Is Your Data AI-Ready? 7 Critical Factors to Address Before Scaling AI In Your Organization

Introduction: AI Amplifies Data Problems

Every business leader we speak with has AI on the roadmap. Many organizations are already piloting use cases, investing in AI technologies, and experimenting with AI-driven automation.

The ambition is high. The spend is real. The pressure to show results is everywhere.

So why aren’t more AI initiatives delivering the value leaders expect (yet)?

In some cases, the issue is the tools. But again and again, the biggest constraint is simpler: the underlying data.

AI doesn’t magically fix underlying data problems. It amplifies them.

And here’s what makes this matter urgent: the biggest risk isn’t that AI will fail without good data. It’s that AI will work — confidently, at speed, and at scale — on bad data. It will automate the wrong decisions. It will surface misleading insights with conviction. It will scale your problems faster than any human team ever could.

Research reinforces the pattern:

• Gartner predicts that through 2026, organizations will abandon 60% of AI projects unsupported by AI-ready data.
• Salesforce’s 2025 State of Data and Analytics report found that 84% of data and analytics leaders say their data strategies need a complete overhaul before their AI ambitions can succeed.

Translation: companies are funding AI initiatives while underestimating the foundation those initiatives depend on.

This is an AI readiness gap — and it shows up in your data first. If your data foundation isn’t strong enough to trust in day-to-day decisions, AI will amplify the uncertainty quickly.

In this post, we’ll break down seven critical factors that determine whether your data foundation can support AI in real workflows, or whether gaps are quietly setting your initiatives up for disappointment.

For each factor, we’ll explain why it matters, what the research shows, and what practical action looks like right now.

What Is AI-Ready Data?

AI-ready data is data that can be trusted to drive decisions and automation in real workflows.

It’s not "perfect" data. It’s not “clean once and done.” And it’s not defined by a specific platform or architecture.

AI-ready data means your organization has data that is:

• Reliable: quality is understood, monitored, and good enough for the decisions AI is being asked to support
• Owned: accountability for key data domains is clear, not implied
• Governed: access, usage, and risk are managed appropriately, especially for sensitive or regulated data
• Connected: data flows across systems so AI has the context it needs, not fragmented snapshots
• Trusted: leaders and frontline teams agree on sources of truth for critical metrics and decisions

In other words, AI-ready data is data your teams are already willing to act on before AI enters the picture.

When those conditions aren’t in place, AI doesn’t just fall short. It confidently accelerates the same data issues you already have, pushing them deeper into automated decisions and scaled workflows.

If you’re investing in AI this year, these seven factors are where promising pilots either become dependable business capabilities or turn into expensive, hard-to-unwind failures.

AI-ready data isn’t “one and done.” It’s an ongoing capability that needs to evolve as your use cases, systems, and risk profile evolve.

Factor 1: Is Your Data Quality High Enough for AI?

Why this factor matters

Data quality has always mattered. AI raises the consequences.

Can you trust the inputs behind decisions? A reporting tool might surface a bad number in a spreadsheet that a human catches and corrects. AI does something more dangerous: it ingests that bad number, builds patterns around it, makes predictions from it, and serves those predictions to decision-makers as if they’re trustworthy.

AI can repeat the mistake at scale, without an obvious warning.

When data is wrong, missing, or out of date, AI sends teams in the wrong direction with confidence. The wrong accounts get prioritized. Renewals are misjudged. Service is routed poorly. Trust erodes quickly.

When trust breaks, adoption drops. In addition, the business takes on real risk: missed revenue, poor customer experiences, and decisions made on a false version of reality.

Salesforce’s 2025 research underscores how common this is. In its 2025 State of Data and Analytics report, Salesforce found that 89% of organizations with AI in production have experienced misleading or inaccurate outputs, and 55% have wasted significant resources training AI models on flawed data.

How to think about data quality for AI

Data quality for AI typically breaks down across three dimensions. You don’t need to perfect all of them before you start, but you do need to know which one is undermining trust and outcomes.

• Accuracy: Is the data correct, and how would we know?
• Completeness: Is anything missing that would materially change the decision?
• Timeliness (recency): Is the data updated recently enough to act on with confidence?

Red flags you’ll recognize

• Leadership meetings get stuck debating whose numbers are “right.”
• Teams maintain shadow spreadsheets because core systems are not trusted.
• AI outputs sound polished, but frontline teams immediately notice missing context.

What to focus on first

• Anchor data quality to a real decision. Choose a single, high-impact AI use case and define what “good enough” data means for that decision. Quality should be outcome-driven, not theoretical.
• Fix the biggest trust breaker. Identify the one data issue that most undermines confidence today, whether it’s incorrect values, missing context, or outdated information, and address that before expanding AI further.

When these fundamentals are in place, AI becomes something teams rely on to make real decisions.

Factor 2: Does Your AI Have Access to the Full Business Context It Needs to Be Right?

Why this factor matters

AI outputs are only as reliable as the data they can access. In most enterprises, a significant amount of critical information sits in disconnected systems, which limits AI’s ability to produce accurate recommendations.

The scale of the problem is hard to ignore:

• The average enterprise runs on nearly 900 applications, yet only about 30% are connected, so data fragmentation is the default.
• Salesforce research found that data leaders estimate 19% of their company’s data is siloed, inaccessible, or otherwise unusable, and many believe their most valuable insights are trapped there.
• Data access remains a bottleneck. 93% of business leaders say they’d perform better if they could ask data questions in natural language.
• Roughly 80-90% of a company’s data is unstructured data, including emails, documents, internal messages, PDFs, etc.

When AI operates on fragmented data, it produces outputs based on partial information. If key context is split across systems and teams, such as CRM in one place, support history in another, and finance details somewhere else, AI can still generate an answer. The output may be incomplete or wrong because the underlying context was missing.

This is where the real cost shows up. Teams act on incomplete context:

• Sales prioritizes the wrong accounts because service risk is not visible.
• Service routes issues without seeing entitlements or customer value.
• Leaders make decisions based on a single system snapshot that does not reflect what is happening across the business.

Red flags you’ll recognize

• Team members constantly ask around for the full story because no single view is reliable.
• Different teams give different answers to the same question (Sales versus Service versus Finance).
• Customer or operational context gets stitched together manually in spreadsheets, Slack, or email before decisions get made.

What to focus on first

• Define the minimum context AI needs to be right. For a single AI use case, identify the few pieces of information that would materially change the recommendation if they were missing.
• Stop manual context assembly. Decide where that context should live and how it stays current, so teams are no longer stitching together information every time a decision needs to be made.

The goal is a connected ecosystem where AI can draw from multiple sources with timely, accurate information. Effective data integration for AI is what enables reliable outputs across real workflows.

Salesforce Agentforce example

Rather than stopping at recommendations or generated responses, Agentforce agents can initiate workflows, carry out multi-step tasks, and escalate when confidence is low or approvals are required.

This is why Agentforce is described as agentic AI rather than generative AI. Its value comes from reasoning through business context and taking action within enterprise guardrails, including permissions, approvals, escalation paths, and full auditability.

Here's why that matters: when leads arrive overnight, cases spike during a product launch, or renewals need attention across hundreds of accounts, Agentforce can keep work moving without waiting for someone to notice and act. AI assistants, copilots, and chatbots aren't designed to operate that way.

Factor 3: Have You Established Data Governance for AI?

Why this factor matters

Data governance is widely understood as important, but many organizations struggle to make it operational. AI raises the stakes because it does more than report on data. It uses data to generate outputs that influence decisions, recommendations, and automated actions.

Salesforce’s 2025 research found that only 43% of data and analytics leaders have established formal data governance frameworks. Among those leaders, 88% agree that AI advances demand entirely new approaches to data governance and security.

Without AI-ready data governance, AI operates without consistent rules and accountability. Ownership of data assets is unclear. Definitions vary across teams, so the same metric can mean different things in different systems. Issues surface, but accountability is murky when outputs are misleading or inaccurate. Organizations also lack clear guardrails for which data should be used for which AI use cases.

The impact shows up in two places quickly:

• Security and compliance exposure increases when access and usage rules are not consistently defined or enforced.
• Decision-making slows down when leaders cannot trust the numbers, insights, or recommendations being presented.

Data governance for AI requires decision discipline. Leaders need clarity on:

• Who owns key data domains
• What “true” means for critical metrics
• Who can use the data
• How issues get resolved and prevented

Red flags you’ll recognize

• No one can clearly name who owns core data domains like customer, revenue, product, or service.
• Teams use the same terms differently, such as customer, active user, churn risk, and renewal date, which turns AI outputs into debate instead of decisions.
• When data issues show up, people do not know where to report them, who fixes them, or how they will be prevented next time.

What to focus on first

• Clarify ownership and decision rights. Define who owns the data domains your AI relies on and who is accountable when definitions, quality, or usage break down.
• Align on shared meaning. Standardize a short set of definitions and rules that teams and AI will use, so insights lead to decisions instead of debate.

When data governance is treated as an operating layer, not a compliance exercise, AI becomes easier to trust, easier to scale, and far more useful in real workflows.

Factor 4: Are Your Systems Integrated Well Enough to Support AI at Scale?

Why this factor matters

This factor comes down to whether information can move reliably enough to support AI in real operations. When data does not flow consistently across systems as the business runs, even well-designed AI workflows break down.

Integration is a foundational requirement for AI in production. When data integration is strong, workflows run without friction. When it is weak, AI outputs arrive late, downstream processes fail, and teams resort to manual work to keep operations moving.

Gartner research reflects how common this challenge is. A 2025 Gartner survey found that 48% of infrastructure and operations leaders cite integration difficulties as a top AI adoption challenge, making it one of the most frequently reported barriers alongside budget constraints.

This is where many organizations get stuck. AI works in pilots, demos, or controlled environments. Production is different. Data needs to flow continuously across systems, updates need to stay current, and reliability matters more than novelty.

When integrations are fragile or inconsistent, AI outputs are based on stale or incomplete information. The result is guidance that feels disconnected from what teams are seeing on the ground.

Red flags you’ll recognize

• Teams don’t trust the dashboards because numbers don’t match across systems, so meetings turn into reconciliation instead of decision-making.
• Important signals arrive too late to act on, like renewal risk, inventory status, case escalations, or payment issues, so AI insights show up after the moment has passed.
• Work keeps falling back to manual steps: exports and imports, spreadsheet merges, rekeying data, or “someone has to update Salesforce,” because systems are not staying in sync.

What to focus on first

• Take an inventory of your most business-critical integrations. Look for connections that routinely break, require manual fixes, or leave teams reconciling data across systems.
• Improve the integrations that feed your highest-value AI use case first. Start where reliability impacts outcomes directly, then expand once the foundation is stable.

Integration is moving toward more automation, and organizations that reduce manual effort here will scale faster. Gartner’s strategic planning assumption is that by 2027, AI assistants and AI-enhanced workflows in data integration tools will reduce manual intervention by 60% and enable self-service data management.

Factor 5: Is Your Data Protected Against AI-Specific Security Risks?

Why this factor matters

Many organizations have mature security programs, but those controls often stop short of how data is used inside AI tools and AI-enabled workflows.

AI usage is already spreading across the business. Many teams rely on AI to draft emails, summarize meetings, prepare proposals, analyze customer conversations, and answer internal questions. This behavior is becoming a normal part of everyday work.

The practical issue is whether your organization has defined clear boundaries for AI use. Teams need to know which data must never be used, which data is allowed under specific conditions, and which tools are approved. Those rules also need to be simple enough to follow when people are moving fast.

AI introduces new exposure patterns. Sensitive data can be copied into prompts, uploaded as documents, pulled from internal systems through connectors, and reused or transformed in ways that traditional security controls do not always capture.

Most of this exposure originates internally. Well-intentioned employees create risk when they overshare data, use unsanctioned tools, or move sensitive information into AI systems without proper safeguards.

Gartner predicts that through 2026, at least 80% of unauthorized AI transactions will be caused by internal policy violations: employees oversharing data, using AI tools in unsanctioned ways, or feeding sensitive information into systems without proper controls.

The rise of shadow AI amplifies the problem. According to IBM’s 2025 Cost of a Data Breach Report, breaches involving shadow AI cost organizations $4.63 million on average, which is $670,000 more than the average breach cost.

The gap between AI adoption speed and data security readiness is one of the most dangerous disconnects in enterprise technology today.

The kinds of data that commonly create risk

Employees frequently paste or upload the following data into personal or unsanctioned AI tools:

• Customer and personal data: Names, emails, phone numbers, IDs, addresses, support transcripts
• Commercially sensitive information: Pricing, discounting, margins, deal terms, contracts, renewal risk
• Financial and operational data: Revenue figures, forecasts, payroll, vendor terms, and incident reports
• Security and internal access information: Credentials, API keys, internal configs, system diagrams
• Confidential internal content: Strategy decks, board materials, M&A discussions, legal documents, HR matters

Red flags you’ll recognize

• Teams are using AI for real work, but no one can clearly explain what data is restricted
• Employees rely on personal or unsanctioned AI accounts because approved options are unclear or inconvenient.
• Sensitive internal content appears in prompts, uploads, or AI-generated summaries as teams try to move faster.

What to focus on first

• Make AI data usage visible. Create a living inventory of the AI tools and data connections in use, including unsanctioned ones, so security can manage behavior.
• Make “safe AI use” easy to understand. Publish a short, plain-language set of guidelines that defines restricted data, conditionally allowed data, and approved tools.
• Offer secure alternatives for common tasks. Provide approved tools for summarization, drafting, and analysis so teams do not default to shadow AI.

This approach supports productivity while reducing exposure. Teams gain clarity and speed without turning everyday work into a security risk.

Factor 6: Have You Defined Which Data Sources AI Should Trust for Each Decision?

Why this factor matters

Most organizations do not have a single system that represents “the source of truth” for everything, and that is normal. Different systems are authoritative for different parts of the business.

Salesforce often reflects account activity and pipeline signals. Finance systems are authoritative for billing and payment status. Support platforms contain case history and escalations. Product analytics captures usage behavior.

Problems arise when an AI workflow relies on the wrong system for the decision it is supporting, or when it combines conflicting signals without clear rules. The output may sound credible while being built on inputs that do not align with how the business actually operates.

This is a data readiness issue rather than an AI model issue. When authoritative sources are not clearly defined, AI exposes inconsistencies faster and at a greater scale than traditional reporting. What previously showed up as minor reporting discrepancies now appears directly in recommendations that teams are expected to act on.

Red flags you’ll recognize

• AI outputs trigger immediate pushback like, “That’s not the real number,” or “That field isn’t reliable,” because the workflow relied on the wrong system or stale copies.
• Different teams interpret the same AI recommendation differently because they’re anchoring to different data sources.
• Teams feel compelled to check multiple systems before acting, which slows decisions and undermines confidence in AI-supported workflows.

What to focus on first

• Define the source of truth by decision. For one high-value AI use case, list the set of data points that drive the recommendation, then specify which system is authoritative for each one.
• Establish rules for data conflicts. Decide how discrepancies or missing data should be handled so AI-supported workflows follow predictable logic and teams are not left resolving disagreements after the fact.

When these boundaries are clear, AI-supported decisions become easier to validate and more effective in day-to-day operations. Teams understand which data was used, where it came from, and why the output can be trusted.

Factor 7: Are Your Data Strategy and AI Strategy Aligned?

Why this factor matters

AI creates value when it improves a business outcome inside a real workflow. Many AI initiatives stall when they move forward without the data decisions, ownership, and funding required to support them in production.

Many organizations invest thoughtfully in both AI and data. Business leaders push AI initiatives to move faster and compete more effectively. Data and technology leaders invest in platforms, governance, integration, and reporting to improve reliability.

Problems arise when these efforts are not connected through a shared plan that links AI outcomes to the data capabilities required to deliver them.

When alignment is missing, AI projects move forward on assumptions. The data decisions that make AI usable in operations get deferred and then made under pressure. Timelines slip, confidence drops, and work expands as teams discover dependencies late.

This is not a tooling problem. It is a strategy and alignment problem.

Strategy alignment determines whether AI becomes a repeatable business capability or a set of disconnected experiments.

Red flags you’ll recognize

• AI and data priorities are both treated as urgent, but there is no single plan connecting them. Dependencies surface late, and work gets sequenced midstream.
• Ownership is distributed across teams, and when something breaks (definitions, access, integration, approvals), it is unclear who has decision rights to unblock progress quickly.
• Leaders expect impact quickly, but key decisions have not been made at the leadership level. Teams debate what the metric means, which system is authoritative, what data is allowed, and how the workflow should change. Outputs trigger discussion rather than action.

What to focus on first

• Create a shared plan tied to one priority outcome. Bring business, data, and technology leaders together around a single AI-driven outcome, the workflow it affects, and the data decisions required to support it.
• Fund the outcome, not just the technology. Ensure the investment covers the full path to impact, including workflow changes and the data work required to make AI usable in practice.

The goal is early alignment on decisions, ownership, and investment. That is what keeps AI success measured by business outcomes and adoption, not technical milestones.

When alignment happens at the planning level, the first six factors connect into a coordinated operating approach. Teams move faster with fewer surprises. Trust builds earlier. AI delivers value that justifies continued investment.

The Bottom Line

These seven factors are connected. Data quality issues get amplified by silos. Silos persist without governance. Governance fails without ownership and adoption. Security exposure grows when AI tools and data flows are not visible. Business value depends on strategic alignment.

Organizations seeing real results from AI are rarely the ones with the biggest budgets or the flashiest technology. They’re the ones that recognized early that AI success starts with data, and treated it like a business-critical foundation, with clear decisions, ownership, and operating discipline.

If these seven factors are already in place, your organization is positioned to scale AI with fewer surprises. If a few sections raised concerns, that is common. It is also fixable. What matters is acting before the next wave of AI investment locks weak assumptions into automated workflows.

The good news: these are solvable problems. The question isn’t whether you can get your data AI-ready. It’s whether you’ll do it before your next AI investment, or after it has already cost you.

How Summit Can Help

Getting data AI-ready requires coordinated work across data foundations, systems, governance, security, and the workflows where AI will be used. Progress depends on clear priorities, decision rights, and follow-through in production.

Summit helps organizations connect these pieces into a practical path forward that supports real outcomes.

Summit’s Data and AI Readiness services help you:

• Connect AI goals to the data decisions required to deliver them, so “AI readiness” is defined by the workflow and outcome, not guesswork.
• Focus on the small number of data domains and system connections that matter most first, so momentum builds instead of disappearing into a backlog.
• Put guardrails in place for trust and security, so teams can use AI in day-to-day work without creating exposure.
• Keep execution tied to measurable business outcomes, so AI investments translate into adoption and value.

Whether your organization is in planning or already piloting, Summit can help you identify the highest-risk gaps, sequence the work, and move from pilots into dependable production workflows.

If AI investment is a priority this year, data readiness should be treated as part of the plan.

• Explore Summit’s Data, Analytics, and AI Services to see how Summit strengthens data foundations and supports real AI impact.
• For Salesforce organizations, start with Data and AI readiness for Salesforce ecosystems, where data, workflows, and adoption must work together in production.

Or connect with our team to discuss how we can help align your AI goals with the data decisions required to support them.