Use Cases of Enterprise AI
Enterprise AI Systems can be an extremely valuable addition to any organization's operations if deployed contextually and properly. The most promising areas where Enterprise AI capabilities can be put to use include the following:
1. Banking & Finance
Businesses must realize that while looking for the most effective ways to leverage AI capabilities, various malicious actors are doing the same. This is particularly relevant in the financial sector. Enterprise AI capabilities enhance security mechanisms via proactive fraud detection and risk management. Modern Enterprise AI capabilities combine traditional ML algorithms with advanced LLMs to enhance security mechanisms through proactive fraud detection and risk management. These systems can now process both structured transactional data and unstructured data sources like customer communications, social media activity, and news feeds to identify complex fraud patterns and potential risks.
Other uses for Enterprise AI capabilities, such as credit risk assessments, can simulate and assess each user's risk profile and present deep insights into whether a particular loan or financial service would be a good investment. Such capabilities can introduce unprecedented degrees of precision decision-making within the organization and allow informed operations where risk and reward are appropriately assessed and rewarded.
2. Customer Service
Customer service is one area where almost every major organization can benefit from implementing Enterprise AI capabilities to some degree. Automating responses and providing personalized service to each user via virtual assistants ensures that inquiries can be handled and resolved instantly, increasing overall operational efficiency and customer satisfaction.
Furthermore, Natural Language Processing (NLP) capabilities allow all such inquiry responses to be human-like or as close to human-like language as possible. AI-enabled analytics can also be used to gain insights into user preferences, behaviors, and needs, allowing products and services to be tailored to meet them.
3. Supply Chain Optimization
Efficiency, accuracy, and cost-effectiveness are some of the most immediate benefits of Enterprise AI. More than any other sector, these benefits can lead to significant productivity improvement within an organization's supply chain. Enterprise AI Systems can optimize almost all aspects of the supply chain, such as routing, inventory management, delivery schedules, delivery partners management, and resource allocation. Demand and supply patterns can be thoroughly analyzed, with potential disruptions being identified well before they can cause significant damage and inventory costs minimized with chances of overstocking or stockouts being eliminated. LLMs can analyze supplier emails, customer feedback, and market reports alongside traditional metrics to predict potential disruptions and optimize routing decisions.
4. Predictive Maintenance
Enterprise AI can play a critical role in internal and external maintenance schedules to ensure that all equipment can be monitored precisely. Machine sensors and equipment performance across various metrics can be leveraged to proactively predict when the equipment may need maintenance, thus preventing unexpected equipment failure and downtime while extending the lifespan of all such machinery.
Modern systems leverage LLMs to process equipment manuals, maintenance histories, and technician notes alongside traditional sensor data to accurately predict maintenance needs. Using these insights, maintenance activities can be carried out when necessary to reduce maintenance costs while lowering the chances of equipment malfunctions.
5. Energy Management
Energy management has become a vital consideration for organizations adopting sustainable business practices. Enterprise AI Systems now analyze both structured energy consumption data and unstructured information sources, such as weather reports, building usage patterns, and occupancy feedback, to optimize energy usage. Peak demand times can be determined, and systems can be adjusted to avoid overuse and maximize off-peak rates.
Energy fluctuation points can be predicted from multiple sources, with storage and distribution being optimized to leverage the opportunities presented by smart grids and other renewable energy sources.
Challenges in Adopting Enterprise AI
The most immediate and critical challenges an organization faces when adopting Enterprise AI are both strategic and technical in nature. On the strategic side, organizations must focus on defining a comprehensive AI strategy, allocating appropriate budgets, and building a skilled team, among other priorities. On the technical front, the challenges include the following:
1. Connecting Fragmented Data Sources
Most organizations lack safe methods to connect and ingest data, particularly unstructured data, from their diverse systems. They also struggle to utilize their proprietary data for GenAI projects. Siloed data hinders the selection and ingestion process and poses challenges in ensuring data integrity, quality, and consistency. Moreover, building pipelines to integrate fragmented data becomes increasingly complex with each additional data source.
2. Protecting Sensitive Data
When implementing enterprise AI systems, AI teams face significant challenges in managing data privacy, security, and governance. Sensitive data must be protected with robust controls to ensure compliance and avoid misuse. Maintaining access entitlements throughout AI pipelines is crucial to prevent unauthorized data access.
Deploying enterprise AI systems without mature AI-specific security and governance frameworks can expose the organization to security risks, regulatory non-compliance, and misuse of sensitive data.
3. Defending Against AI Vulnerabilities and Attacks
Building enterprise AI systems introduces new AI-specific attack vectors, such as prompt injections, data poisoning, and unauthorized access. To safeguard against these threats, it's essential to implement robust LLM firewalls, ensure compliance with corporate policies, and prevent sensitive data leaks. Continuous monitoring and rapid response mechanisms are also crucial for detecting and mitigating emerging risks.
4. Ensuring Data Quality for Enterprise AI Systems
The success of enterprise AI systems depends on the quality of data fed into them. It is essential to carefully curate, label, and validate data to ensure it's accurate, relevant, and consistent, leading to reliable AI insights. Effective data governance and managing risks like format mismatches and redundancy are essential to maintaining AI model effectiveness and trustworthiness.
5. Tracing Provenance
To build trust and ensure transparency, tracking the entire data journey within an enterprise AI system is crucial. From source to AI-generated results, full visibility into data lineage can enhance control, optimize operations, and boost confidence in AI-driven outcomes. The traceability also helps identify and address potential issues early, ensuring the reliability of AI systems.
6. Complying with Regulatory Requirements
Enterprise AI systems must navigate the evolving regulatory landscape, including the EU AI Act and NIST RMF, and adapt swiftly. Strong governance and integrated regulatory mechanisms are essential to mitigate legal, reputational, and financial risks, ensuring ethical and safe AI innovation. Staying ahead of regulatory changes helps build trust and fosters responsible AI development.
How to Implement Enterprise AI
While the exact Enterprise AI implementation strategy will differ for each organization, the following fundamental steps serve as a solid foundation.
- Well-Defined Objectives: Setting well-defined, specific, measurable, achievable, relevant, and time-bound (SMART) objectives ensures alignment with business goals. It also guides the successful prioritization and implementation of Enterprise AI initiatives.
- Data Readiness: Ensuring data readiness is crucial for Enterprise AI, which requires high-quality, accessible, and secure data. Additionally, scalable and optimized infrastructure is necessary to ensure effective, ethical AI outcomes.
- Right Technology with the Right Partners: Choosing the right Enterprise AI solution is essential for successful AI adoption. Equally important is selecting trusted partners, requiring thorough analysis of their capabilities, scalability, and alignment with business goals.
- Safe AI Integration: Integrating AI safely requires thorough mapping of the organization’s IT infrastructure, security measures, and regulatory compliance status. This process involves seamless collaboration between departments, including AI experts, IT staff, and business leadership, along with effective communication to prevent disruptions and ensure AI solutions significantly enhance existing operations.
- Skilled and Empowered Teams: Choosing skilled AI professionals and fostering a culture of continuous learning are vital for ensuring the successful adoption of AI within the organization.
- The Right Pilot Project: Initiating AI adoption with a focused pilot project enables organizations to identify challenges, demonstrate impact, and gather insights for scaling AI across operations.
- Continuous Monitoring & Evaluation: Regular monitoring with updates based on real-time feedback and evolving needs ensure optimal performance, accuracy, and efficiency of Enterprise AI.
How Securiti’s Gencore AI Can Help
Securiti's Gencore AI streamlines enterprise AI implementation by seamlessly integrating data from diverse unstructured and structured systems, ensuring policy alignment, safeguarding data and AI security, and delivering continuous operational visibility.
Gencore AI offers the following building blocks, which can be used for a broad range of use cases:
- Data Selection & Ingestion: Safely ingest data using hundreds of native connectors. Define data scope and automatically learn enterprise controls, including access entitlements, for later application at the AI usage layer.
- Data Classification & Sanitization: Classify and redact sensitive data on-the-fly, ensuring privacy and compliance before AI model ingestion.
- Data Vectorization: Create custom embeddings with metadata for vector databases using an embedding model of your choice, preparing enterprise data for LLM use.
- LLM Selection: Select from a wide range of LLM models to build an Enterprise AI system that aligns with the business goals and operational requirements for a specific use case.
- LLM Firewalls: Protect AI interactions with natural language conversation-aware firewalls. Implement policies to block attacks, prevent data leaks, and maintain corporate alignment.
- Enterprise AI System Provenance: Visualize sensitive data flow and generate audit trails. Map interrelations between data, AI models, entitlements, AI agents, and governance controls.
