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Why Executives Are Asking for Analytics Copilots

January 7, 2026

Why Executives Are Asking for Analytics Copilots

By Andrey Avtomonov, CTO at Kaelio | 2x founder in AI + Data | ex-CERN, ex-Dataiku · Jan 7th, 2026

Executives are demanding analytics copilots because traditional BI workflows create costly delays, with 44% of data teams failing to deliver value while 75% of workers already use AI tools independently. Analytics copilots like Kaelio solve this by translating natural language into governed SQL queries, providing instant answers with full transparency while respecting existing security and governance frameworks.

TLDR

Current analytics workflows force executives to wait days or weeks for answers, with less than half of data teams effectively delivering business value
66% of software builders now work under AI mandates, yet most organizations haven't scaled AI across the enterprise
Analytics copilots translate plain English questions into governed SQL, eliminating bottlenecks between business teams and data insights
Kaelio integrates directly with existing data stacks, preserving governance while detecting redundant or inconsistent metrics across the organization
Organizations using AI-enabled KPIs are 5x more likely to align incentives with objectives compared to those using legacy systems

Executives are no longer content to wait days or weeks for answers that should take seconds. Across industries, leadership teams are accelerating investment in a new category of tool: the analytics copilot. These AI powered systems promise to close the gap between raw data and trusted insight, without adding headcount or risk.

The urgency is real. According to a Gartner survey, just 44% of data and analytics leaders say their teams effectively provide value to their organizations. Meanwhile, a McKinsey report found that nearly two-thirds of organizations have not yet begun scaling AI across the enterprise. The result is a widening chasm between what business teams need and what data teams can deliver.

This post explains why current analytics processes are failing, what executives actually want from an analytics copilot, and how Kaelio addresses these challenges head on.

Why Executives Suddenly Care About an Analytics Copilot

What changed? In short, AI became accessible and expectations rose accordingly.

A Retool study found that 75% of global knowledge workers were already using AI, with most reporting they used their own tools because their companies did not provide approved alternatives. At the same time, 66% of software builders now work under AI mandates, according to Retool's 2025 Builder Report. Employees are already solving problems with AI. The question is whether organizations will catch up.

Yet the value gap persists. Gartner's survey revealed that less than half of D&A teams deliver measurable business value. Executives are asking: if AI is so powerful, why are we still waiting on dashboards?

An analytics copilot answers that question by translating natural language questions into governed, explainable answers, directly from your data stack. Unlike traditional BI tools, which surface predefined charts, a copilot converses with users. It interprets intent, generates SQL, and returns results with full lineage and transparency.

Kaelio is built for exactly this use case. It sits on top of your existing semantic layer, detects metric drift, and returns fully explained SQL plus lineage, so non-technical teams get trusted answers while data teams keep governance intact.

What Are the Hidden Costs of Yesterday's Analytics Workflows?

Even simple questions often turn into long Slack threads, then tickets, then small analytics projects. Data teams get overwhelmed, business teams wait, and definitions slowly drift across dashboards, spreadsheets, and conversations.

Gartner predicts that by 2025, 95% of decisions that currently use data will be at least partially automated. But most organizations are not ready. The Gartner survey found that the top six roadblocks to D&A success are all human related challenges: culture, skills, and alignment, not technology.

McKinsey research highlights similar friction. G&A functions like HR, IT, and finance need rapid insights into ever larger and more complex data sets. Yet these functions face budget constraints and struggle to show a direct correlation to top or bottom line growth.

The costs add up:

Delayed decisions as business teams wait for analyst capacity
Inconsistent metrics as definitions drift across tools and teams
Talent drain as skilled analysts spend time on repetitive requests instead of high value work

These inefficiencies are not just frustrating. They are expensive.

What Do Executives Really Want in an Analytics Copilot?

Executives are not asking for more dashboards. They want answers, fast, trusted, and actionable.

BCG research found that AI can reveal novel layers of insight by exploring and connecting data in ways beyond the reach of even the savviest managers. Organizations using AI enabled KPIs are five times more likely to effectively align incentive structures with objectives compared to those relying on legacy KPIs.

Gartner predicts that by 2025, chief data officers who establish value stream based collaboration will significantly outperform their peers in driving cross-functional collaboration and value creation.

Gainsight's perspective is direct: "Real-time insights are the difference between being reactive and proactive." As the Gainsight blog puts it, real-time insights give you the power to intervene at the moment it matters most.

A BCG survey found that two-thirds of companies are exploring the use of AI agents, advanced systems that can act on their own, suggesting that 2025 could mark a turning point for their adoption.

What does this mean in practice? Executives want copilots that:

Answer questions in plain English, without requiring SQL or BI expertise
Show how numbers were calculated and where they came from
Respect existing governance, security, and compliance rules
Improve over time as teams use them

Key takeaway: The demand is not for more tools. It is for trusted, governed, instant answers.

Which Analytics Copilots Are on the Market—and Where Do They Fall Short?

The market for analytics copilots is growing fast. Microsoft, Databricks, and others have entered the space. But not all copilots are created equal.

Microsoft Power BI Copilot has an overall rating of 4.4 based on over 3,000 reviews on Gartner Peer Insights, with 84% willing to recommend it. Databricks Genie uses Chain-of-Thought reasoning to break each question into clear steps for precise query generation.

But both approaches have limitations.

A Coalesce analysis notes that semantic layers are exploding in popularity in 2025, but so is confusion. Vendors offer different interpretations and solutions, making it hard to compare.

McKinsey research found that just 11 percent of companies have adopted generative AI at scale, underscoring the difficulty of moving from pilot to production.

Microsoft Power BI Copilot

Power BI Copilot requires careful model preparation. As Microsoft notes, trial SKUs aren't supported. Only paid SKUs are supported, and administrators must enable Copilot in Microsoft Fabric before teams can use it.

The setup overhead is real. If your semantic model is not pristine, outputs can be low quality and inaccurate.

Databricks Genie

Genie spaces are based on a subset of your company data. This means users may not get answers from the full breadth of their data estate. Additionally, like other large language models, Genie can exhibit non-deterministic behaviors, which can be a concern for regulated environments.

Both tools are powerful, but neither solves the governance and consistency problems that plague most analytics teams. That is where Kaelio comes in.

How Kaelio Raises the Bar for Enterprise Analytics Copilots

Kaelio is designed to fit into existing enterprise environments rather than replace them. It connects directly to your data warehouse, transformation tools, semantic layers, governance systems, and BI platforms.

When a user asks a question in natural language, the platform:

Interprets the question using existing models, metrics, and business definitions
Generates governed SQL that respects permissions, row level security, and masking
Returns an answer along with an explanation of how it was computed
Shows lineage, sources, and assumptions behind the result

As Forrester research puts it, "Data governance has never been about the data. It's about your business." Kaelio aligns with this philosophy. It does not own or redefine metrics on its own. Instead, it relies on your organization's existing semantic and modeling tools as the source of truth.

What it adds is a feedback loop. As users ask questions, the platform captures where definitions are unclear, where metrics are duplicated, and where business logic is being interpreted inconsistently. According to Kaelio's documentation, the platform finds redundant, deprecated, or inconsistent metrics and surfaces where definitions have drifted.

The dbt Semantic Layer, which Kaelio integrates with, eliminates duplicate coding by allowing data teams to define metrics on top of existing models and automatically handling data joins. If a metric definition changes in dbt, it is refreshed everywhere it is invoked and creates consistency across all applications.

A Coalesce article defines the semantic layer as "a business-friendly abstraction between your warehouse or lake and your BI/AI tools. It maps raw tables and columns into named entities, metrics, relationships, and policies so people and machines can query data using consistent business terms instead of technical schemas."

IDC research found that 30 percent of AI services buyers achieved 30% or greater improvement in measurable KPIs from their AI services engagement. The key is not just adopting AI, but deploying it in a way that achieves business outcomes.

Key takeaway: Kaelio does not replace your BI tools. It makes them more accessible, consistent, and governed.

How Do You Roll Out a Governance-Ready Copilot Program?

Deploying an analytics copilot is not just a technology project. It requires careful planning across security, compliance, and change management.

Here is a practical checklist for data and business leaders:

Assess your semantic layer maturity.
- Are metric definitions centralized and versioned?
- Do you have clear ownership of key metrics?
Enable security and compliance controls.
- Ensure your copilot respects row level security and data masking policies.
- Row Zero, for example, applies industry best practices like threat modeling, pen testing, and Defense in Depth.
Integrate with existing governance tools.
- AWS HealthLake automatically transforms FHIR data into analytics-ready formats, enabling immediate SQL-on-FHIR data access without complex data pipelines.
- For healthcare organizations, HIPAA compliance is non-negotiable. Nightfall's agentless integration simplifies security and HIPAA compliance across industry-leading SaaS applications.
Start with high-impact, low-risk use cases.
- OpenAI's enterprise deployment guide notes that in just two years, 39% of U.S. adults have already used AI. The internet reached only 20% adoption in the same time frame.
- Focus on use cases where the business value is clear and the risk is manageable.
Invest in change management.
- McKinsey research found that only 1% of survey respondents believed their AI investments had reached full maturity.
- Encourage adoption with hackathons, use case workshops, and peer-led learning sessions.
Measure and iterate.
- Track adoption, accuracy, and time-to-insight.
- Use feedback loops to improve metric definitions and documentation over time.

Key Takeaways for Data & Business Leaders

The demand for analytics copilots is not a passing trend. It reflects a fundamental shift in how organizations expect to interact with data.

Traditional BI tools and manual analytics workflows cannot keep pace with business demands.
Executives want instant, trusted, governed answers, not more dashboards.
Most analytics copilots on the market require significant model cleanup and do not solve governance problems.
Kaelio is purpose-built for enterprise environments, integrating with your existing stack and improving governance over time.

As Forrester research advises, organizations should "Break past the traditional frameworks and approaches to finally get sustained results from your data governance."

Kaelio empowers serious data teams to reduce their backlogs and better serve business teams. If you are ready to move from analytics bottlenecks to instant, trustworthy answers, Kaelio is the analytics copilot built for the job.

About the Author

Former AI CTO with 15+ years of experience in data engineering and analytics.

Frequently Asked Questions

What is an analytics copilot?

An analytics copilot is an AI-powered tool that translates natural language questions into governed, explainable answers directly from your data stack, providing instant insights without requiring technical expertise.

Why are current analytics processes failing?

Current analytics processes often involve lengthy communication threads and small projects, leading to delays and inconsistent metrics. These inefficiencies are costly and hinder timely decision-making.

How does Kaelio improve analytics processes?

Kaelio enhances analytics by integrating with existing data stacks, interpreting questions using existing models, and providing governed SQL answers with full transparency and lineage, ensuring consistency and trust.

What do executives want from an analytics copilot?

Executives seek analytics copilots that provide fast, trusted, and actionable answers in plain English, respect governance and compliance rules, and improve over time with usage.

How does Kaelio differ from other analytics copilots?

Kaelio stands out by deeply integrating with existing enterprise data environments, focusing on governance and consistency, and providing a feedback loop to improve metric definitions and documentation over time.