform creates a unified information space that structures data on business processes, organizational structure, IT systems, strategic goals, and risks.

For businesses, this means the ability to:

- assess the impact of changes before they are implemented;

- calculate the potential impact of AI implementation projects;

- improve the quality of planning and the validity of management decisions;

- develop systematic work with data and processes.

Practical value for organizations

For company executives, maximizing the impact of projects and technologies while minimizing risk is paramount. Key business priorities are crystal clear: reducing operating costs, reallocating freed resources to strategic tasks, increasing the speed of decision-making, and, as a result, strengthening market positions. To achieve these goals, organizations rely on a transparent framework: understanding how internal processes are structured, where inefficiencies lie, and how changes will impact financial performance. Without quality data, a well-established corporate architecture, and measurable evaluation criteria, any investment in neural networks remains justified only on a gut feeling.

This is why the SILA Union digital solution has been implemented by most enterprises: companies use the platform to calculate the cost effectiveness of AI solutions before implementation. This approach allows organizations to save money by abandoning clearly unprofitable initiatives, reallocate resources toward the most promising areas, and build sustainable leadership in their industries. Effective implementation of artificial intelligence requires a clear methodology with predictable results. Companies that build their AI operations on this foundation gain both a technological advantage and a systemic tool for managing business performance.

Elena's interview and more details on the topic https://rutube.ru/video/95e9e8b3b97329f6e106e0cb90d0f398/