How to Use AI Responsibly

Check out this massive global research study into the use of generative AI involving over 48,000 people in 47 countries - excellent work by KPMG and the University of Melbourne! Key findings: 𝗖𝘂𝗿𝗿𝗲𝗻𝘁 𝗚𝗲𝗻 𝗔𝗜 𝗔𝗱𝗼𝗽𝘁𝗶𝗼𝗻 - 58% of employees intentionally use AI regularly at work (31% weekly/daily) - General-purpose generative AI tools are most common (73% of AI users) - 70% use free public AI tools vs. 42% using employer-provided options - Only 41% of organizations have any policy on generative AI use 𝗧𝗵𝗲 𝗛𝗶𝗱𝗱𝗲𝗻 𝗥𝗶𝘀𝗸 𝗟𝗮𝗻𝗱𝘀𝗰𝗮𝗽𝗲 - 50% of employees admit uploading sensitive company data to public AI - 57% avoid revealing when they use AI or present AI content as their own - 66% rely on AI outputs without critical evaluation - 56% report making mistakes due to AI use 𝗕𝗲𝗻𝗲𝗳𝗶𝘁𝘀 𝘃𝘀. 𝗖𝗼𝗻𝗰𝗲𝗿𝗻𝘀 - Most report performance benefits: efficiency, quality, innovation - But AI creates mixed impacts on workload, stress, and human collaboration - Half use AI instead of collaborating with colleagues - 40% sometimes feel they cannot complete work without AI help 𝗧𝗵𝗲 𝗚𝗼𝘃𝗲𝗿𝗻𝗮𝗻𝗰𝗲 𝗚𝗮𝗽 - Only half of organizations offer AI training or responsible use policies - 55% feel adequate safeguards exist for responsible AI use - AI literacy is the strongest predictor of both use and critical engagement 𝗚𝗹𝗼𝗯𝗮𝗹 𝗜𝗻𝘀𝗶𝗴𝗵𝘁𝘀 - Countries like India, China, and Nigeria lead global AI adoption - Emerging economies report higher rates of AI literacy (64% vs. 46%) 𝗖𝗿𝗶𝘁𝗶𝗰𝗮𝗹 𝗤𝘂𝗲𝘀𝘁𝗶𝗼𝗻𝘀 𝗳𝗼𝗿 𝗟𝗲𝗮𝗱𝗲𝗿𝘀 - Do you have clear policies on appropriate generative AI use? - How are you supporting transparent disclosure of AI use? - What safeguards exist to prevent sensitive data leakage to public AI tools? - Are you providing adequate training on responsible AI use? - How do you balance AI efficiency with maintaining human collaboration? 𝗔𝗰𝘁𝗶𝗼𝗻 𝗜𝘁𝗲𝗺𝘀 𝗳𝗼𝗿 𝗢𝗿𝗴𝗮𝗻𝗶𝘇𝗮𝘁𝗶𝗼𝗻𝘀 - Develop clear generative AI policies and governance frameworks - Invest in AI literacy training focusing on responsible use - Create psychological safety for transparent AI use disclosure - Implement monitoring systems for sensitive data protection - Proactively design workflows that preserve human connection and collaboration 𝗔𝗰𝘁𝗶𝗼𝗻 𝗜𝘁𝗲𝗺𝘀 𝗳𝗼𝗿 𝗜𝗻𝗱𝗶𝘃𝗶𝗱𝘂𝗮𝗹𝘀 - Critically evaluate all AI outputs before using them - Be transparent about your AI tool usage - Learn your organization's AI policies and follow them (if they exist!) - Balance AI efficiency with maintaining your unique human skills You can find the full report here: https://lnkd.in/emvjQnxa All of this is a heavy focus for me within Advisory (AI literacy/fluency, AI policies, responsible & effective use, etc.). Let me know if you'd like to connect and discuss. 🙏 #GenerativeAI #WorkplaceTrends #AIGovernance #DigitalTransformation

"five building blocks — conceptual and technical infrastructure — needed to operationalize responsible AI ... 1. People: Empower your experts Responsible AI goals are best served by multidisciplinary teams that contain varied domain, technical, and social expertise. Rather than seeking "unicorn" hires with all dimensions of expertise, organizations should build interdisciplinary teams, ensure inclusive hiring practices, and strategically decide where RAI work is housed — i.e., whether it is centralized, distributed, or a hybrid. Embedding RAI into the organizational fabric and ensuring practitioners are sufficiently supported and influential is critical to developing stable team structures and fostering strong engagement among internal and external stakeholders. 2. Priorities: Thoughtfully triage work For responsible AI practices to be implemented effectively, teams need to clearly define the scope of this work, which can be anchored in both regulatory obligations and ethical commitments. Teams will need to prioritize across factors like risk severity, stakeholder concerns, internal capacity, and long-term impact. As technological and business pressures evolve, ensuring strategic alignment with leadership, organizational culture, and team incentives is crucial to sustaining investment in responsible practices over time. 3. Processes: Establish structures for governance Organizations need structured governance mechanisms that move beyond ad-hoc efforts to tackle emerging issues posed in the development or adoption of AI. These include standardized risk management approaches, clear internal decision-making guidance, and checks and balances to align incentives across disparate business functions. 4. Platforms: Invest in responsibility infrastructure To scale responsible practices, organizations will be well-served by investing in foundational technical and procedural infrastructure, including centralized documentation management systems, AI evaluation tools, off-the-shelf mitigation methods for common harms and failure modes, and post-deployment monitoring platforms. Shared taxonomies and consistent definitions can support cross-team alignment, while functional documentation systems make responsible AI work internally discoverable, accessible, and actionable. 5. Progress: Track efforts holistically Sustaining support for and improving responsible AI practices requires teams to diligently measure and communicate the impact of related efforts. Tailored metrics and indicators can be used to help justify resources and promote internal accountability. Organizational and topical maturity models can also guide incremental improvement and institutionalization of responsible practices; meaningful transparency initiatives can help foster stakeholder trust and democratic engagement in AI governance." Miranda Bogen, Kevin Bankston, Ruchika Joshi, Beba Cibralic, PhD, Center for Democracy & Technology, Leverhulme Centre for the Future of Intelligence

Most companies have an AI policy. Few have one that actually stops sensitive data leakage and protects the company. A policy that says "use AI responsibly" is not a policy. It's a wish. Here are 10 things your responsible AI policy needs: 𝟭/ 𝗔𝗽𝗽𝗿𝗼𝘃𝗲𝗱 𝗧𝗼𝗼𝗹𝘀 𝗟𝗶𝘀𝘁 Name specific tools employees can use. If it's not on the list, it's not approved. Update quarterly. Specify by department. 𝟮/ 𝗗𝗮𝘁𝗮 𝗖𝗹𝗮𝘀𝘀𝗶𝗳𝗶𝗰𝗮𝘁𝗶𝗼𝗻 𝗥𝘂𝗹𝗲𝘀 Mirror your existing classification scheme: → Public: Any approved tool → Internal: Enterprise agreements only → Confidential: Approved enterprise tools with protections enabled → Restricted (PII, PHI, PCI): Never enters external AI systems 𝟯/ 𝗛𝘂𝗺𝗮𝗻 𝗥𝗲𝘃𝗶𝗲𝘄 𝗥𝗲𝗾𝘂𝗶𝗿𝗲𝗺𝗲𝗻𝘁𝘀 Define where humans stay in the loop: customer-facing content, legal docs, financial decisions, hiring, ethical edge cases. AI drafts. Humans approve. AI never has final authority over decisions affecting someone's rights, pay, or employment. 𝟰/ 𝗗𝗶𝘀𝗰𝗹𝗼𝘀𝘂𝗿𝗲 𝗦𝘁𝗮𝗻𝗱𝗮𝗿𝗱𝘀 Decide when you'll disclose AI involvement. Default: disclose when AI was materially relied upon in regulated or customer-impacting contexts. 𝟱/ 𝗜𝗣 𝗮𝗻𝗱 𝗖𝗼𝗻𝗳𝗶𝗱𝗲𝗻𝘁𝗶𝗮𝗹𝗶𝘁𝘆 Clarify what can't go into prompts. Who owns AI-generated content? What if trade secrets enter a public model? 𝟲/ 𝗕𝗶𝗮𝘀 𝗚𝘂𝗮𝗿𝗱𝗿𝗮𝗶𝗹𝘀 Make bias controls use-case based: hiring, credit/pricing, claims/approvals, targeting that could create discriminatory outcomes. Define who signs off. 𝟳/ 𝗜𝗻𝗰𝗶𝗱𝗲𝗻𝘁 𝗥𝗲𝗽𝗼𝗿𝘁𝗶𝗻𝗴 When AI goes wrong: who to contact, what to document, how fast to report, what triggers escalation. 𝟴/ 𝗧𝗿𝗮𝗶𝗻𝗶𝗻𝗴 𝗥𝗲𝗾𝘂𝗶𝗿𝗲𝗺𝗲𝗻𝘁𝘀 A policy nobody understands is a policy nobody follows. Mandatory training before access. Role-specific guidance. Annual refreshers. 𝟵/ 𝗚𝗼𝘃𝗲𝗿𝗻𝗮𝗻𝗰𝗲 Someone has to own this: who maintains the policy, approves tools, audits compliance, and how often it's reviewed. 𝟭𝟬/ 𝗔𝘂𝗱𝗶𝘁 𝗮𝗻𝗱 𝗘𝗻𝗳𝗼𝗿𝗰𝗲𝗺𝗲𝗻𝘁 Policies fail at the enforcement layer. Define: access controls, logging, periodic spot checks, and consequences (coaching → access removal → HR escalation). Companies that skip policy work now will spend 10x more cleaning up problems later. Save this for when you create or update your AI policy.

AI Integration and Safeguards How Intelligence Is Applied Responsibly Preo Communications integrates AI as an operational layer that enhances judgment, speed, and accuracy without introducing unnecessary risk. The objective is controlled leverage, not automation for its own sake. Where AI Is Applied AI is used in areas where it meaningfully improves outcomes. Common applications include: Pattern detection in analytics and attribution Forecasting and scenario modeling Audience segmentation and personalization Content optimization and performance analysis Workflow automation and efficiency gains AI supports teams by surfacing insight faster and reducing manual overhead. Human Led Decision Making AI informs decisions, it does not make them. Strategic direction, prioritization, and brand judgment remain human-led. AI outputs are treated as inputs to evaluation rather than instructions to follow without context. This prevents over-optimization and protects brand integrity. Data Quality and Input Control AI performance depends on data discipline. Inputs are carefully selected, cleaned, and structured to avoid bias, leakage, or misleading conclusions. Models are adjusted as data sources change to maintain reliability over time. Guardrails and Testing AI systems are introduced incrementally. Each application is tested in controlled environments before being expanded. Performance thresholds, review checkpoints, and rollback options are defined in advance to limit downside risk. Transparency and Traceability Outputs must be explainable. AI-driven insights are documented and traceable so teams understand why a recommendation exists and how it was generated. This maintains trust and supports better decision-making. Why AI Governance Matters Unstructured AI adoption increases volatility and risk. Governance ensures that efficiency gains do not come at the cost of accuracy, compliance, or strategic clarity. AI becomes valuable when it is embedded into well-designed systems with clear ownership and oversight. By applying AI deliberately and responsibly, Preo Communications enhances performance while preserving control, consistency, and long-term resilience.

Fostering Responsible AI Use in Your Organization: A Blueprint for Ethical Innovation (here's a blueprint for responsible innovation) I always say your AI should be your ethical agent. In other words... You don't need to compromise ethics for innovation. Here's my (tried and tested) 7-step formula: 1. Establish Clear AI Ethics Guidelines ↳ Develop a comprehensive AI ethics policy ↳ Align it with your company values and industry standards ↳ Example: "Our AI must prioritize user privacy and data security" 2. Create an AI Ethics Committee ↳ Form a diverse team to oversee AI initiatives ↳ Include members from various departments and backgrounds ↳ Role: Review AI projects for ethical concerns and compliance 3. Implement Bias Detection and Mitigation ↳ Use tools to identify potential biases in AI systems ↳ Regularly audit AI outputs for fairness ↳ Action: Retrain models if biases are detected 4. Prioritize Transparency ↳ Clearly communicate how AI is used in your products/services ↳ Explain AI-driven decisions to affected stakeholders ↳ Principle: "No black box AI" - ensure explainability 5. Invest in AI Literacy Training ↳ Educate all employees on AI basics and ethical considerations ↳ Provide role-specific training on responsible AI use ↳ Goal: Create a culture of AI awareness and responsibility 6. Establish a Robust Data Governance Framework ↳ Implement strict data privacy and security measures ↳ Ensure compliance with regulations like GDPR, CCPA ↳ Practice: Regular data audits and access controls 7. Encourage Ethical Innovation ↳ Reward projects that demonstrate responsible AI use ↳ Include ethical considerations in AI project evaluations ↳ Motto: "Innovation with Integrity" Optimize your AI → Innovate responsibly

Adopting the latest technology alone won’t build an effective AI roadmap. Leaders need a thoughtful approach—one that empowers their teams and stays true to their values. Over the past few years, we’ve seen AI’s incredible potential, but also its complexity. Crafting effective AI strategies can challenge even the most seasoned tech leaders. To truly unlock AI’s value, we need to put people at the core of our roadmap. At RingCentral, we’ve made it a priority to envision AI in ways that benefit our teams, partners, and customers. Here are a few strategies my team has found essential for building human-centered AI: 1. Emphasize the “why” behind AI adoption: Start by identifying the specific needs AI will address. Help your team see the value of AI as a tool to enhance their work—not replace it. 2. Start with small, targeted wins: Choose use cases that tackle real challenges and show early success. These wins build trust in AI’s potential and create momentum for further adoption. 3. Prioritize transparency and ethics: Set clear guidelines around data privacy and responsible AI use, ensuring that team members feel they’re part of an ethical and trusted process. Guiding AI adoption with a clear, people-first approach enables us to create a workplace where innovation truly serves the people behind it, paving the way for meaningful growth. 💡 How are you approaching AI within your teams?

4 AI Governance Frameworks To build trust and confidence in AI. In this post, I’m sharing takeaways from leading firms' research on how organisations can unlock value from AI while managing its risks. As leaders, it’s no longer about whether we implement AI, but how we do it responsibly, strategically, and at scale. ➜ Deloitte’s Roadmap for Strategic AI Governance From Harvard Law School’s Forum on Corporate Governance, Deloitte outlines a structured, board-level approach to AI oversight: 🔹 Clarify roles between the board, management, and committees for AI oversight. 🔹 Embed AI into enterprise risk management processes—not just tech governance. 🔹 Balance innovation with accountability by focusing on cross-functional governance. 🔹 Build a dynamic AI policy framework that adapts with evolving risks and regulations. ➜ Gartner’s AI Ethics Priorities Gartner outlines what organisations must do to build trust in AI systems and avoid reputational harm: 🔹 Create an AI-specific ethics policy—don’t rely solely on general codes of conduct. 🔹 Establish internal AI ethics boards to guide development and deployment. 🔹 Measure and monitor AI outcomes to ensure fairness, explainability, and accountability. 🔹 Embed AI ethics into product lifecycle—from design to deployment. ➜ McKinsey’s Safe and Fast GenAI Deployment Model McKinsey emphasises building robust governance structures that enable speed and safety: 🔹 Establish cross-functional steering groups to coordinate AI efforts. 🔹 Implement tiered controls for risk, especially in regulated sectors. 🔹 Develop AI Guidelines and policies to guide enterprise-wide responsible use. 🔹 Train all stakeholders—not just developers—to manage risks. ➜ PwC’s AI Lifecycle Governance Framework PwC highlights how leaders can unlock AI’s potential while minimising risk and ensuring alignment with business goals: 🔹 Define your organisation’s position on the use of AI and establish methods for innovating safely 🔹 Take AI out of the shadows: establish ‘line of sight’ over the AI and advanced analytics solutions  🔹 Embed ‘compliance by design’ across the AI lifecycle. Achieving success with AI goes beyond just adopting it. It requires strong leadership, effective governance, and trust. I hope these insights give you enough starting points to lead meaningful discussions and foster responsible innovation within your organisation. 💬 What are the biggest hurdles you face with AI governance? I’d be interested to hear your thoughts.

𝗘𝘃𝗲𝗿𝘆 𝗱𝗮𝘆, 𝗼𝗿𝗴𝗮𝗻𝗶𝘇𝗮𝘁𝗶𝗼𝗻𝘀 𝗿𝗮𝗰𝗲 𝘁𝗼 𝗶𝗺𝗽𝗹𝗲𝗺𝗲𝗻𝘁 𝗔𝗜 𝘀𝗼𝗹𝘂𝘁𝗶𝗼𝗻𝘀 𝗯𝘂𝘁 𝗵𝗼𝘄 𝗺𝗮𝗻𝘆 𝗿𝗲𝗮𝗹𝗶𝘇𝗲 𝘁𝗵𝗲𝘆’𝗿𝗲 𝗱𝗲𝗽𝗹𝗼𝘆𝗶𝗻𝗴 𝘁𝗶𝗰𝗸𝗶𝗻𝗴 𝘁𝗶𝗺𝗲 𝗯𝗼𝗺𝗯𝘀? Why should it matter? Without robust governance, AI can amplify risks that can destroy trust, harm individuals and invite costly penalties. The Sołtysiński Kawecki & Szlęzak's whitepaper reveals key realities: • High-risk AI in hiring, credit & law enforcement faces strict EU regulations. • Prohibited practices: subliminal manipulation, social scoring, exploitative biometrics. • Limited-risk AI must clearly disclose AI-generated content. 𝗔𝗹𝗮𝗿𝗺𝗶𝗻𝗴 𝗥𝗶𝘀𝗸𝘀 𝗢𝗿𝗴𝗮𝗻𝗶𝘇𝗮𝘁𝗶𝗼𝗻𝘀 𝗙𝗮𝗰𝗲 𝗧𝗼𝗱𝗮𝘆 • Ethical & Societal: Bias, opaque decisions, environmental harm • Operational: Unpredictable models, hallucinations, bad data • Reputational: Eroded trust, social media backlash • Security & Privacy: Attacks, data misuse, re-identification 𝗧𝗵𝗲 𝗣𝗮𝘁𝗵 𝘁𝗼 𝗥𝗲𝘀𝗽𝗼𝗻𝘀𝗶𝗯𝗹𝗲 𝗔𝗜: 𝗞𝗲𝘆 𝗥𝗲𝗰𝗼𝗺𝗺𝗲𝗻𝗱𝗮𝘁𝗶𝗼𝗻𝘀 ✅ Appoint an AI Champion to lead governance ✅ Build a culture of AI literacy for all employees ✅ Ensure clear transparency in how AI makes decisions ✅ Embed strong technical safeguards to prevent misuse ✅ Maintain meaningful human oversight of high-impact AI decisions ✅ Conduct regular bias and fairness assessments ✅ Draft a simple, actionable internal AI policy aligned with the AI Act 𝗘𝘅𝗮𝗺𝗽𝗹𝗲 𝗜𝗻 𝗧𝗵𝗲 𝗪𝗶𝗹𝗱 Microsoft applies its own Responsible AI Standard and AETHER Committee reviews, ensuring ethical development and deployment of AI across products like Azure OpenAI and M365 Copilot. 𝗕𝗼𝘁𝘁𝗼𝗺 𝗟𝗶𝗻𝗲 Only responsible governance can turn AI from a risk multiplier into a force for inclusive progress by embedding ethics, fairness and resilience into every system. Prof. Dr. Ingrid Vasiliu-Feltes|Helen Yu|JOY CASE|Hr Dr. Takahisa Karita|Antonio Grasso|Nicolas Babin |Alberto Espinosa Machado|Dr. Ram Kumar|Phillip J Mostert| Sara Simmonds |Anthony Rochand|Prasanna Lohar|Shalini Rao #AIForGood #EthicalAI #AICompliance #ResponsibleAI #DigitalTrust #AIGovernance #TechForGood #DigitalEquity #InclusiveInnovation

AI is changing the world at an incredible pace, but with this power comes big questions about ethics and responsibility. As software engineers, we’re in a unique position to influence how AI evolves and that means we have a responsibility to make sure it’s used wisely and ethically. Why ethics in AI matters? AI has the potential to improve lives, but it can also create risks if not managed carefully. From privacy issues to bias in decision-making, there are a lot of areas where things can go wrong if we’re not careful. That’s why building AI responsibly isn’t just a ‘nice-to-have’; it’s essential for sustainable tech. IMO, here’s how engineers can drive positive change: Understand Bias and Fairness AI often mirrors the data it's trained on, so if there’s bias in the data, it’ll show up in the results. Engineers can lead by checking for fairness and ensuring diverse data sources. Focus on Transparency Building AI that explains its decisions in a way users understand can reduce mistrust. When people can see why an AI made a choice, it’s easier to ensure accountability. Privacy by Design With personal data at the core of many AI models, making privacy a priority from day one helps protect user rights. We can design systems that only use what’s truly necessary and protect data by default. Encourage Open Dialogue Engaging in discussions about AI ethics within your team and community can spark new ideas and solutions. Bringing ethical considerations into the coding process is a win for everyone. Keep Learning The ethical landscape around AI is constantly evolving. Engineers who stay informed about ethical guidelines, frameworks, and real-world impacts will be better equipped to design responsibly. Ultimately, responsible AI isn’t about limiting innovation, it's about creating solutions that are inclusive, fair, and safe. As we push forward, let’s remember: “Tech is only as good as the care and thought behind it.” P.S. What do you think are the biggest ethical challenges in AI today? Let’s hear your thoughts!

�� Using AI? Here’s Why You Must Understand Your Data Source AI is a game-changer, but with great power comes great responsibility—especially when it comes to data. Many AI tools deliver incredible results, but if you don’t know where your data is sourced from, you’re setting yourself up for potential trouble. Here’s why: 🛡️ Data Integrity Matters: AI is only as good as the data it’s trained on. If the source data is biased, outdated, or incorrect, the output could mislead your decision-making. 🔒 Protect Your Intellectual Property: Some AI tools use open-source models or datasets. If you’re feeding sensitive, proprietary information into these tools without understanding how it’s used, you might inadvertently expose your intellectual property. 🏛️ Compliance Is Critical: Industries like finance, healthcare, and law require strict adherence to data privacy regulations. Using AI without knowing the data lineage can lead to non-compliance, fines, or worse. How to Protect Yourself and Maximize AI’s Potential: 1️⃣ Ask Questions: Before using an AI tool, ask how it sources, stores, and processes data. Transparency is key. 2️⃣ Use Closed Systems for Proprietary Data: When dealing with sensitive information, consider using AI solutions that allow for closed-loop systems to keep your data secure. 3️⃣ Validate the Output: Don’t rely solely on AI-generated insights. Cross-check results with trusted sources to ensure accuracy. 4️⃣ Train Your Team: Ensure your team understands the risks and best practices for using AI tools responsibly. AI is a fantastic tool, but it’s not a “set it and forget it” solution. Success requires thoughtful implementation, informed decisions, and a clear understanding of the technology. 💬 What’s your approach to ensuring AI outputs are reliable and compliant? Let’s discuss! #AI #DataIntegrity #DigitalTransformation #ArtificialIntelligence #AICompliance #TechLeadership #BusinessInnovation #AIEthics