Here are five predictions from a firm that specializes in AI-centric data transformation and digitalization
What can the world expect of AI innovations next year? Here are five predictions from our experts to help readers in their strategizing:
- Rise of autonomous AI agents: In 2024, we saw organizations exploring generative AI (GenAI), but adoption has not been widespread other than for personal productivity. Next year, we predict that AI agents will be heavily invested in. These “virtual co-workers” will augment human workers to manage multi-step workflows.
- Greater trust and governance: Bad data leads to bad AI: organizations will be at risk if data is incomplete, scattered, or based on inaccurate sources for decision-making. The need for AI oversight and accountability is important as organizations place emphasis on data democratization, data stewardship, and AI data pipelines. Next year, organizations will need a strong data governance framework to manage their AI systems and ensure AI is transparent and used responsibly, while complying with AI regulations and policies.
- Democratization of data and AI upskilling: Legacy infrastructure and outdated tools, along with fragmented, siloed data, make data democratization across organizations difficult and challenging. This can hinder business users from harnessing trusted insights from their AI-powered solutions. Next year, we will see stronger arguments for developing talents and competency in AI and data science, along with building metadata-driven intelligence layers to support AI-driven development and rapid prototyping. Organizations will need to ensure trusted data is democratized and used responsibly.
- Calls for clearer AI returns on investment: Organizations experimenting with GenAI models will face more calls to quantify their returns on AI investments next year. Firms will need clear metrics aligned with their business objectives to measure the true value and impact of their GenAI plans and projects. Until they can demonstrate tangible outcomes, they may face hurdles in diversifying and scaling AI usage across the business.
- GenAI goes vertical: While there are multiple use cases among major industries, including financial services, healthcare, and retail, current exploration is often limited to reducing operational costs and improving productivity. Next year, by broadening the usage of AI to higher-level tasks such as optimizing supply chain management, combating fraud and risks, and improving customer experience, industry-specific AI models (called Vertical AI) will become more prevalent. This model involves utilizing enterprise data tailored to specific industries to deliver more meaningful insights and can provide self-service options to tackle workforce shortages and enhance work agility, thereby ensuring quicker returns on investment.
