Most organizations today have no shortage of data. ERP, CRM, and operational systems generate insights at an unprecedented pace. Yet many leaders still struggle with the same question:
Why doesn’t all this data translate into better decisions?
The issue is rarely the volume of data. It is whether that data is trustworthy enough to act on, and whether teams actually adopt the insight it produces. Too many organizations invest in analytics and AI as isolated initiatives, rather than building decision platforms that teams trust and use.
Why analytics and AI adoption stall
Despite years of investment in data platforms, dashboards, and advanced models, enterprise adoption remains inconsistent.
McKinsey’s State of AI research shows that while nearly all organizations now use AI in at least one area, most remain stuck in pilot mode, with limited enterprise level impact. Value tends to appear in individual use cases but rarely scales across functions.
Alithya’s analytics teams see similar patterns across clients:
- Dashboards that explain the past but don’t influence decisions
- AI models that teams don’t trust or understand
- Siloed analytics tied to departments, not business outcomes
- Low adoption outside of specialist or analytics teams
These challenges point to a common problem: analytics initiatives are often technology led, not decision led.
Moving from analytics projects to analytics platforms
High performing organizations take a fundamentally different approach.
Rather than asking “What data can we analyze?”, they ask:
- What decisions matter most?
- Who needs insight, and when?
- What action should follow this insight?
This shift, from reporting to decision enablement, is what separates analytics projects from analytics platforms.
We have identified four foundations that are essential for sustainable analytics transformation:
- Aligned data strategy tied to business priorities
- Scalable architecture that adapts as needs evolve
- Trusted, governed data with shared metric definitions, so every user and every AI agent computes the same answer
- Self-service analytics that empower end users, not just data teams
Where AI fits, and where it doesn’t
AI is reshaping the analytics landscape, but it’s not a shortcut to adoption.
McKinsey research shows that organizations scaling AI successfully focus first on workflow integration and change, not just the AI tools.
In practice, AI enhances analytics when it:
- Identifies patterns humans might miss
- Surfaces anomalies and risks earlier
- Supports scenario modeling and forecasting
- Automates repetitive analysis tasks
AI adoption falters when it’s introduced without clarity around ownership, governance, or decision rights.
Gartner has predicted that a significant share of generative AI projects will be abandoned after the pilot stage when value fails to appear, consistent with the view that value depends more on organizational readiness than on AI spending alone.
AI is only as ready as the data beneath it
The readiness question most organizations skip is not whether their people are ready for AI. It is whether their data is. A dashboard can tolerate data that is roughly right. An AI agent is less forgiving, because it composes its own queries and narrates the answer in plain language, with no analyst in the loop to catch a number that is only roughly right.
Data that is ready for AI looks different from data that was merely good enough for reporting. It carries deep history and transaction-level detail, not just the pre-aggregated summaries a dashboard settles for. It is governed so that access and privacy rules follow the data into every AI answer, rather than being lost the moment an agent queries it. And critically, it is defined. Every metric that matters has one agreed calculation that lives in the data, not in the head of whoever wrote the last query.
That last point is where a real and growing risk is emerging. As organizations connect AI agents directly to their source systems, protocols like MCP make it trivial to wire an agent straight to a database or application. This is powerful, and quietly dangerous when no governing definition sits behind the data. Handed raw tables, the agent improvises the calculation at query time. Ask it for revenue one way and you get gross sales. Ask it another way and you get net of returns and intercompany eliminations. The same ambiguity applies to margin: two leaders can ask the same question in the same meeting and leave with two different numbers, each delivered with equal confidence.
The answer is not to wall off AI access. It is to point the agent at a governed dataset rather than at raw sources. A lakehouse that serves as the single source of truth encodes the agreed definition of revenue, margin, and every other metric once, so the result is consistent no matter who asks or how they phrase it. The semantic model becomes the contract between the AI and the enterprise's numbers. This is also why analytics maturity tends to climb a ladder: from raw data, to curated and conformed data, to a modeled semantic layer, to data that is genuinely ready for AI to reason against.
Practical AI-enabled analytics use cases
Organizations successfully scaling analytics and AI often start with focused, high trust use cases:
- Predictive forecasting layered on ERP and financial data
- Operational analytics that highlight risk or inefficiency
- Adoption analytics that show how systems are actually used
- Decision support dashboards embedded into daily workflows
Value emerges fastest when insight is delivered where work happens, not in standalone tools.
Adoption is still the hard part
Even with AI acceleration, adoption remains the limiting factor.
According to McKinsey, fewer than 40% of organizations report enterprise level financial impact from AI, despite widespread experimentation. The gap is rarely about the AI model itself. It is about trust and behavior, and trust rests on the data foundation as much as on change management.
Successful analytics programs:
- Redesign workflows alongside analytics
- Establish trust through data quality and transparency
- Train users to interpret, not just view, insight
- Govern AI outputs without slowing innovation
Analytics that don’t influence behavior quickly lose relevance.
Analytics integration is the force multiplier
Analytics deliver the most value when integrated across platforms.
When analytics connect ERP, CRM, operations, and AI models:
- Insights gain context
- Decisions become faster and more consistent
- AI outputs improve as data quality improves
This is why analytics adoption often accelerates after core enterprise platforms are live, and why analytics are a natural expansion path following ERP, CRM, or EPM implementations.
What high growth organizations do differently
Organizations that scale analytics and AI successfully:
- Focus on decision outcomes, not dashboards
- Introduce AI incrementally with clear guardrails
- Measure adoption as rigorously as performance
- Treat analytics as a living capability, not a project
They recognize that analytics transformation is as much about people and process as it is about data and models.
Analytics and AI don’t create value on their own. Decisions do.
When organizations move beyond analytics projects to decision platforms, grounded in trust, integration, and adoption, data becomes a strategic asset rather than an untapped resource.
In an AI accelerated world, the organizations that win are not the ones with the most data or the most models, but the ones that make analytics usable at scale.
This article was written in collaboration with Reddy Beerem, Vice President, Business Development.