Chris Ngoi, CFA, CA’s Post

I have recently had the privilege of acting as the Technical Editor on the production of the CIOB AI Playbook, chaired by David Philp. Please see the link for the very insightful and practical document if you are tackling the concept of #AI to deliver benefits. Below are my key points for successful adoption from an organizational perspective rather than technology:   To successfully achieve the benefits of AI, we need to: Ø Determine the why – what is the #purpose, use cases, outcomes and goals for the business that drive the #digital and AI #strategy. Ø Develop the digital and corresponding AI strategy with achievable and measurable milestones. Ø Embed organizational and behavioral #change from the very beginning, with everyone sold on the purpose, and their individual role in achieving the outcome as a unit. Ø Train, educate and develop the organization so that it can fully embed and utilize AI, with mastery, to achieve the #benefits.  Ø Ensure the #data going in is right to achieve the outcomes required, including data strategy, #governance, #quality checks, improving the data where necessary. The output is only ever as good as the input. Ø Identify and integrate the tools to manage and implement the strategy (AI will only be component, albeit critical, of what is required to effectively benefit from a digital strategy) Ø Keep it simple, this requires industry experts not data scientists, however it is recommended you have an AI expert to develop the right solution and change expert to embed it. Ultimately ensuring #sustainable results, is about enabling autonomy and mastery for individuals to collectively be successful. Ø Measure, review, iterate, amend, keep going towards the goal but remain flexible and #agile, everything always changes. Adaptability, flexibility, learning and #resilience is what enables long term success, whilst always maintaining that #goal insight    If you require support in developing and implementing your business, digital or AI strategy Vision into Reality have a team of experts to support with each element in achieving your desired outcomes. https://lnkd.in/eCF8FN7J

🔒 Trust is the Cornerstone of AI Innovation for Boardrooms 🤖 As more and more software companies integrate AI into their product features, it's essential that users have trust. This trust goes deeper than just the output - be it an answer, summary, prediction or something else. It's also the trust in the layers that led to the production of the output. 🌟 Generative and Conversational AI are common additions to software features. They offer impressive capabilities enhancing user experiences, productivity and understanding. However, the real game-changer lies beneath the surface—the data source and how it is transformed into a robust knowledge base. Here’s why this matters: Data Quality: The accuracy of AI responses depends heavily on the quality of the data it uses. Reliable, comprehensive, and up-to-date data is the bedrock of any effective AI system. Knowledge Base Development: Transforming raw data into a structured, meaningful knowledge base ensures that AI systems can provide precise, contextually relevant responses. This process involves meticulous data processing, organization, and continuous updates. Building Trust: Without a solid knowledge base, even the most advanced AI models can produce misleading or incorrect information. The trust users place in AI systems hinges on the integrity and effectiveness of the underlying data and knowledge infrastructure. 🔍 At GOVRN, it took a long time to design, build, test and deploy a Knowledge Base fit for the boardroom. It is the foundation needed to add the different forms of AI models to enhance critical board functions. As we advance in AI technology, don't lose sight of the importance of the foundational elements that support it. Investing in high-quality data and a well-structured Knowledge Base is key to unlocking the true potential of AI and building systems that users can rely on. #AI #TrustInTech #DataQuality #KnowledgeManagement #GenerativeAI #ConversationalAI #TechInnovation

Explainable AI: Building Trust and Clarity in Enterprise Decision-Making As AI becomes central to decision-making in business, Explainable AI (XAI) has evolved into a strategic necessity. Organizations today must ensure that AI-driven decisions are transparent, traceable, and defensible. Deploying AI without explainability introduces risks beyond technical issues - it puts businesses at risk of legal exposure, customer distrust, and reputational damage. Understanding the data driving AI outcomes is crucial. If your AI system recommends denying a loan or prioritizing a medical treatment, you must be able to trace that decision back to the specific data points and variables that influenced it. Without this clarity, even well-intentioned AI solutions can lead to misaligned decisions, regulatory breaches, and ethical concerns. Explainable AI provides the framework to dissect these decisions, offering stakeholders insight into how data shapes AI-driven conclusions. Implementing Explainable AI in Business For enterprises, the first step is identifying which AI models impact high-stakes decisions—those affecting customer interactions, compliance, or financial risk. These are the systems where explainability matters most. Next, businesses should focus on evaluating their data. Leaders must ask two critical questions: Do we understand the data driving these conclusions? and Does the AI's conclusion make sense given our understanding of that data? This practice is fundamental to catching biases, data gaps, or inconsistencies early in the process. To build effective explainability, organizations can leverage tools like SHAP (Shapley Additive Explanations) or LIME (Local Interpretable Model-agnostic Explanations) that translate complex AI models into more understandable insights. These tools provide a clear breakdown of how each data input influences the outcome, making it easier for stakeholders to validate AI decisions against business logic. The Strategic Edge of Explainable AI Explainable AI goes beyond meeting regulatory requirements; it fosters a culture of transparency and trust within your organization. When AI decisions are clear and rational, teams can confidently act on them, and customers are more likely to trust the outcomes. In regulated industries like finance and healthcare, where AI impacts people's lives, explainability also acts as a safeguard against compliance risks. Companies that integrate Explainable AI from the start will lead the way, transforming AI from a mysterious black box into a well-lit path for innovation. #EnterpriseAI #DigitalTransformation #Leadership #ExplainableAI

How Established Companies Can Leverage AI

🔍 Explainable AI: Building Trust and Clarity in Enterprise Decision-Making Michael makes some great points in his article below. Here's my extra take on this important aspect of AI. As AI becomes integral to business decisions, Explainable AI (XAI) is no longer optional—it’s essential. Without explainability, AI poses risks beyond technical challenges: legal exposure, customer distrust, and reputational damage. Key Considerations for Explainable AI: • 🧐 Transparency: AI decisions must be transparent, allowing businesses to trace recommendations (e.g., loan approvals or medical treatments) back to specific data points. • ⚖️ Defensibility: Without explainability, organizations risk misaligned decisions, regulatory issues, and ethical concerns. • 📊 Data Understanding: Leaders must understand the data driving AI conclusions to ensure decisions are unbiased, accurate, and aligned with business objectives. Steps to Implement Explainable AI: 1. Identify high-stakes AI models that impact critical decisions—customer interactions, compliance, and financial risk. 2. Evaluate the data: Ensure you understand what data influences AI outcomes and if the conclusions align with business logic. 3. Use tools like SHAP or LIME to break down complex AI models into understandable insights, validating AI's decisions. 🚀 Strategic Benefits of Explainable AI: • Builds trust with customers and stakeholders 🤝. • Ensures compliance in regulated industries like finance and healthcare. • Fosters a culture of transparency and boosts confidence in AI-driven decisions. Organizations that embrace Explainable AI are positioned to lead, turning AI from a “black box” into a powerful, transparent tool for innovation. #EnterpriseAI #DigitalTransformation #Leadership #ExplainableAI

The future of AI is Agentic Brilliant share by: Armand Ruiz Original post below ⬇️⬇️⬇️ The future of AI is Agentic Let's learn the basics:    𝗗𝗲𝗳𝗶𝗻𝗶𝘁𝗶𝗼𝗻  An AI agent is a system designed to reason through complex problems, create actionable plans, and execute these plans using a suite of tools.    These agents exhibit advanced reasoning capabilities, memory retention, and task execution abilities.    𝗖𝗼𝗺𝗽𝗼𝗻𝗲𝗻𝘁𝘀 𝗼𝗳 𝗔𝗜 𝗔𝗴𝗲𝗻𝘁𝘀  1. Agent Core: The central processing unit that integrates all functionalities.    2. Memory Module: Stores and retrieves information to maintain context and continuity over time.    3. Tools: External resources and APIs the agent can use to perform specific tasks.    4. Planning Module: Analyzes problems and devises strategies to solve them.    𝗖𝗮𝗽𝗮𝗯𝗶𝗹𝗶𝘁𝗶𝗲𝘀  1. Advanced Problem Solving: AI agents can plan and execute complex tasks, such as generating project plans, writing code, running benchmarks, and creating summaries.      2. Self-Reflection and Improvement: AI agents can analyze their own output, identify problems, and provide constructive feedback. By incorporating this feedback and repeating the criticism/rewrite process, agents can continually improve their performance across various tasks, including code production, text writing, and answering questions.    3. Tool Utilization: AI agents can use tools to evaluate their output, such as running unit tests on code to check for correctness or searching the web to verify text accuracy. This allows them to reflect on errors and propose improvements.    4. Collaborative Multi-Agent Framework: Implementing a multi-agent framework, where one agent generates outputs, and another provides constructive criticism, leads to enhanced performance through iterative feedback and discussion.    The business world is not yet ready to fully adopt agents; they are still in the very early experimental stage, and that's why everyone is implementing RAG like crazy, as it is a safer bet.    But believe me, AI agents are where it's at—and at the current pace of innovation, we will soon start collaborating with AI agents instead of humans for some specific tasks. _________ If you've ever asked yourself: How Do I Use AI? Follow for the answers you're looking for.

Organizations racing to implement generative AI solutions overlook a fundamental determinant of success: robust evaluation frameworks. Research and market analysis consistently demonstrate that the differentiator between successful and abandoned GenAI projects isn't primarily the choice of model or technical implementation - it's the rigor of the evaluation process. Leading organizations are reshaping their approach to GenAI evaluation through three fundamental principles: 1. Comprehensive Test Suite Development: Successful enterprises build evaluation frameworks encompassing hundreds of carefully curated test cases. This strategic foundation ensures reliability across diverse use cases and supports scalable implementation. 2. Systematic Error Analysis: While initially resource-intensive, organizations that invest in thorough error analysis gain invaluable insights into application behavior and optimization opportunities. This data-driven approach consistently delivers superior outcomes in enterprise implementations. 3. Evaluation Automation: Through automated testing processes - whether via direct error validation or LLM-as-judge methodologies - organizations can maintain consistent quality standards while scaling their AI initiatives effectively. The implications extend beyond technical implementation. This structured approach to evaluation establishes a framework for measurable improvement and data-informed decision-making across the enterprise. For organizations evaluating their GenAI initiatives, the question isn't just about implementation speed but about building sustainable frameworks for long-term success. Companies must assess their evaluation methodologies against industry best practices and competitive benchmarks. The future belongs to organizations that recognize evaluation isn't a checkpoint but a cornerstone of successful AI transformation. #EnterpriseAI #DigitalTransformation #TechnologyStrategy #AIImplementation #BusinessInnovation #DataDrivenDecisions #CorporateStrategy #TechLeadership

There are 5.56 million companies in the UK market, and we’re still not fully leveraging AI effectively? We see it every single day: 🔹 Businesses struggling to stay competitive 🔹 All because they haven't found the right balance in using AI These are the 3 most common mistakes we see businesses make with AI: 1️⃣ Implementing AI without a clear strategy. 🔄 Instead: Define clear goals: Understand what we want AI to achieve. Align with business objectives: Ensure AI initiatives support our core mission. Prioritize use cases: Focus on areas where AI can have the most impact. 2️⃣ Overlooking the importance of data quality. 🔄 Instead: Invest in data management: Ensure our data is clean and well-structured. Use advanced analytics: Leverage AI tools for deeper data insights. Regularly update data: Keep our data current to enhance AI accuracy. 3️⃣ Ignoring the need for human oversight. 🔄 Instead: Maintain a human-in-the-loop: Combine AI with human judgment. Foster AI literacy: Educate our team on how to interact with AI systems. Focus on ethical AI: Implement guidelines to ensure responsible AI use. If we do this, we can find the perfect balance to harness AI effectively and transform our consulting approach. AI isn't just a trend; it's a catalyst for innovation and competitive advantage. Integrating AI with human expertise is crucial to becoming better consultants. For enhanced results: Embrace continuous learning: Stay updated on AI advancements. Experiment and iterate: Test AI solutions and refine them based on feedback. Collaborate across teams: Ensure our AI strategy involves all key stakeholders. By striking the right balance between AI capabilities and human insight, we can drive meaningful transformation and deliver exceptional consulting services. Join the Conversation: 💬 How are you using AI to enhance your consulting practice? Share your experiences in the comments! At DeRisk360, we specialize in harnessing AI to unlock data-driven insights and elevate consulting practices. #AI #Consulting #DataAnalytics #AIEthics #BusinessStrategy #DeRisk360

🧩 Integrating AI isn't just about technology—it's about change management. 98% of organizations agree that significant support is needed to onboard employees to use AI tools effectively. Let Aidols guide your organization through this critical transition: aidolsgroup.com #ChangeManagement #AIAdoption #OrganizationalReadiness

🌍 𝟯 𝘁𝗶𝗽𝘀 𝗳𝗼𝗿 𝗕𝗼𝗮𝗿𝗱𝘀 𝗮𝗻𝗱 𝗘𝘅𝗲𝗰𝘂𝘁𝗶𝘃𝗲𝘀 - 𝗔𝗜 𝗥𝗶𝘀𝗸 𝗠𝗮𝗻𝗮𝗴𝗲𝗺𝗲𝗻𝘁 1. Define your organisation’s position on the use of AI and establish methods for innovating safely. Questions to ask: 🔸Have we defined our risk appetite for the use of emerging technology, AI and advanced analytics? 🔸Is it within our risk appetite not to use AI if our competitors are? 🔸For which business functions, use cases and data assets are we comfortable using AI models, and with what degree of governance and oversight? 2. Take AI out of the shadows: establish ‘line of sight’ over the AI and advanced analytics solutions that are currently in use across your organisation Questions to ask: 🔸Where might we already be using AI within our organisation? 🔸What are our current AI models being used for, and have we assessed the risks associated with those use cases? 🔸What data is used by the model, and what business functions are supported by the model? 🔸Are we meeting our contractual and regulatory obligations as they relate to the use of data as well as the models themselves? 🔸Is our current use of AI models in alignment with our risk appetite, AI principles and values? 3. Embed ‘compliance by design’ across the AI lifecycle Questions to ask: 🔸Do we have an AI governance framework in place? 🔸Is our AI governance framework practical to follow? How reliant are we on manual controls to remain compliant? 🔸How confidently can I answer the example governance questions for each AI lifecycle phase? 👇How progressed is your organisation on its AI governance journey? 💬 Send me a message to learn more about AI governance and how to implement it in your organisation. ✅ Follow me for insights into #ResponsibleAI and #AIethics best practices, EU AI Act compliance, #AI literacy for #technology #leadership and #management Source: https://lnkd.in/emJG9Uxy