Over the past three decades, no innovation has held the potential to transform every aspect of business quite like generative AI. Today, AI stands at the forefront of enterprise reinvention, and an organization’s readiness to harness its data has become a critical determinant of AI-driven success.
estimated yearly cost of cybercrime in 2025
of organizations plan to increase spending in technology and are prioritizing investments in data and AI
of executives said that “good quality data” is the most valuable ingredient to enhance their generative AI capabilities
more revenue growth is achieved by data-driven companies than by their peers
AI goes autonomous & agentic: The rise of “agentic AI” — systems that don’t just follow instructions but can plan, decide, and act independently — is a major shift. These agents are helping automate complex workflows in data analysis, customer service, code writing and business insights.
Data-centric & Responsible AI: Instead of obsessing just over model architecture, more focus is on high-quality, well-managed data. Reliable, clean data — plus ethical, transparent AI (explainability, bias mitigation) — is becoming a priority.
Real-time, edge & multimodal analytics: Organizations increasingly demand real-time insights. AI and analytics are migrating to the “edge” — devices, sensors, machines — to deliver instant decisions. Also, AI is getting better at handling multimodal data (text, images, audio, video), enabling richer, more context-aware applications.
Augmented analytics & democratization of data: With AI-powered tools automating data cleaning, visualization, and insight generation, even non-technical users can now ask complex questions — and get answers. Data analysis is no longer only for experts.
In short: 2025’s data + AI wave is about smarter, faster, more ethical, and more accessible intelligence — shifting from niche data-science teams to everyday use across businesses.
Agile are increasingly leveraging AI and predictive analytics to support sprint planning, prioritization, and risk identification. Rather than relying solely on gut-feel, data-driven tools help analyze past sprint data to forecast capacity and suggest improvements.
There’s also a move from project-based Agile to product-centric delivery, meaning business analysts are working more closely with long-lived product teams, aligning analysis with continuous value delivery and business outcomes.
Hyper-agile or hybrid Agile frameworks are gaining popularity — teams are combining Scrum, Kanban, Lean, and even waterfall as needed, tailoring their way of working rather than strictly following one methodology.
A return to core Agile values and simplicity is happening: organizations emphasize minimizing ceremony, reducing waste, and focusing on delivering customer value over rigid processes.
Finally, business agility is expanding beyond IT: Agile mindsets and practices are now adopted in functions like HR, finance, marketing, and operations, increasing the role of the BA to act as a strategic connector.