Successful AI-driven Business Strategies: Business Adapting to the AI revolution

In a recent article on AI Journal, Radix IoT co-founder Michael Skurla shares overcoming AI stumbling blocks, and building a sustainable AI foundation through effective data aggregation across your infrastructure.

The AI tsunami has rushed agile businesses to adjust to integrated AI-driven business strategies, aiming to automate various tasks to empower decision-making through data analysis while increasing efficiency, innovation, and customer-centric operations. AI use by organizations increased to 78% in 2024, with 83% of companies in 2025 considering AI use in their business strategies as a top priority.

But before riding the AI-driven wave, businesses must first grasp what drives AI and how best to leverage it to meet specific business objectives.

Overcoming AI Stumbling Blocks

The first stumbling block of any organization embarking on an AI journey are the goals–which sound simple but are often the most complex. Oftentimes organizations are awash with ideas but struggle to pin down the exact KPIs they want achieved. In addition to being time consuming and complicated, this often involves many stakeholders, various objectives, and goals that must be conceptualized as a digestible reality to be accomplished. The best path here is to outline the overarching objectives, into a staged roadmap of deliverables.

This, however, leads to the most critical questions: what type of data is available, and how is it collected to achieve these goals. Since raw data is the foundation of any AI implementation, accessing that data is a step most often overlooked by organizations.

Data collection and normalization are the key to success in enabling any practical AI solution.

This prioritizes businesses to select data aggregation solutions that provide real-time access to relevant and critical data from all their connected systems, equipment, and sensors. Expediting data collection is essential and can harness actionable analytics. Bridging data sources and AI systems will solve data integration problems across multiple protocols, formats, and data storage separations and provide highly informed and reliable AI responses.

The data collection solution must also have the capability of gathering and normalizing data from a wide range of communication protocols (BACnet, Modbus, MQTT, SNMP, and DNP3, etc.) and work seamlessly across both legacy systems and modern devices. Only then can AI engines effectively process a unified data stream since AI systems perform best with standardized and normalized data. To maintain contextual relationships, data must be reformatted–or rationalized beyond the proprietary formats or inconsistent data structures. Proper data organization and storage through contextual relationships is critical, considering that traditional data lakes often suffer from “data swamps” and struggle with data consistency and relationships, which lead to fragile and difficult to maintain AI solutions long-term. It is important to understand that regardless of the original KPIs outlined, a good data repository allows for agile changes and improved utilization of AI infrastructure investment over time.

Reliable Data Aggregation Builds AI Foundations

Once businesses understand the critical role of data aggregation as the foundation for building accurate and reliable AI models that provide accurate insights from consolidated data, then they can reap the best value by integrating AI toolsets into their operational processes to foster a myriad of business objectives, including:

• Operational Predictive Analytics: Improves expedited business decisions by eliminating data silos so aggregated and analyzed historical data can help predict future events and meet challenges/risks before they pose irreversible issues. By leveraging data, AI tools can forecast business trends directly linked to customer /end-user demands based on specific market conditions. Data queried for real-time analysis or historical pattern recognition through a standard Open API connection can enable proactive decisions, managing risks while optimizing existing resources.

• Optimizing Supply Chain: By managing inventories, businesses can forecast demand and supply chain logistics, leverage existing structured data analytics to predict demand patterns, and streamline procurement and inventory. AI-powered real-time sensors tracking can improve logistics since access to critical data optimizes efficiency and lowers operational costs by reducing waste. Maintaining coherent data across all connected business sources can help manage various generations of equipment from different vendors.

• Preventive Risk Management: Access to real-time, structured data can help identify patterns indicative of security or compliance risks. AI alerts can flag and reverse anomalies and risks and meet security compliance.

• Automating Repetitive Tasks: Businesses can reduce time and costs by customizing reports to meet the specific stakeholders’ needs. Focusing on higher-value tasks and reports can increase operational efficiency, providing time and energy cost savings.

• OpEx and ESG Reporting: Aggregating operational data across various portfolios to gain full access to historical operational data, businesses can enable AI to optimize budgeting, forecasting, financial analysis, and find cost-saving opportunities for more accurate financial models for budgeting and forecasting. For energy-intensive businesses needing to streamline their environmental, social, and governance (ESG) reporting process, AI can leverage actionable consolidated data to generate comprehensive ESG reports with detailed sustainability metrics and provide ongoing data monitoring and analysis. With real-time access to key datasets, businesses can identify unusual energy usage in real-time, analyze historical data, set benchmarks, and generate alerts for significant fluctuations for expedited, informed operational decisions.

• Threat Detection: AI-driven threat detection and response systems, empowered with data aggregation platforms, can identify patterns in network traffic or user behavior in real –time, and flag cybersecurity threats. With expedited response time to potential threats, alerts, and reaction time, businesses can mitigate breaches faster, build resilient backup plans, and eliminate/reduce costly downtime.

• Business Intelligence: AI-driven tools for processing and visualizing business data provide the best insights by analyzing data from across sources to identify patterns and generate data-driven actionable insights. AI-driven tools also provide more intuitive reports and dashboards for highly accurate, data-driven decisions to empower businesses to make sound financial decisions.

An effective data-driven approach for AI applications can only be realized when a data aggregation platform is integrated to provide real-time and historical data at scale and process queries significantly faster than traditional database solutions used in the BMS space. Only then can AI engines, with access to comprehensive datasets, function cohesively without the AI engine needing to understand the entire data ecosystem format. Accessing normalized data allows AI engines to connect to one source of data instead of understanding a multitude of equipment and systems. Allowing fast and relevant access to data without custom development of connectors or bridging technologies is still at a fragile and complex stage–but critical to an AI-driven business strategy.