, I attended the Gartner Data & Analytics (D&A) Summit 2026 in Orlando, Florida. Across three days of hearing from data & analytics leaders, one idea stood out clearly: analytics is no longer just about asking questions and comprehending the past. It is becoming much more about proactively shaping decisions in real time.
We are witnessing a fundamental shift. As you may be experiencing in your everyday lives, we are getting access to an increasing number of AI tools and agents. A lot of us have been experimenting with AI—using it as a coding assistant, productivity booster, brainstorming partner, and more. Like many of us, I’ve started noticing just how much of my day-to-day work AI has quietly absorbed, at my job and at home.
We are slowly starting to see a shift at an organizational level. We are expected to move from dashboards and reports toward intelligent systems that not only generate insights but recommend and automate actions.
Whether we like it or not, we will be hearing and working with AI for the next few years, at least. But beneath all the excitement around AI, one truth remains: the future of data and analytics is not just AI-first—it is human-centered.
In this blog post, I want to highlight some of the key trends I heard about, at the conference, and what I envision working on as an analytics professional.
#1 A Shift From Reporting to Decision Systems
For years, analytics teams have focused on answering questions.
We are asked: What happened? Why did it happen?
However, now, the expectation is different.
Instead of expecting analysts to put together a story with actionable insights (through dashboards or slides), organizations are pivoting to create systems that can guide decisions, rather than humans leading the charge alone. Dashboards alone are no longer enough. They need interpretation, context, and action.
Sometime back, I wrote about decision intelligence, saying:
“While AI is focused on providing the technology to mimic human intelligence, Decision Intelligence will apply that technology to improve how decisions are made.”
And in hearing where the industry is headed, I believe that Decision Intelligence is the next evolution.
Decision Intelligence is about systems that combine data, AI, and business logic, embedded into workflows, to present insights and make business recommendations that are actionable, not just informative.
This shift redefines the role of analysts and data & analytics teams.
We are expected to be decision enablers rather than mere insight providers.
What can we do as analytics professionals today?
- Start thinking beyond dashboards to what decisions should your work influence?
- Design outputs that recommend actions, not just insights
#2 AI is Ready But Our Data & Context Isn’t
There’s no denying the scale of AI investment. AI spend is expected to reach trillions in the coming years. In that world of tomorrow, it is not the organizations experimenting the most that will win, but the ones operationalizing AI effectively.
The biggest barrier to adapting to AI today is not the technology itself. It’s the data readiness and business context.
AI does not fix bad data. It amplifies it.
If the underlying data for the AI agent to consume and act upon is inconsistent, poorly structured, or difficult to work with, AI will only amplify issues. In such cases, outputs are less trustworthy than valuable while the organization pays BIG money on AI tokens.
That said, AI-ready data alone is not enough. Context matters just as much.
Without clearly defined metrics, consistent business logic, and a common understanding across teams, even the most advanced AI systems cannot produce reliable or actionable insights.
What can we do as analytics professionals today?
- Invest in data quality and standardization before scaling for AI
- Focus on defining business context, not just building models
#3 The Rise of Agentic Analytics
Today, many organizations are still in that experimentation phase (or what I like to call “the copilot phase”), where humans are still in the loop and working alongside AI tools to accelerate insights.
And this is just the beginning.
I see the next evolution as agentic analytics. We will no longer just be in the experimentation phase. We are ready to enter the execution phase and the shift is already visible in how analytics workflows are evolving:
- AI agents orchestrate workflows
- Systems proactively surface insights
- Automation of repetitive analytical tasks
- Insights generated before stakeholders ask
- Data pipelines managed more autonomously
All that to say, I do not think this removes humans from the loop completely. But, it definitely changes where we add value.
What can we do as analytics professionals today?
- Learn how to work with AI agents, not just use AI tools
- Focus on higher-value thinking while automating repetitive tasks
#4 Analytics Is Becoming Conversational
I love anything human-centered – it is one of my passions to see things from a human perspective and one of the most exciting shifts for me is how people will interact with data.
We are moving from complex dashboards to natural language queries and narrative-driven insights. Analytics is becoming more conversational, with GenAI enabling storytelling alongside the visuals you create in dashboards or Excel.
And that is a huge opportunity for human-centered analytics!
(you can read more about why human-centered analytics matters more than ever HERE)
In other words, analytics is becoming more reflective with how humans naturally think and make decisions.
What can we do as analytics professionals today?
- Build skills in data storytelling, not just data visualization
- Focus on explaining insights clearly, not just presenting them
#5 The Real Foundations are Data + Semantics + Trust
While AI gets the spotlight, the real transformation has to happen underneath—at the architecture level.
The modern analytics stack will look like:
- Data Layer – clean, reliable, governed data
- Semantic Layer – shared business definitions and context
- AI/Agents Layer – models that analyze and automate
- Decision Systems Layer – where insights turn into action
Without these four critical layers in a good co-ordination, even the most advanced AI systems will produce inconsistent or untrustworthy outcomes.
What can we do as analytics professionals today?
- Advocate to use the same definitions and meaning of data across all teams
- Consider data governance and business definitions as strategic priorities, not something optional
The Next Decade: What’s Coming
We are moving from a world of dashboards to a world of decisions.
Analytics is evolving from AI copilots to autonomous, agent-driven decision systems that are powered by context, semantics, and real-world data.
This is not just a tech shift, but a fundamental change in how organizations operate.
And the organizations that succeed will be the ones that don’t just adopt AI, but the ones that thoughtfully integrate it into how humans think, decide, and act.
So, Where Do Humans Fit In Then?
Before the conference, my key question was: if artificial intelligence begins to normalize human intelligence, where do we, as humans, matter?
The answer I found: humans are more important than ever.
As AI takes on data preparation, querying, and even insight generation, the role of humans shifts toward what truly differentiates us:
- Framing the right problems
- Interpreting context and nuance
- Making ethical and strategic decisions
- Applying critical thinking to solve complex challenges
This is where human-centered analytics becomes quintessential.
Because ultimately, the goal of analytics is not just better data—it’s better decisions for people.
The future of data and analytics is not about choosing between humans and AI. It’s about designing trustworthy systems where AI is intelligent and aligned—and humans remain at the center of decision-making.
Final Thought
We are moving from a world of dashboards to a world of decisions.
And the individuals and organizations that succeed will be the ones who don’t just adopt AI, but rethink how decisions are made.
The question is no longer “How do we analyze data better?”
It’s “How do we design systems where humans and AI make better decisions together?”
………
That’s it from my end on this blog post. Thank you for reading! I hope you found it an interesting read.
Rashi is a data wiz from Chicago who loves to analyze data and create data stories to communicate insights. She’s a full-time senior healthcare analytics consultant and likes to write blogs about data on weekends with a cup of coffee.