A comprehensive analysis based on Economist Impact research involving 715 technical executives and 385 data and AI technologists across the enterprise landscape.
This article is a summary of an Economist Impact report, commissioned by Databricks, and combines a global survey of 715 technical executives and 385 data and artificial intelligence (AI) technologists who work across the fields of data engineering, data science and enterprise architecture.
Access the complete Economist Impact research
AI adoption is no longer experimental—it's mainstream. The survey shows 85% of organizations already use generative AI (GenAI) in at least one business function, and 97% of enterprises over $10B in revenue are engaged. Yet, only one in five leaders believe their current investments are sufficient, signaling underinvestment risks.
Organizations already using GenAI in business functions
Large enterprises ($10B+ revenue) are AI-engaged
AI's immediate benefits show up in productivity and efficiency—coding assistants, workflow automation, customer support, marketing content. Flo Health, for example, boosted data engineering efficiency by 24%, and Repsol saw productivity improvements of up to 30% among coders.
But leaders see long-term value in business model innovation, market positioning, and ESG alignment. Revenue growth hasn't been the main driver yet—but it will become decisive as adoption matures.
Boost in data engineering efficiency
Productivity improvement among coders
Executives recommend methodical, intentional pilots. Providence, for example, created governance and technical environments for controlled experiments. TD Bank restricted its GenAI chatbot to internal staff, protecting customers from unpredictable outputs.
Only 22% of organizations say their architecture can fully support AI workloads today. Data silos, latency, and fragmented systems remain the biggest barriers. Data engineers report spending much of their time just fixing data pipelines.
Organizations with AI-ready architecture
Architects cite real-time processing as top gap
Data is the differentiator. Two-thirds of organizations see greatest potential in integrating GenAI with proprietary data, yet almost half of data scientists still use LLMs without retrieval-augmented generation (RAG).
Organizations see potential in proprietary data + GenAI
Data scientists use LLMs without RAG
Enterprise data is unstructured
The report highlights a gap between $1T+ projected AI spend and near-term revenues. Executives warn against hype but agree that underinvesting is riskier than overinvesting.
Projected AI spend vs. near-term revenue gap
Partner with KData's experts to implement strategic AI initiatives that drive measurable ROI and competitive advantage.
Executives stress that AI is an augmentation tool, not just automation. Leaders are preparing for a future where natural language is the default interface to data. Nearly 60% of enterprise architects expect non-technical staff to interact with data solely via natural language within three years.
Enterprise architects expect natural language as default data interface within 3 years
Governance is often seen as a brake, but the report reframes it as an accelerator. Half of data engineers say governance takes more time than anything else, yet weak governance risks compliance breaches and reputational harm.
50% of data engineers spend more time on governance than core work
Lead in scaling GenAI
Expanding production
Lag due to regulatory hurdles
Untapped data value
Aggressive scaling with open-source models
Prefers custom proprietary models
Experimenting with riskier, innovative use cases
"There is no such thing as a technology strategy. There's only a business strategy that technology supports."
— Executive Survey Participant
The central message: AI leadership is business leadership. There is no "AI strategy" separate from business strategy.
Enterprise AI is moving from hype to hard value. The winners will be those who master these strategic imperatives:
Handle AI scale with unified data architectures
Proprietary and unstructured data as unique differentiator
Productivity gains with long-term revenue growth
Risk-graded pilots that scale securely
AI access while enforcing strong governance
AI as core business transformation element
In short, AI success requires as much organizational and cultural transformation as it does technical innovation. Leaders must align investments, infrastructure, governance, and workforce strategies to turn AI into a true competitive advantage.
Follow us on LinkedIn to get the latest insights on data engineering, Databricks, Snowflake, AI strategies, and cloud best practices. Join our professional community of data experts.
Data Engineering & AI Experts
Join thousands of data professionals