Market intelligence professionals face a stark reality: despite heavy investments in artificial intelligence technologies, most organisations fail to achieve meaningful business outcomes. 

Our analysis of Coastal’s 2025 research report shows that while 67% of enterprises plan to maintain or increase AI spending, only 21% report proven outcomes from their AI initiatives.

The gap between AI investment and impact stems from poor foundational readiness, not tool limitations. The research identifies legacy data infrastructure as the main barrier to AI-powered market intelligence, with 31% of organisations specifically citing data integration challenges as their biggest AI obstacle.

Fewer than one in five companies have built a modern data foundation capable of supporting advanced intelligence at scale.

For market intelligence professionals who need actionable insights from AI investments, the shift from digital transformation to data and AI modernisation is a strategic necessity.

This modernisation requires three elements: unified multi-cloud architecture for data flow across platforms, streamlined processes for AI-powered automation, and strategic alignment linking intelligence initiatives to measurable business outcomes.

Some organisations already benefit from this approach with unified customer profiles, embedded analytics in workflow applications, and AI-powered automation that cuts overhead while delivering faster insights.

These successes show that market intelligence teams with modernised data foundations can make decisions faster, improve forecast accuracy, and spot opportunities before competitors.

This article examines the structural changes needed for market intelligence functions to use AI effectively, with practical guidance on data modernisation strategies that help intelligence professionals deliver valuable business insights.

Research Context

This analysis uses Coastal’s 2025 Future Readiness Survey, which assessed AI readiness in organisations using market intelligence platforms. The research gathered quantitative responses from over 120 senior business and technology leaders across various industries including business services, finance, healthcare, manufacturing, and media.

The study used both structured survey data and open-text responses to identify trends and insights into challenges facing intelligence professionals. With 38.9% of respondents at C-level and 54.5% at VP-level, the research provides a broad view of AI implementation challenges from both strategic and operational angles.

The report examines architectural barriers to intelligence scalability, process limitations that block automated insight generation, and the gap between pilot initiatives and company-wide implementation.

This data helps competitive intelligence analysts compare their organisation’s AI readiness against industry standards and set priorities for intelligence infrastructure modernisation.

From Data Collection to Intelligence Architecture

Traditional market intelligence has focused on collecting and organising data from different sources, often using batch processing with significant delays between data capture and insight delivery.

Coastal’s research shows this approach doesn’t work with modern AI-enabled intelligence, which needs real-time data access, unified data models, and cross-platform integration.

The research highlights a shift from Digital Transformation to Data & AI Modernisation. This means changing from “implementing process-focused applications” to “modernising data infrastructure and methods for how data is stored, transformed, and activated.” For competitive intelligence analysts, this requires moving from disconnected tools to an integrated system that enables continuous monitoring and real-time competitive assessment.

The data shows 67% of organisations plan to increase AI spending, yet only 21% report proven outcomes – a gap that affects intelligence teams’ ability to deliver useful insights. This gap exists mainly because of architectural limitations, with many organisations trying to implement advanced intelligence capabilities on old infrastructures that can’t support the speed, scale, or integration needed for AI-powered analysis.

Market intelligence managers need to push for fundamental data modernisation before implementing AI. Without fixing these structural problems, intelligence initiatives will fail to deliver practical business impact.

From Data Silos to Unified Intelligence Platforms

The most important finding for market intelligence professionals is the critical need for unified data architecture to enable scalable AI. The research identifies three architectural principles that directly affect intelligence effectiveness:

1. Multi-cloud connectivity: Market intelligence functions must connect platforms like Snowflake and Data Cloud to enable controlled, real-time data access across organisational boundaries.
2. Modular AI architectures: Intelligence teams need systems that allow AI models to access and analyse unified datasets across platforms, removing barriers between internal and external data.
3. Business-led data governance: Effective market intelligence requires widespread access to insights while maintaining consistent data definitions and security controls.

For strategic planning analysts, this architectural change offers an opportunity to centralise intelligence functions that have typically operated separately. By creating unified data platforms, organisations can build a “single source of truth” for market intelligence, removing contradictory insights and enabling more thorough competitive analysis.

The research shows organisations with modern data architecture are 2.7 times more likely to report positive ROI from their AI initiatives, yet 64% still lack a clear data modernisation plan. This presents both a challenge and an opportunity for market intelligence leaders.

Process Improvement for Intelligence Automation

Beyond architecture, the research highlights another often overlooked barrier to AI effectiveness: old processes not designed for automated intelligence generation.

According to the survey, 38% of organisations cite reducing manual work and improving efficiency as their main reason for AI investment, yet many try to implement AI within existing workflows rather than redesigning processes for automation.

The research identifies several process-related challenges that directly impact market intelligence effectiveness:

1. Dependencies and approvals: Many intelligence workflows contain unnecessary approval steps that block automated action, greatly reducing the speed of market monitoring.
2. Undocumented processes: Intelligence generation often relies on insider knowledge and manual interpretation, making it hard to standardise and automate analysis.
3. Human-centred workflow design: Many intelligence processes were built around human limitations rather than business needs, including redundant steps that become bottlenecks in an AI-enabled environment.

Competitive intelligence analysts need to standardise and streamline their information gathering and sharing processes. By documenting repeatable analysis methods and removing unnecessary complexity, intelligence teams can create the conditions for effective AI support.

The research suggests that process improvement is now essential for AI readiness. As the report notes, “the issue isn’t agent capability—it’s how work is structured.” For market intelligence professionals, this means rethinking how intelligence is produced, shared, and used across the organisation.

Key Statistics and Insights

1. 67% of organisations plan to maintain or increase AI spending, yet only 21% report proven outcomes—showing a major gap between investment and results in market intelligence projects.
2. Fewer than 1 in 5 organisations have built a modern data foundation that can support advanced intelligence applications at scale.
3. 31% of respondents identify data integration as their biggest AI barrier, with another 23% citing data readiness or quality issues as a top challenge.
4. Organisations with a clear data & AI roadmap are 2.7x more likely to report positive ROI from their intelligence initiatives, but 64% still lack a proper modernisation plan.
5. 38% of organisations say reducing manual work and improving efficiency is their main reason for AI investment, making process improvement the top priority for market intelligence teams.
6. The change from rigid, application-specific data models to flexible, unified, business-focused data models is a fundamental architectural shift needed for scalable AI-powered intelligence.
7. Old data architecture prevents 1 in 5 organisations from scaling their AI initiatives, directly affecting their ability to extract competitive insights from unstructured market data.

Technical Glossary

Data & AI Modernisation Era: The current technology phase featuring multi-cloud connectivity, real-time data access, and AI-powered automation that follows the Digital Transformation Era focused on application implementation.

Lakehouse Architecture: A data management system that combines data lakes (for unstructured data storage) and data warehouses (for structured analytics) to support both AI workloads and business intelligence in one environment.

AI Agents: Advanced AI systems that can make decisions and execute tasks within set boundaries, going beyond simple rule-based automation to adaptive intelligence that learns and improves processes.

Business-Focused Data Models: Standardised data models organised around business concepts rather than technical structures, ensuring consistent interpretation of information across different systems.

Zero-ETL: A new architectural approach allowing data to flow between platforms without traditional extract-transform-load pipelines, reducing delays and complexity in cross-platform data sharing.

API-Led Architecture: A connection method using application programming interfaces to link data, applications, and devices in a structured, reusable way that supports scalability and governance.

Data-as-a-Product: A method treating datasets as managed products with defined owners, quality standards, and service agreements rather than as byproducts of business processes.

Process Mining: The analysis technique of discovering, monitoring, and improving business processes by extracting information from event logs in information systems.

Modular AI: A flexible approach to artificial intelligence that allows organisations to combine specialised AI components into complete solutions rather than using single, all-purpose systems.

Decision Intelligence: A field that combines data science with decision theory, connecting intelligence insights directly to business decisions and measurable results.

Key Questions & Answers

What is the main barrier stopping organisations from getting ROI from their AI investments in market intelligence?

The main barrier is outdated data infrastructure that can’t integrate properly, with 31% of organisations naming data integration as their biggest AI challenge. Old systems prevent smooth data flow between systems, creating isolated information that blocks comprehensive market analysis.

How does the shift to Data & AI Modernisation affect market intelligence functions?

This shift changes how market data is collected, analysed, and used. Intelligence functions must move from batch reports to real-time insights, from separate analysis systems to unified intelligence platforms, and from fixed dashboards to AI-supported, self-service intelligence tools.

What architectural changes are needed to support advanced market intelligence?

Organisations need three key architectural changes:

(1) multi-cloud connectivity so data can flow across platforms;

(2) unified, business-focused data models for consistent interpretation; and

(3) AI-ready data governance that balances access with security.

Why should process improvement come before AI implementation for market intelligence?

Old intelligence processes have unnecessary approvals, dependencies, and manual steps that block AI systems. Process improvement removes these bottlenecks, standardises repeatable analysis workflows, and creates the right conditions for automated intelligence generation.

How can market intelligence teams show ROI from their AI investments?

Teams should focus on four approaches:

(1) identify common, expensive intelligence activities with measurable costs;

(2) define success in business terms that match stakeholder priorities;

(3) connect AI capabilities to specific use cases with measurable outcomes; and

(4) design projects with built-in measurement to track progress.

What skills will market intelligence professionals need to develop for the Data & AI Modernisation Era?

Intelligence professionals need to move beyond data collection and analysis to develop skills in data architecture, data modelling, process improvement, and AI governance. The most valuable professionals will combine subject expertise with technical knowledge and business understanding.

How should market intelligence leaders handle data governance in an AI-enabled environment?

Leaders should move from IT-driven governance focused mainly on security and compliance to business-friendly governance that prioritises accessibility, consistent definitions, and automated policy enforcement. This builds the foundation for trusted, self-service intelligence that decision-makers can access.