Though artificial intelligence has been making inroads into the enterprise, the rise of generative AI is accelerating the pace of adoption. It’s time for enterprise CXOs to consider building systems of intelligence that complement systems of record and systems of engagement.

In the last two decades, enterprises have invested in building solid foundations for managing data and information. Relational databases such as Oracle and Microsoft SQL Server became the cornerstone of information systems. Built on this foundation were customer relationship management, human resources management, supply chain management and other line of business applications that quickly became the digital backbone of an organization. Data warehouses and business intelligence systems enabled enterprises to transform information into insights. These investments led organizations to create systems of record that acted as a single source of information for all stakeholders.

Examples of systems of record include enterprise resource planning systems, customer databases, financial management systems, electronic health records in the healthcare industry, and document management systems, among others. These systems play a crucial role in supporting operational efficiency, decision-making and regulatory compliance within organizations.

If systems of record transformed data into information and insights, systems of engagement made them accessible to employees, customers and partners. This layer unlocks the potential of information by making it accessible via desktop, web and mobile experiences.

The primary purpose of systems of engagement is to enable meaningful and dynamic interactions, foster relationships, and enhance collaboration among stakeholders. It provides tools and capabilities that facilitate real-time communication, information sharing, feedback gathering, and collaboration across multiple channels and devices.

Examples of systems of engagement include customer relationship management systems, social media platforms, collaboration tools like project management software and enterprise social networks, customer self-service portals and online communities. These systems play a vital role in building and maintaining relationships, enhancing customer satisfaction, supporting employee collaboration and fostering engagement with various stakeholders.

The rise of AI-assisted chatbots and agents is forcing enterprise CXOs to build a new layer that acts as a system of intelligence.

A system of intelligence refers to a framework or infrastructure that combines data, analytics, and artificial intelligence technologies to generate insights, make informed decisions, and drive intelligent actions within an organization. It leverages advanced analytics techniques, machine learning algorithms, natural language processing, and other AI capabilities to extract valuable insights from large volumes of data and enable data-driven decision-making.

The systems of intelligence significantly overlap with the systems of record and engagement. It uses the internal systems of record as a single source of truth to deliver insights through new interactive channels that integrate with the existing system of engagement.

The primary goal of a system of intelligence is to transform raw data into actionable intelligence, empowering organizations to gain a competitive edge, improve operational efficiency, enhance customer experiences, and drive innovation. It goes beyond traditional business intelligence systems by incorporating generative AI and natural language processing to automate data analysis, uncover patterns, predict outcomes, and provide intelligent recommendations.

Some of the key characteristics of systems of intelligence include:

Data Integration and Aggregation: SOIs integrate and aggregate data from various sources, including structured and unstructured data, internal and external data and real-time or historical data. This enables a holistic view of the organization’s data assets for analysis and intelligence generation.

Advanced Analytics and AI Techniques: SOIs employ advanced analytics techniques, machine learning algorithms, statistical modeling, and AI capabilities to extract insights, discover patterns, detect anomalies and predict future outcomes. These techniques enable organizations to uncover hidden patterns in the data and make accurate predictions.

Automation and Intelligent Decision-Making: SOIs automate data analysis processes and decision-making through AI technologies. They can autonomously process and analyze large volumes of data, identify trends and generate intelligent recommendations to support decision-making processes.

Real-Time and Predictive Insights: A System of Intelligence provides real-time and predictive insights, enabling proactive decision-making. It can analyze data in real-time, detect emerging trends, and predict future outcomes allowing organizations to take timely actions and capitalize on opportunities.

Actionable Outputs and Integration: SOIs deliver actionable outputs, such as visualizations, reports, alerts, and recommendations, to stakeholders across the organization. They often integrate with existing systems and processes to deliver insights and recommendations directly into operational workflows.

The SOI will enable enterprises to build fraud detection systems, predictive maintenance systems, recommendation engines, intelligent virtual assistants and AI-powered analytics platforms. These systems enable organizations to harness the power of data and AI to gain valuable insights, make informed decisions, and drive intelligent actions, leading to improved operational efficiency, better customer experiences and enhanced business outcomes.

If RDBMs and BI systems are the foundation of systems of record, vector databases and semantic search become the key elements of systems of intelligence. A vector database converts structured and unstructured data into a set of real numbers, called vectors, that are stored in an index database that supports fast retrieval. It also maintains the metadata and catalog that cite the source in existing systems of record. A semantic search service leverages the vector database by performing similarity searches and retrieving contextual information.

This context, when combined with advanced prompt engineering, helps enterprises build intelligent AI-based assistants on the lines of Microsoft Copilot or Google Duet AI.

The foundation models become the core of systems of intelligence. The contextual information generated via semantic search is fed to these generative AI models, which deliver rich insights and accurate information to users. The use cases aligned with SOI go beyond typical chatbots. Different teams within an organization will use them to handle a range of scenarios, from marketing to sales forecasting.

The next generation of platforms, tools, and cloud services will be focused on enabling businesses to build and consume systems of intelligence. Platform and cloud providers are rapidly developing tools and services to support this trend.