Franck Greverie - According to our recent report “Data Powered Enterprises,” while 60% of organizations have initiated pilots or proofs of concept for Gen AI, 75% of data executives cite large-scale deployment as a major challenge. One key factor is that only 42% of organizations possess the requisite data foundations to use these Gen AI models effectively.
Three roadblocks hinder progress. First, the lack of quality enterprise data necessary for successful customization of Gen AI, coupled with weak data foundations and governance, plus a weak data management platform. Second, organizations often fail to address privacy and security imperatives early on, neglecting to design guardrails to ensure compliance with regulations. Third, the transformation of the enterprise operating model is often lacking, particularly the processes needed to continuously update AI solutions, and implement organizational transformation, change management, and upskilling. To realize tangible business benefits from AI initiatives, maintaining data quality from the outset is essential. Ultimately, if your data isn’t ready for AI, your business isn’t ready for AI.
Kevin Campbell - While data quality is crucial for any business process, AI amplifies the repercussions of data quality issues since AI models often function as black boxes. If they are fed incorrect data, the outcome can rapidly erode trust in the technology. A single faulty recommendation can lead users to blame the AI rather than acknowledge underlying data flaws.
For enterprises aiming to build custom Gen AI assistants, selecting a robust AI model and high-quality enterprise data is key. This is also the case when building AI agents-or “Agentic AI,” a type of artificial intelligence that perceives, reasons, and acts to achieve goals with limited human supervision. The return on investment from AI implementations is substantial when custom assistants or agents significantly enhance employee knowledge and performance or autonomously execute enterprise tasks. Additionally, it is imperative for organizations to establish a strong data management and governance structure to allow robust updates and maintain the efficacy of AI solutions over time.
Many organizations layer AI onto existing systems plagued by data errors. Teams must make ongoing efforts to patch errors to keep operations running smoothly. Over time, such ad-hoc fixes drain resources, hinder operations, and create inefficiencies that businesses struggle to escape.
K.C. - As companies increasingly pursue AI initiatives, the emphasis on data quality and robust management practices is more critical than ever. Syniti’s approach complements this need, equipping organizations with the data they need to implement Gen AI models and AI agents, when the time comes. The comprehensive scope of Syniti products integrates data quality with data migration, resulting in high levels of data accuracy-an essential element for clients expecting tangible business benefits from AI. What sets Syniti apart is its support not only for technical activities but also for crucial processes that business users must engage in when using AI, including preparation, cleansing, and mapping of data.
F.G. - AI innovation is happening at lightning speed. In the coming five years, AI will become totally pervasive across the digital landscape. Big advances will be made in custom Gen AI and AI agents, which will grow more autonomous, goal-oriented, adaptive, context-aware, language-aware, proactive, and reactive. In the coming years, we will start to see a vast impact in use cases including hyper-automation of IT and business operations; customer services; software and product engineering; robotic humanoids and “cobots;” 2D and 3D avatars; and AI wearables.
(1) Insights & Data, Business Services, Cloud Infrastructure Services, and Digital Customer Experience.
