AI Suitability Assessment Guide

If your team is asking “Can we use this AI tool?” You need governance.   Especially when AI systems can develop discriminatory bias, give incorrect advice, leak customer data, introduce security flaws, and perpetuate outdated assumptions about users.   AI governance programs and assessments are no longer an optional best practice.   They're on the fast track to becoming mandatory as several AI regulations roll out. Most notably for high-risk AI use. I recommend AI assessments beyond high risk use cases to also capture the privacy, security and ethical risks. Here’s how companies can conduct an AI risk assessment: ✔ Start by building an AI data inventory List every AI tool in use, including hidden ones embedded inside vendor software. Capture data inputs, decisions it makes, who has access, and outputs. ✔ Assess the decision impact Identify where wrong AI decisions could cause harm or discriminate, and review AI systems thoroughly to understand if it involves high-risk.   ✔ Examine company data sources Check whether your training data is current, representative, and free from historical bias. Confirm you have disclosures and permissions for use. ✔ Test for bias and fairness Run scenarios through AI systems with different demographic inputs and look for discrepancies in outcomes. ✔ Document everything Maintain detailed records of the assessment process, findings, and changes you make. Regulations like the EU AI Act and the Colorado AI Act have specific requirements for documenting high-risk AI usage.   ✔ Build monitoring checkpoints Set regular reviews and repeat risk assessments when new products or services are introduced or as models, vendors, business needs, or regulations change. AI oversight isn’t coming someday. It’s here.   Companies that start preparing now will be ready when the new regulations come into force. Read our full blog for more tips and to see how to put this into action 👇

Most AI vendor decisions fail before the contract is even signed. Not because the tech isn't good enough... but because choosing the right AI partner is more complex than most people expect. And now every leader is trying to answer the same question: “How do we choose the right AI partner without wasting time, money, or trust?” Here’s what I’ve learned 👇 A credible vendor accelerates your transformation. The wrong one slows everything down. That’s why I created this simple one-page guide to help teams evaluate AI vendors with clarity, confidence, and practical criteria. It’s built from best practices used across top consulting firms, but adapted to be: ✔️ Practical ✔️ Non-technical ✔️ Usable by any team making AI decisions 𝗪𝗵𝗮𝘁’𝘀 𝗶𝗻𝘀𝗶𝗱𝗲: • Key evaluation criteria • A practical scorecard framework • Red flags that help you avoid costly mistakes 𝟯 𝘁𝗵𝗶𝗻𝗴𝘀 𝘁𝗵𝗮𝘁 𝗺𝗮𝘁𝘁𝗲𝗿 𝗺𝗼𝗿𝗲 𝘁𝗵𝗮𝗻 𝗺𝗼𝘀𝘁 𝗽𝗲𝗼𝗽𝗹𝗲 𝗿𝗲𝗮𝗹𝗶𝘇𝗲: 1. Start with trust, not features. ↳A shiny demo means nothing without credibility and a track record. 2. If a vendor can’t explain how their AI works… that's a red flag. ↳Transparency isn’t optional anymore. 3. Even the best tools fail inside the wrong workflow. ↳ Fit > features. Every time. 🔖Save this for your next vendor conversation ♻️ Share if your network is navigating this too _______ ➕ Follow Ana Petras for practical, human-centered AI guidance #AIEvaluation #FutureOfWork #ResponsibleAI #DiverseAISolutions

Most marketing leaders overestimate their AI skills. A tiny percentage are using AI right. Jumping on the AI bandwagon without a clear strategy can waste budget, stall momentum, and expose your brand to risk. That’s why we created the AI Strategy Audit for Marketing, a quick assessment tool that helps you understand where your team stands across eight core areas, from data readiness to ethical AI use. Check all the true statements in each section: 1. Strategic Alignment [ ] Our marketing goals are clearly defined and measurable. [ ] We have identified how AI can support specific objectives (e.g., lead gen, customer insights, content creation). [ ] AI initiatives align with our broader business strategy. 2. Data Readiness [ ] We have access to clean, organized customer and campaign data. [ ] Our data is integrated across platforms (CRM, email, web analytics, social). [ ] We're using data to personalize experiences or predict behaviors. 3. Tech Stack & Tools [ ] We currently use or are evaluating AI-powered tools (e.g., chatbots, predictive analytics, generative content). [ ] Our team knows which tools are best for each marketing function. [ ] We assess ROI before adopting new tools. 4. Team & Skills [ ] Our team understands AI basics and its marketing applications. [ ] We’ve provided training or resources to upskill our team in AI tools. [ ] We have a process for testing and integrating new AI solutions. 5. Execution & Measurement [ ] We run small-scale pilots before full AI adoption. [ ] Key performance indicators (KPIs) are defined for all AI initiatives. [ ] We regularly review AI tool performance and update strategies. 6. Training & Change Management [ ] We have a structured training plan for adopting AI tools. [ ] Change management is embedded in our AI rollout process. [ ] Feedback loops are in place to adapt training and improve adoption. 7. Ethics [ ] We evaluate AI tools for potential bias or harmful outcomes. [ ] Marketing messages generated with AI are reviewed for ethical alignment. [ ] We promote transparency in how AI influences marketing decisions. 8. Data Privacy [ ] We comply with relevant privacy laws (e.g., GDPR, CCPA). [ ] Customer data used in AI systems is securely stored and managed. [ ] We disclose AI use where it impacts user data or personalization. 📝 Score Yourself: 22–24 checks: AI-Mature. You're leveraging AI responsibly and effectively. 13–21 checks: AI-Ready. Strong foundation with room to grow. 0–12 checks: AI-Curious. Start small, focus on data and skills first. Use your score to: ➤ Pinpoint where your AI strategy needs attention ➤ Spark internal conversations that drive clarity and action ➤ Prioritize the next best steps without chasing shiny tools ♻️ Repost to your network if they need to see this. DM me if you want to discuss getting a full AI Readiness Action Plan for your organization.

AI risk management is evolving quickly. For many organizations, especially SMEs, the real challenge is not ambition. It is knowing where to begin. AI Management Essentials (AIME), a new self-assessment tool from the UK Department for Science, Innovation and Technology (DSIT), provides a clear starting point. Published for consultation in November 2024, AIME focuses on practical steps to help teams build a baseline for responsible AI management. Why this is timely: → Many frameworks like ISO 42001, NIST RMF, and the EU AI Act are difficult to apply without expert support → AIME simplifies these into 10 clear categories that align with real-world operations → It was created specifically with smaller organizations in mind, without sacrificing depth What AIME includes - The tool helps organizations evaluate the maturity of their AI management systems across 10 areas: -AI system records -AI policy -Fairness -Impact assessments -Risk assessments -Data governance -Bias mitigation -Data protection -Issue reporting -Third-party communication Each section features: → Motivating statements that define good practice → Diagnostic questions to assess current state → A future roadmap including scores and action recommendations based on input Designed for flexibility: → Built for SMEs and startups, but scalable for departments within large organizations → Informed by pilots with regulators, industry partners, and techUK → Meant to complement, not replace, standards like ISO or regulations like the AI Act What stands out: → Encourages transparency through AI system records and risk logs → Clarifies the difference between fairness and bias, and pushes for ongoing monitoring → Emphasizes data documentation, including provenance, completeness, and representativeness → Introduces guidance for evaluating third-party AI services and pretrained models → Promotes anonymous and transparent issue reporting for employees and users The bigger picture: Governments are moving toward stronger AI oversight. Tools like AIME provide a credible way to prepare today while standards and laws continue to evolve. One action item: Review the AIME consultation draft and compare it to your existing AI practices. Even basic alignment will help build trust, improve readiness, and demonstrate good governance. #AIManagement #ResponsibleAI #AICompliance #DSIT #AIGovernance #SME #ISO42001 #EUAIAct #NISTRMF #AIReadiness #TrustworthyAI

I watched executives struggle with a crucial question. "How do we know which AI projects are worth doing?" This wasn't theoretical.  This was during my work with the government on AI project evaluations. They had dozens of AI ideas.  Each one promised transformation.  Each one had a compelling business case. I was tasked to figure out how to rate them all. Here's what I learned: the question isn't whether AI can deliver value. The question is whether we're checking all the ways it can fail. Most AI projects don't fail because of bad technology.  They fail because nobody evaluated them properly. This was my premise going into it. I had to figure out: → What are the risks? → Who could this harm? → Will people actually use it? → Can we keep this running after the pilot? I ended up developing a simple framework that fixes this called the 𝗔𝗜 𝗘𝘃𝗮𝗹𝘂𝗮𝘁𝗶𝗼𝗻 𝗦𝗰𝗼𝗿𝗲𝗰𝗮𝗿𝗱. It examines seven key elements that every successful AI project requires. 𝟭. 𝗕𝘂𝘀𝗶𝗻𝗲𝘀𝘀 𝗩𝗮𝗹𝘂𝗲 → What results will this create? 𝟮. 𝗧𝗲𝗰𝗵𝗻𝗶𝗰𝗮𝗹 𝗙𝗲𝗮𝘀𝗶𝗯𝗶𝗹𝗶𝘁𝘆 → Can we actually build this with what we have? 𝟯. 𝗦𝘁𝗿𝗮𝘁𝗲𝗴𝗶𝗰 𝗙𝗶𝘁 → Does this match where we're headed? 𝟰. 𝗥𝗶𝘀𝗸 𝗮𝗻𝗱 𝗖𝗼𝗺𝗽𝗹𝗶𝗮𝗻𝗰𝗲 → What could go wrong? What laws do we need to follow? 𝟱. 𝗘𝘁𝗵𝗶𝗰𝗮𝗹 𝗮𝗻𝗱 𝗥𝗲𝘀𝗽𝗼𝗻𝘀𝗶𝗯𝗹𝗲 𝗨𝘀𝗲 → Who could get hurt? How do we prevent it? 𝟲. 𝗖𝗵𝗮𝗻𝗴𝗲 𝗥𝗲𝗮𝗱𝗶𝗻𝗲𝘀𝘀 → Will people use this, or will it sit on a shelf? 𝟳. 𝗦𝗰𝗮𝗹𝗮𝗯𝗶𝗹𝗶𝘁𝘆 𝗮𝗻𝗱 𝗦𝘂𝘀𝘁𝗮𝗶𝗻𝗮𝗯𝗶𝗹𝗶𝘁𝘆 → Can this grow without breaking? Here's what I often see happen. A company finds a great AI opportunity. Everyone gets excited. The business case looks amazing. Then data privacy comes up, or ethical issues. Or whether legal reviewed it. And the room goes quiet. Because nobody checked those things up front. The scorecard doesn't slow things down; it ensures the best ideas surface. The organizations that win with AI aren't moving fastest. They're making the smartest choices upfront. Which of these areas does your organization struggle with most? Let me know in the comments 👇 If you want a high-res of this sheet, let me know in the comments. --- 💡 Share if this helps others ➕ Follow Jason Moccia for more tech and leadership insights

Most companies say they want to “get better at AI.” But what does that actually mean? For anyone trying to move beyond vague ambitions to real, measurable progress— this AI Maturity Model from Hustle Badger and Susannah Belcher is worth bookmarking. It’s more than a framework. It’s a roadmap to becoming an AI-ready organization across strategy, culture, tools, and trust. Here’s how it works: Step 1️⃣ : Diagnose your starting point Rate your organization across 6 categories—like data readiness, governance, and leadership mindset—from Level 1 (Limited) to Level 5 (Best-in-class). Step 2️⃣: Visualize your maturity scorecard Get a snapshot of strengths, gaps, and hidden risk factors (like weak AI governance or untrained teams). Step 3️⃣: Align on what matters This isn’t about maxing every score. It’s about identifying which dimensions actually move the needle for your business and customers. Step 4️⃣: Build your AI development canvas Assign clear owners, define target maturity levels, and create specific actions and timelines to get there. Step 5️⃣: Repeat and evolve Because AI isn’t static—your maturity model shouldn’t be either. 🧠 What I loved most:  This framework creates shared language and accountability around AI. It’s not just a tech team thing—it touches leadership, hiring, operations, and product delivery. Whether you’re early in the journey or already shipping AI-powered products, this model offers a smart way to: ▸ Run internal audits ▸ Create realistic roadmaps ▸ And scale AI capability without chaos 🔗 Worth a read if you're building AI into your org's future: https://lnkd.in/ejVSwmAW 👉 Curious—has your company done an AI maturity assessment yet? What category do you think most teams are underestimating? #AI #ProductBuiding #OrgMaturity

The G7 Toolkit for Artificial Intelligence in the Public Sector, prepared by the OECD.AI and UNESCO, provides a structured framework for guiding governments in the responsible use of AI and aims to balance the opportunities & risks of AI across public services. ✅ a resource for public officials seeking to leverage AI while balancing risks. It emphasizes ethical, human-centric development w/appropriate governance frameworks, transparency,& public trust. ✅ promotes collaborative/flexible strategies to ensure AI's positive societal impact. ✅will influence policy decisions as governments aim to make public sectors more efficient, responsive, & accountable through AI. Key Insights/Recommendations: 𝐆𝐨𝐯𝐞𝐫𝐧𝐚𝐧𝐜𝐞 & 𝐍𝐚𝐭𝐢𝐨𝐧𝐚𝐥 𝐒𝐭𝐫𝐚𝐭𝐞𝐠𝐢𝐞𝐬: ➡️importance of national AI strategies that integrate infrastructure, data governance, & ethical guidelines. ➡️ different G7 countries adopt diverse governance structures—some opt for decentralized governance; others have a single leading institution coordinating AI efforts. 𝐁𝐞𝐧𝐞𝐟𝐢𝐭𝐬 & 𝐂𝐡𝐚𝐥𝐥𝐞𝐧𝐠𝐞𝐬 ➡️ AI can enhance public services, policymaking efficiency, & transparency, but governments to address concerns around security, privacy, bias, & misuse. ➡️ AI usage in areas like healthcare, welfare, & administrative efficiency demonstrates its potential; ethical risks like discrimination or lack of transparency are a challenge. 𝐄𝐭𝐡𝐢𝐜𝐚𝐥 𝐆𝐮𝐢𝐝𝐞𝐥𝐢𝐧𝐞𝐬 & 𝐅𝐫𝐚𝐦𝐞𝐰𝐨𝐫𝐤𝐬 ➡️ focus on human-centric AI development while ensuring fairness, transparency, & privacy. ➡️Some members have adopted additional frameworks like algorithmic transparency standards & impact assessments to govern AI's role in decision-making. 𝐏𝐮𝐛𝐥𝐢𝐜 𝐒𝐞𝐜𝐭𝐨𝐫 𝐈𝐦𝐩𝐥𝐞𝐦𝐞𝐧𝐭𝐚𝐭𝐢𝐨𝐧 ➡️provides a phased roadmap for developing AI solutions—from framing the problem, prototyping, & piloting solutions to scaling up and monitoring their outcomes. ➡️ engagement + stakeholder input is critical throughout this journey to ensure user needs are met & trust is built. 𝐄𝐱𝐚𝐦𝐩𝐥𝐞𝐬 𝐨𝐟 𝐀𝐈 𝐢𝐧 𝐔𝐬𝐞 ➡️Use cases include AI tools in policy drafting, public service automation, & fraud prevention. The UK’s Algorithmic Transparency Recording Standard (ATRS) and Canada's AI impact assessments serve as examples of operational frameworks. 𝐃𝐚𝐭𝐚 & 𝐈𝐧𝐟𝐫𝐚𝐬𝐭𝐫𝐮𝐜𝐭𝐮𝐫𝐞: ➡️G7 members to open up government datasets & ensure interoperability. ➡️Countries are investing in technical infrastructure to support digital transformation, such as shared data centers and cloud platforms. 𝐅𝐮𝐭𝐮𝐫𝐞 𝐎𝐮𝐭𝐥𝐨𝐨𝐤 & 𝐈𝐧𝐭𝐞𝐫𝐧𝐚𝐭𝐢𝐨𝐧𝐚𝐥 𝐂𝐨𝐥𝐥𝐚𝐛𝐨𝐫𝐚𝐭𝐢𝐨𝐧: ➡️ importance of collaboration across G7 members & international bodies like the EU and Global Partnership on Artificial Intelligence (GPAI) to advance responsible AI. ➡️Governments are encouraged to adopt incremental approaches, using pilot projects & regulatory sandboxes to mitigate risks & scale successful initiatives gradually.

𝗠𝗼𝘀𝘁 𝗔𝗜 𝗿𝗲𝗮𝗱𝗶𝗻𝗲𝘀𝘀 𝗮𝘀𝘀𝗲𝘀𝘀𝗺𝗲𝗻𝘁𝘀 𝗹𝗼𝗼𝗸 𝗮𝘁 𝘁𝗲𝗰𝗵𝗻𝗼𝗹𝗼𝗴𝘆. 𝗙𝗲𝘄 𝗹𝗼𝗼𝗸 𝗮𝘁 𝗯𝗲𝗵𝗮𝘃𝗶𝗼𝘂𝗿. Data maturity. Tooling. Infrastructure. Security controls. All important. Yet most AI initiatives stall for a different reason. The organisation was behaviourally unprepared. I’ve written a new blog post: 𝗔𝗜 𝗥𝗲𝗮𝗱𝗶𝗻𝗲𝘀𝘀 𝗙𝗿𝗼𝗺 𝗮 𝗣𝗲𝗼𝗽𝗹𝗲 𝗮𝗻𝗱 𝗖𝗵𝗮𝗻𝗴𝗲 𝗣𝗲𝗿𝘀𝗽𝗲𝗰𝘁𝗶𝘃𝗲: 𝗔 𝗣𝗿𝗮𝗰𝘁𝗶𝗰𝗮𝗹 𝗟𝗲𝗮𝗱𝗲𝗿𝘀𝗵𝗶𝗽 𝗙𝗿𝗮𝗺𝗲𝘄𝗼𝗿𝗸 It sets out five measurable dimensions that determine whether AI becomes an advantage or internal friction: psychological safety, leadership alignment, capability and literacy, governance balance, and change narrative. AI accelerates whatever already exists. If decision rights are unclear, it amplifies confusion. If trust is low, it increases resistance. If governance is immature, it multiplies risk. This article includes a practical AI People Readiness Canvas that leaders can use in executive workshops to diagnose gaps and prioritise action over the next 90 days. No technology audit. A behavioural audit. Written for CIOs, CTOs, HR leaders, transformation leads, and boards serious about making AI sustainable, not performative. Link: https://lnkd.in/eEygRkcF If you assessed your AI readiness beyond technology, what would score lowest? #AIReadiness #Leadership #ChangeManagement #CIO #CTO #AIGovernance #TechnologyLeadership #BusinessTransformation

OpenAI just released a new leadership guide on AI adoption. You’ll see plenty of posts dryly listing the five steps: Align, Activate, Amplify, Accelerate, Govern. You know me. I look for the paradoxes that actually decide whether this works in the real world. Align → Mandate vs motivation The guide celebrates company-wide targets and exec role-modelling. Think “everyone uses ChatGPT every day.” The risk is compliance theatre. People hit quotas without changing how they work. My advice: explain the why in business terms, not tools. Set outcome goals tied to customer, cost, or quality. Share how leaders actually use AI in their own work, not slogans. Activate → Learning vs performance pressure Structured training, champions, hack days, OKRs for AI fluency. Great on paper. The tension is that once it’s in performance reviews, people optimise for looking good, not learning well. My advice: prioritise role-specific workflows over generic “AI 101.” Reward one meaningful workflow upgrade per person per quarter, with before/after evidence. Amplify → Signal vs hype Central hubs, newsletters, internal show-and-tell. Good knowledge hygiene. But amplification can inflate tiny demos into “transformations.” Hype crowds out value. My advice: publish reusable prompts and playbooks with measured impact. Tag wins by difficulty, risk, and repeatability so teams know what to copy and what to ignore. Accelerate → Speed vs stability Fast intake, prioritisation, cross-functional councils, pilot to production. Reality check: data access, security, and procurement are slower than hackathons. My advice: pre-clear a small set of approved tools and patterns. Maintain a simple rubric for value, risk, and readiness. Timebox pilots and decide start/stop/scale on a single page. Govern → Empowerment vs control Lightweight playbooks and “safe to try” rules are the promise. The trap is centralising every decision or writing policies no one can apply. My advice: write policy as checklists people can use in the flow of work. Escalate only when risk triggers fire. Review quarterly so governance keeps pace with reality. The examples are good. The tensions are real. Training lifts fluency when it’s embedded in daily work, not treated as a side quest. Councils unblock delivery only if they own decisions. Idea labs can surface a thousand concepts, but only a handful survive contact with data, risk, and customers. Bottom line Playbooks love neat verbs. Operations live in trade-offs. If you want AI to stick, pair each "A" with its tension and decide in advance how you’ll handle it. That’s how you turn adoption into outcomes. Adoption is cheap. Safety and ROI are not. That’s the difference between theatre and transformation.

🇳🇱 The Dutch government just set the bar for AI Act readiness. Their AI Act Guide (v1.1) is one of the most practical, well-structured, and publicly accessible resources out there—and it’s available for free. 📘 What’s inside Published by the Ministry of Economic Affairs of the Netherlands, this 21-page guide offers a structured walkthrough of the EU AI Act for businesses, developers, and public authorities. It’s designed to help you figure out: ✔️ Whether your system is in scope ✔️ What risk category it falls under ✔️ Whether you’re a provider or deployer ✔️ Which obligations apply The four-step structure is especially useful: Start with risk – not with definitions Check if your system meets the AI Act’s definition of AI Identify your role (provider or deployer) Understand your obligations based on use and risk The guide explains how the AI Act applies to: Prohibited AI practices (e.g. social scoring, predictive policing) High-risk AI systems in sectors like health, education, HR, law enforcement, and critical infrastructure General Purpose AI & Generative AI—including transparency, risk mitigation, and open model exceptions Government obligations, such as Fundamental Rights Impact Assessments and system registration 🔍 It also includes definitions, exceptions, deployment scenarios, and real regulatory references—without legal jargon. 🙌 Kudos to the Dutch Ministry of Economic Affairs for producing such a clear, governance-oriented tool. This is the kind of leadership we need as compliance deadlines approach. Free to download, easy to share, and perfect for onboarding your team. #AIGovernance #EUAIAct #AICompliance #ResponsibleAI #RiskManagement #AIpolicy #OpenAccess === Did you like this post? Connect or Follow 🎯 Jakub Szarmach Want to see all my posts? Ring that 🔔. Sign up for my biweekly newsletter with the latest selection of AI Governance Resources (1.450+ subscribers) 📬.