Problem Statement - Data readiness is a myth, and achieving perfect data for AI is an unattainable pipe dream [1][2][3] - Fortune 500 companies lose an average of $250 million due to poor data quality [7] - Traditional semantic layers and knowledge graphs are insufficient for capturing the nuances of business language and tribal knowledge [8][9][10][11][12][13][14] Solution: Agentic Semantic Layer (PromQL) - PromQL is presented as a "day zero smart analyst" AI system that learns and improves over time through course correction and steering [17][18][19][20] - It uses a domain-specific language (DSL) for data retrieval, computation, aggregation, and semantics, decoupling LLM plan generation from execution [21][22] - The system allows for editing the AI's "brain" to correct its understanding and guide its learning [28] - It incorporates a prompt learning layer to improve the semantic graph and create a company-specific business language [31] - The semantic layer is version controlled, allowing for fallback to previous builds [33] Key Features and Benefits - Correctable, explainable, and steerable AI that improves with use [19] - Ability to handle messy data and understand business context [24][25] - Reduces months of work into immediate start, enabling faster AI deployments [37] - Self-improving and achieves 100% accuracy on complex tasks [37] Demonstrated Capabilities - The system can understand what revenue means and perform calculations [23] - It can identify and correct errors in data, such as incorrect status values [24] - It can integrate data from multiple databases and SAS applications [25][27] - It can summarize support tickets and extract sentiment [26][29] - It can learn the meaning of custom terms and relationships between tables [35][36] Customer Validation - A Fortune 500 food chain company and a high-growth fintech company achieved 100% accurate AI using PromQL [38]

Core Insights - The Capgemini Research Institute report indicates that two-thirds of public sector organizations are currently exploring or using generative AI initiatives to enhance public services, with 90% planning to adopt agentic AI within the next 2-3 years [2][3] - Despite the enthusiasm for AI, public sector organizations face significant challenges related to data readiness, trust, compliance, and data management, which hinder their ability to fully leverage AI technologies [2][5][6] AI Adoption and Expectations - Public sector organizations have high expectations for AI, with 39% planning to evaluate agentic AI feasibility, 45% intending to explore pilot programs, and 6% aiming to scale existing initiatives within the next 2-3 years [3] - The report highlights that 64% of organizations have progressed to pilot or scaled deployments, with higher adoption rates in defense (82%), healthcare (75%), and security (70%) sectors [3] Data Readiness Challenges - A significant barrier to AI adoption is data security concerns (79%) and limited trust in AI outputs (74%), with only 36% of organizations in the EU prepared to comply with the EU AI Act [5] - Only 12% of public sector organizations consider themselves very mature in activating data, and just 21% have the necessary data to train and fine-tune AI models [6][8] Data Sharing and Governance - Data sharing is essential for AI adoption, yet 65% of organizations are still in the planning or pilot stages of data sharing initiatives, complicating the deployment of AI [7] - The rise of Chief Data Officers (CDOs) and Chief AI Officers (CAIOs) reflects the growing importance of data governance, with 64% of organizations having a CDO and 27% appointing a CAIO [9] Research Methodology - The report is based on a survey conducted by the Capgemini Research Institute involving 350 public sector organizations across various segments and levels of government, providing a comprehensive view of AI adoption in the public sector [10]