DiLytics - BI-to-AI Opportunity & Readiness Check

DiLytics Diagnostic

BI-to-AI Opportunity & Readiness Check

Find out where your organization stands on the journey from BI to AI.

Complete all 10 questions to receive your diagnostic result and tailored recommendations.

10 questions · 2–3 minutes · Get your indicative score

Page 1 of 5 • Data Foundation

Questions 1 & 2

How well integrated is the data behind your key BI and analytics? Which best describes your environment?

Highly fragmented: Key business data remains in disconnected systems and silos. Limited integration: Some important sources are integrated, but significant gaps remain. Partially integrated: Most major sources are available, but integration is incomplete. Well integrated: Most important business data is integrated and readily available for analytics. Highly integrated: Key enterprise data is broadly integrated and readily accessible for analytics.

How confident are you in the quality of the data underlying your BI and analytics? Consider accuracy, completeness, consistency and timeliness.

Low confidence: Data-quality problems frequently undermine analysis. Limited confidence: Significant issues occur regularly. Moderate confidence: Data is generally usable, but recurring issues exist. High confidence: Data is reliable with relatively few issues. Very high confidence: Data quality is actively governed and trusted across the organization.

Page 2 of 5 • BI & Analytics

Questions 3 & 4

How deeply is BI embedded in business decision-making?

Reporting only: BI is primarily used for periodic reporting. Limited: Some teams use BI for decisions, but adoption is inconsistent. Established: BI regularly supports operational and management decisions. Broadly embedded: BI is routinely used across important business functions. Core to decision-making: Analytics is deeply embedded in business processes and decisions.

To what extent does your important business analysis rely on spreadsheets, manual extracts or other workarounds outside your formal BI environment?

Extensive: Much of the organization's important analysis relies on spreadsheets, manual extracts or other workarounds. Significant: A substantial amount of important analysis depends on spreadsheets, manual extracts or other workarounds. Moderate: Important analysis regularly involves spreadsheets or other tools alongside BI. Limited: Spreadsheets or other workarounds are used occasionally for supplementary analysis. Very little: Important analysis is almost entirely performed within the formal BI/analytics environment.

Page 3 of 5 • Analytics Gaps

Questions 5 & 6

How much analyst or management time is spent explaining why a KPI or business result changed?

Extensive: Explaining why KPIs or business results changed is largely manual and consumes significant analyst or management time. Significant: A substantial amount of analyst or management time is spent identifying and explaining the drivers of business-result changes. Moderate: Explaining significant changes often requires additional analysis by analysts or managers. Limited: Some additional analysis is needed, but explanations can usually be developed with relatively little effort. Very little: KPI and business-result changes can generally be understood using existing analytics with minimal additional effort.

How much manual effort is required to identify and investigate exceptions, anomalies or unexpected changes in business performance?

Extensive: Exception identification and investigation is largely manual and requires significant analyst effort. Significant: Identifying and investigating exceptions frequently requires substantial manual analysis. Moderate: Some exceptions can be identified and investigated easily, while others require manual analysis. Limited: Most exceptions can be identified and investigated with relatively little manual effort. Very little: Exceptions and unexpected changes are readily identified and investigated using existing analytics capabilities.

Page 4 of 5 • AI Opportunity

Questions 7 & 8

How valuable would it be for business users to ask questions of their data using natural language? For example: "Why did operating costs increase last quarter?"

Little or no value: Existing BI and analytics capabilities adequately meet users' needs. Limited value: Natural-language interaction could be useful in a few situations, but would have limited impact. Moderate value: It could meaningfully improve how users access and explore information in some areas. High value: It could significantly improve users' ability to get insights and make decisions. Very high value: It could substantially change how users interact with data and access insights across the organization.

How valuable would automated AI-generated explanations, insights or narratives from your BI data be? For example: explaining KPI movements, identifying key drivers and summarizing management performance.

Little or no value: Existing BI and analytics provide sufficient explanations and insights for most business needs. Limited value: AI-generated explanations or insights could be useful in a few situations, but would have limited impact. Moderate value: They could improve understanding of business performance and trends in some areas. High value: They could significantly reduce the effort needed to understand performance, identify drivers and communicate insights. Very high value: They could substantially improve how the organization understands, interprets and communicates insights from BI data.

Page 5 of 5 • AI Readiness

Questions 9 & 10

How clearly are business owners identified for the AI use cases your organization is considering or may pursue?

Not at all clear: AI use cases are still exploratory, with ownership yet to be established. Slightly clear: Ownership has been discussed for some areas, but is largely undefined. Moderately clear: Business ownership is defined for some priority use cases. Clearly defined: Most priority use cases have identified business owners. Very clearly defined: Each priority AI use case has a clearly accountable business owner.

How strong is executive sponsorship for applying AI to business processes, data and analytics?

Very weak: There is little or no executive interest or sponsorship. Weak: AI is discussed by leadership, but sponsorship is limited or informal. Moderate: Some executives actively support selected AI initiatives. Strong: Executive leadership actively supports and promotes AI initiatives. Very strong: AI has visible, sustained executive sponsorship with leadership actively driving its adoption.

Ready to View Your BI-to-AI Score?

Once all 10 questions across all 5 pages are answered, click below to generate your personalized BI-to-AI Score and 5-pillar profile.

Please answer all 10 questions before getting your score. Missing questions are highlighted in red.

Diagnostic Progress

0 / 10

Questions Completed

Page 1 • Data Foundation 0/2

Page 2 • BI & Analytics 0/2

Page 3 • Analytics Gaps 0/2

Page 4 • AI Opportunity 0/2

Page 5 • AI Readiness 0/2

Please answer all 10 questions.

Your Diagnostic Result

0/100

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Your BI-to-AI Profile & Dimension Assessment

See how your current BI and analytics environment positions you for AI across each core pillar.

Data Foundation 0%

Needs Attention

AI Readiness 0%

Readiness Gaps

BI & Analytics 0%

Opportunity to Strengthen

AI Opportunity 0%

Selective Opportunity

Analytics Gaps 0%

Significant Pain Points

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