New research from MIT shows that organizations leading in real-time business capabilities¹ achieve:

• 20% better growth of new products and services
• 22% better operating efficiency
• 17% better risk management

AI and AI agents help businesses improve operations, innovate better, and scale with less risk. In other words, it makes those businesses more real time than their competition.

According to the MIT research: “Excellence in four capabilities—real-time data for decision-making, integrated customer experience, business agility, and high-quality employee experience distinguishes real time businesses from the rest.” See Figure 1.

GenAI and AI provide real-time access to data at a speed and scale that were previously out of reach. This not only helps speed up decision-making and improve efficiencies but also enables more organizational capacity to grow the business. Real enterprise examples:

• Blue Cross Blue Shield of Michigan showed over $10,000,000 savings in their contract reviews using GenAI to make unstructured data available.
• GetJerry.com deployed five chatbots to handle customer service requests over text and SMS, moving responses from 100% handled by humans to just 11%, which saves $4,000,000 a year and improves customer satisfaction.
• KoBold Metals, a firm that uses AI to create a superior process to discover superior locations to mine, recently discovered a huge copper deposit in Zambia which will produce 300,000 tons or more per year².

The enterprise data readiness problem

The evidence of firms being able to drive more real-time decisions with the help of GenAI/AI, even at this early stage, is compelling across industry and function. Customers get answers faster, talent gets training faster, and decision quality improves. However, key roadblocks prevent organizations from fully realizing real-time business capabilities. One of the biggest roadblocks to overcome? Data readiness.

• Unstructured data: An estimated 80% of enterprise data is unstructured, making it inaccessible for real-time decision-making
• Incomplete data: Missing or incomplete data delays model development
• Policy and guardrail alignment: Models and data often lack alignment with organizational policies, resulting in risks and inefficiencies
• Siloed processes: Traditional AI development is fragmented across data preparation, training, tuning, and deployment, slowing down the process

Data is the foundation of enterprise AI applications, yet preparing it for LLM consumption is costly and complex. The quality, amount, and format of your data can all mean the difference between getting to production and going back to the drawing board.

Seekr addresses these challenges by simplifying the entire lifecycle of AI development, from inception to deployment. Here’s how:

Efficient AI workflow management

SeekrFlow centralizes the AI lifecycle into a single interface, allowing businesses to manage every step efficiently. This horizontal approach breaks down silos by integrating data preparation, model training, tuning, and deployment. Additionally, its compatibility with diverse hardware and cloud environments optimizes workflows and reduces costs.

Cutting data prep time from months to days

Enterprise AI projects are being stalled by costly and time-intensive methods to gather, label, and structure data for AI applications. With the Seekr AI-Ready Data Engine, enterprises can accelerate model deployment by not only structuring their business data but also ensuring it’s in a format AI can learn from. Seekr uses synthetic data generation to compensate for gaps in datasets, enabling faster and more precise model fine-tuning. This ensures models are ready to handle real-time business demands.

Transparent and reliable model validation

Seekr’s validation tools align model outputs with organizational policies and guardrails. Using token-level error detection and prompt comparisons, SeekrFlow dynamically enforces these principles, ensuring that models operate safely and reliably. This transparency instills confidence in launching AI applications for mission-critical, real-time operations.

Rapid, reliable deployment

The five-click deployment process makes launching models straightforward and error-free. Whether deploying on dedicated or serverless infrastructure, Seekr’s system ensures models are operational in minutes. By providing seamless integration, Seekr ensures unstructured data remains accessible and actionable throughout the lifecycle.

Speeding innovation and reducing talent dependency

Seekr’s no-code interface empowers organizations to go from concept to production in 30 minutes or less, bypassing the need for extensive engineering resources. This speed allows for rapid experimentation and deployment, helping firms achieve real-time business capabilities faster.

What this means for enterprises

Seekr eliminates the roadblocks to real-time business intelligence, helping organizations unlock unstructured data, align models with organizational policies, and address data gaps. Its horizontal approach ensures a seamless AI lifecycle that enables teams to innovate faster and reduce costs. Seekr equips businesses with scalable, trustworthy AI solutions, accelerating time to AI ROI.

¹ MIT CISR Research Briefing Volume XXIV, Number 8, August 2024. “What’s Next: Top Performers are Becoming Real-Time Businesses”, Weill, P, van den Berg, E, Birnbaum, J, de Planta, M.

²https://www.ft.com/content/9602467d-f5d7-40eb-af5a-f1fbf1ccfcd7