--- title: "Avaliação de prontidão para IA: Sua empresa pronta para IA" description: "Descubra em minutos o nível de prontidão da sua empresa para IA e os próximos passos para escalar com confiança." url: "https://cheesecakelabs.com/br/ai-readiness-assessment" locale: "pt" updatedAt: "2026-08-04T19:35:02.985Z" --- # Avaliação de prontidão para IA: Sua empresa pronta para IA > Descubra em minutos o nível de prontidão da sua empresa para IA e os próximos passos para escalar com confiança. *Free Diagnostic Tool* ## AI Transformation Readiness Assessment Discover where your organization stands on the path from AI experimentation to true business transformation, and what to do next. 24 questions · 10–12 minutes · 7 dimensions scored ### Explore the 7 Dimensions Each dimension represents a critical aspect of AI transformation. Click on the cards to explore. ### Strategic Vision & AI Ambition Are you bolting AI onto existing processes (Wave 1), or redesigning how your business works (Wave 2)? **1. How would you describe your organization's primary AI objective right now?** - We're still exploring what AI could do for us - We're using AI tools to speed up existing tasks - We're redesigning specific workflows around what AI makes possible - We're rethinking our business model and competitive positioning with AI at the center **2. When leadership discusses AI, the conversation typically centers on:** - General excitement or anxiety about AI trends - Specific tools and vendors (ChatGPT, Copilot, etc.) - Which business processes to transform and what outcomes to target - How AI fundamentally changes our value proposition and industry dynamics **3. Has your organization defined a clear "North Star" metric that AI initiatives must serve?** - No, we don't have a metric tied to AI efforts - We have general goals like "efficiency" or "cost reduction" but nothing specific - We have specific outcome metrics for individual AI projects - We have a company-wide North Star metric, and every AI initiative is explicitly tied to it ### Process Readiness Have you redesigned your processes for AI, or are you automating the mess? **4. When considering AI for a business process, what does your team typically do first?** - Look for an AI tool that seems relevant and try it out - Identify a task within the current process to automate - Map the end-to-end workflow, then redesign it with AI in mind - Map the workflow, identify the key decisions (not just tasks), define the future state, and plan for edge cases **5. How does your organization handle edge cases and failure scenarios in AI-powered processes?** - We haven't thought about this yet - We rely on users to catch and escalate AI errors - We design specific fallback procedures for known failure modes - We design for the "unhappy path" from day one. Every AI workflow has explicit error handling, escalation, and human intervention points **6. For your key workflows, have you defined what level of AI involvement is appropriate: Assist (AI supports a human), Approve (AI does the work, human approves), Audit (AI runs, humans review periodically), or Automate (AI runs autonomously)?** - We have not classified our workflows this way - We are aware of these levels but apply them informally or inconsistently - We have mapped most key workflows to one of these levels based on risk and complexity - Every AI workflow is formally classified into Assist, Approve, Audit, or Automate, with clear criteria for each level **7. Before applying AI to a process, do you identify where the real bottleneck is, and where it will shift after AI is introduced?** - We don't typically think about bottleneck shifts - We focus on the current bottleneck only - We consider how AI might shift constraints to other parts of the process - We systematically map both current and post-AI bottlenecks, and redesign the entire flow accordingly ### Decision Architecture & Governance Do you have a principled framework for deciding what AI should control, or are you guessing? **8. For a given business process, can your team articulate which decisions AI should handle vs. which require human judgment?** - We haven't made this distinction yet - We have a general sense but no formal framework for deciding - We explicitly map human vs. AI decision points for each AI initiative - We use a structured framework that considers cost of error, reversibility, and required judgment to assign the right AI autonomy level **9. How do you decide whether a process should be fully automated, periodically audited, or always require human approval?** - We default to keeping humans involved in everything - We automate what feels safe and keep humans for everything else - We evaluate based on risk level and how easy errors are to reverse - We use a principled governance model with clear levels (from AI-assisted to fully autonomous) based on error cost and reversibility **10. Does your organization distinguish between processes that need deterministic (exact) outputs vs. those where probabilistic AI outputs are acceptable?** - We haven't considered this distinction - We're aware of it but don't formally classify our processes this way - We classify critical processes as deterministic vs. probabilistic before choosing technology - This classification is a required decision gate in our AI governance framework ### Data & Technology Foundations Is your data infrastructure ready to power AI, or are you building on top of disconnected, ungoverned systems? **11. How would you describe the state of data that your AI systems need to access?** - Our data is scattered across disconnected systems with no clear ownership or quality standards - We have data in structured systems but with significant quality gaps and limited integration between them - Our key data sources are accessible, reasonably clean, and we have processes for data quality and ownership - Our data infrastructure is well-governed, integrated across systems, with clear ownership, quality standards, and access controls **12. How integrated are the core systems (CRM, ERP, support, finance, etc.) that AI would need to work across?** - Most systems are siloed with manual data transfers between them (spreadsheets, copy-paste, email) - Some systems are connected through basic integrations, but many gaps remain - Our core systems are integrated with defined data flows, though some manual handoffs still exist - Our systems are well-connected through APIs and data pipelines, enabling AI to work across departments seamlessly **13. Does your organization have a data governance framework that covers who owns data, who can access it, and how it is used by AI?** - We have no formal data governance in place - We have some policies around data access and security, but they were not designed with AI in mind - We have data governance that includes AI-specific considerations like training data policies, bias monitoring, and access controls - We have a comprehensive governance framework that covers data lineage, AI-specific read/write permissions, privacy compliance, and regular audits **14. How does your organization approach AI tool and model selection (e.g., choosing between different LLMs, building vs. buying, or right-sizing models for specific tasks)?** - We have not evaluated AI tools or models in any structured way - We default to whichever AI tool is most popular or recommended by a vendor, without comparing alternatives - We evaluate AI tools based on specific criteria like cost, accuracy, latency, and data privacy requirements - We have a structured evaluation process that considers hybrid architectures (e.g., large models for orchestration, smaller models for execution), total cost of ownership, and vendor independence ### Technology & Infrastructure Is your tech stack ready for AI agents, or are you running on systems that can't talk to each other? **15. Is your organization currently using or piloting AI agents that take autonomous actions (not just chatbots or copilots that assist humans)?** - We have not explored AI agents yet - We are aware of AI agents but have not piloted any - We are piloting AI agents in one or two workflows, with human oversight on every action - We have AI agents running autonomously in production workflows, with monitoring and guardrails in place **16. How would you describe the core systems your teams use daily (ERP, CRM, support, finance)?** - Most of our core systems are legacy, on-premise, and over 10 years old, with limited vendor support - We have a mix of older and newer systems, but upgrades are slow and not prioritized - Most systems are relatively modern (cloud-hosted or recently upgraded), though some legacy systems remain - Our core systems are modern, cloud-native, and regularly updated, with a clear technology roadmap **17. Can your key business systems talk to each other programmatically (via APIs), or do most integrations depend on manual work?** - Most systems have no APIs; data moves via spreadsheets, email, and copy-paste - Some systems have APIs, but we rarely use them; integrations are mostly manual or through basic file exports - Our main systems are connected via APIs, though some integrations are still manual or use middleware - Our systems are API-first with documented endpoints, and we actively use integrations and automations across them **18. Where does your organization's data and compute infrastructure primarily live?** - Primarily on-premise servers or local data centers, with no cloud strategy - Mix of on-premise and cloud, with no clear migration plan or cloud-first policy - Mostly cloud, with a defined architecture and some on-premise systems for specific needs - Cloud-first strategy with infrastructure-as-code, scalable compute, and clear policies on data residency and security ### Measurement & ROI Are you measuring what matters, or tracking vanity metrics that won't survive a budget review? **19. How does your organization measure the return on AI investments?** - We don't have specific AI ROI metrics yet - We track adoption metrics: usage rates, hours saved, number of users - We measure business outcomes: cycle time, throughput, cost-to-serve, revenue impact - We measure end-to-end outcomes, track what we do with freed capacity, and tie results directly to our North Star metric **20. When you free up employee time through AI, what happens with that capacity?** - We haven't tracked this. It's unclear where the time goes - People absorb the time into existing work; no deliberate reallocation - We have a general plan to redirect capacity to higher-value work - We define upfront how freed capacity will be redeployed, and we measure whether it actually generates new value **21. If an AI pilot doesn't deliver expected results, what typically happens?** - The project is shelved or considered a failure - We try to iterate but often revert to the old process - We analyze what went wrong, document learnings, and decide whether to pivot or persist - We treat every pilot as a learning experiment. We measure why it failed, share findings broadly, and apply insights to the next initiative ### Culture & People Is your organization culturally ready for AI, or is adoption stalling at the middle? **22. How does leadership communicate about AI's impact on jobs and roles?** - We avoid the topic or haven't addressed it directly - We reassure people their jobs are safe, but haven't defined how roles will evolve - We're transparent that roles will change and have started defining the new skills needed - We actively tell people "your job is to redesign your own job" and support them with time, tools, and psychological safety to do it **23. How would you describe your organization's experimentation culture around AI?** - Very cautious. Most people haven't tried AI tools at work - Some individuals experiment on their own, but it's not encouraged or shared broadly - We encourage experimentation and have some structured time or programs for it - Experimentation is celebrated and systematized. We share wins AND failures, reward learning, and give teams dedicated time to explore **24. Do your senior leaders and mid-level managers share the same view on AI's potential and urgency?** - We haven't assessed alignment across leadership levels - Senior leadership is enthusiastic but middle management is skeptical or overwhelmed - There's general alignment, though expectations on pacing differ - We've actively worked to close the perception gap. All levels share a common understanding of why, what, and when ### Maturity levels **1 — Foundational.** Your organization is in the early stages of AI awareness. The opportunity ahead is significant, and with the right approach, you can leapfrog companies that started earlier but got stuck in Wave 1. **2 — Developing.** You've started exploring AI but are primarily in "Wave 1": bolting point solutions onto existing processes. The next step: move from tools to transformation by redesigning workflows, not just automating tasks. **3 — Advancing.** Your organization has solid AI foundations and is beginning to redesign processes rather than just automate tasks. Focus now on governance maturity, measurement rigor, and building scalable patterns. **4 — Transforming.** You're operating at "Wave 2": genuinely rethinking how your business works with AI at the center. Keep pushing on culture, governance evolution, and measurable outcomes to sustain your advantage. Built by **Cheesecake Labs** · Applied AI Solutions Grounded in frameworks from Wharton Executive Education and enterprise AI deployments at Amazon, T-Mobile, and Blue Origin.