Tag Archives: ArtificialIntelligence

EU AI Act´s General-Purpose AI Models (GPAI) Rules Are Live: How to prove Compliance next months.

Posted on by
Reply

EU obligations for general-purpose AI kicked in on 2 Aug 2025. Models already on the market before 2 Aug 2024, must be fully compliant by 2 Aug 2027 – but boards won’t wait that long.

Over the past few weeks I’ve sat with product, legal, and model teams that felt “compliance-ready” … until we opened the evidence drawer. That’s where most programs stall. The good news: the playbook is clear now. GPAI Code of Practice (10 Jul 2025) gives a pragmatic path, and the Guidelines for GPAI Providers (31 Jul 2025) remove a lot of scope ambiguity. Voluntary? Yes. But it’s the fastest way to show your house is in order while standards mature.

Here’s how I’d tackle this —no drama, just discipline. First, align on who you are in the Act (provider vs. deployer). Then make one leader accountable per model and wire compliance into your release process.

My advice, Companies should:

  • Gap-assess every in-scope model against the Code. Do you have a copyright policy, a training-data summary, documented evals, and a working view of downstream disclosures? If any of those are fuzzy, you’re not ready.
  • Stand up model cards and incident logs; add release gates that block launch without evidence. Map risks to your cyber program using CSF 2.0 so Security and Audit can speak the same language.
  • Run an internal GPAI evidence audit. Publish an exec dashboard with: % of models with complete technical files and disclosures, incident MTTD/MTTR, and time-to-close regulator/customer info requests.

A quick reality check: big providers are splitting—some signalling they’ll sign the Code, others not. That’s strategy. Your advantage (especially if you’re an SME) is disciplined documentation that turns “we promise” into procurement-ready proof.

My rule of the thumb: if the CEO can’t see weekly movements on documentation completeness and incident handling, you are in pilot land – no matter how advanced the model sounds.

What would you put on a one-page dashboard to convince your CFO – and your largest EU customer – that your GPAI program in truly under control?

Agentic Operating Models: from Pilots to P&L

Posted on by
Reply

We’re past the demo phase. Boards are asking a harder question: how do human-plus-agent workflows show up in cash flow—this quarter? There is a clear answer: The winners don’t “add an agent”; they redesign the work. That means owners, SLAs, guardrails, and value tracking—weekly. Not glamorous, just effective.

Here’s the short playbook I’d bring to the next ExCo:

  • Make Agents products. Name a product owner, publish SLAs (latency, accuracy, human-override rate), and set chargeback so value—and cost—land in the P&L.
  • Design human+agent flow, end-to-end. Pilots fail for organizational reasons. Tie every pilot to a customer metric and a service level from day one.
  • Build guardrails you can audit. Map risks to NIST’s Cyber AI Profile; log decisions, provenance, and incidents. “Trust” that isn’t evidenced will stall at Legal.

Does it pay?  Signals are real but uneven. A European bank modernization program cut 35-70% cycle time with reusable “agent components.” In KYC/AML, agent “factories” show 200-2000% productivity potential when humans supervise at scale. Klarna’s AI assistant handles  ~1.3M monthly interactions (~800 FTEs) with CSAT parity. Yet BCG says only ~5% are truly at value-at-scale, and Gartner warns ~40% of agentic projects could be scrapped by 2027. Operating model discipline determines who wins.

If I had 90 days:

  • 30: Inventory top 5 agent candidates; assign owners; baseline SLAs and override rates.
  • 60: Stand up an Agent Review Board (CIO/CDO/GC/CISO); add release gates and rollback.
  • 90: Ship two agents to production; publish a value dashboard (savings, cycle time, SLA hit rate) and decide scale/retire.

A candid note on risk: labor anxiety and model drift will erase ROI if we skip change management and runtime oversight. Bring HR and the 2nd line in early, and rehearse incidents like you would a cyber tabletop.

If we can’t show weekly value, SLA adherence, and audit-ready evidence, we’re still in pilot land—no matter how advanced the model sounds.

What would make your CFO believe – tomorrow – that an agent belongs on the P&L?

Strategy to Capitalize on Generative AI in Business

Featured

Posted on by
Reply

The integration of Generative AI (GenAI) in businesses presents both challenges and opportunities. This article outlines strategies for deploying GenAI, ensuring compliance, managing risks, and facilitating monetization in a rapidly evolving technological environment.

A.- Understanding GenAI Challenges

Key obstacles to GenAI integration include:

  • Lack of incentives: Without apparent benefits, employees might resist new AI tools.
  • Ignorance of AI’s potential: Misunderstanding what AI can do often leads to its underuse.
  • Fear of job displacement: Concerns about AI replacing jobs or empowering junior employees can cause resistance.
  • Restrictive policies: Conservative approaches may stifle AI adoption, pushing employees to seek alternatives outside the organization.

B.- Strategic Integration of GenAI

  • Identify High-Value Applications: Target roles and processes where GenAI can boost efficiency, such as data analysis and customer service, ensuring immediate impact and wider acceptance.
  • Educate and Incentivize Employees: Develop training programs coupled with incentives to foster AI adoption and proficiency.
  • Risks and Contingency Planning: Assess and manage technological, regulatory, and organizational risks with proactive safeguards and strategic planning for potential issues.
  • Incremental Implementation: Start with pilot projects offering high returns, which can be expanded later, showcasing their effectiveness and ROI.

C.- Monetization Strategies

  • Enhance Productivity: Apply GenAI to automate routine tasks and enhance complex decision-making, freeing up resources for more strategic tasks, thereby reducing costs and improving output quality.
  • Develop New Products and Services: Utilize GenAI to create innovative products or enhance existing ones, opening up new revenue streams like AI-driven analytics services.
  • Improve Customer Engagement: Deploy GenAI tools like chatbots or personalized recommendation systems to boost customer interaction and satisfaction, potentially increasing retention and sales.
  • Optimize Resource Management: Use GenAI to predict demand trends, optimize supply chains, and manage resources efficiently, reducing waste and lowering operational costs.

D.- Conclusion

Successfully integrating and monetizing GenAI involves overcoming resistance, managing risks, and strategically deploying AI to boost productivity, drive innovation, and enhance customer engagement. By thoughtfully addressing these issues, companies can thrive in the era of rapid AI evolution.

The EU AI Act: An Overview

Featured

Posted on by
Reply

Set to take effect in stages starting summer 2024, the AI Act is poised to become the world’s first comprehensive AI law. It aims to govern the use and impact of AI technologies across the EU, affecting a broad range of stakeholders including AI providers, deployers, importers, and distributors.
🔹𝐊𝐞𝐲 𝐏𝐫𝐨𝐯𝐢𝐬𝐢𝐨𝐧𝐬 & 𝐈𝐦𝐩𝐚𝐜𝐭: The Act categorizes AI systems into prohibited, high-risk, and general-purpose models, each with specific compliance requirements. Notably, high-risk AI systems face stringent obligations, impacting sectors from employment to public services. The Act also introduces bans on certain AI practices deemed harmful, like emotion recognition in workplaces or untargeted image scraping for facial recognition.
🔹𝐂𝐨𝐦𝐩𝐥𝐢𝐚𝐧𝐜𝐞 & 𝐏𝐞𝐧𝐚𝐥𝐭𝐢𝐞𝐬: Compliance will vary by the nature of AI usage with penalties for non-compliance reaching up to €35 million or 7% of annual worldwide turnover. The AI Act also incorporates and aligns with existing EU regulations like GDPR, requiring businesses to assess both new and existing legal frameworks.
🔹𝐓𝐢𝐦𝐞𝐥𝐢𝐧𝐞 𝐟𝐨𝐫 𝐈𝐦𝐩𝐥𝐞𝐦𝐞𝐧𝐭𝐚𝐭𝐢𝐨𝐧: The AI Act will phase in its provisions, with most obligations impacting businesses after a two-year period post-law enactment.
🔹𝐒𝐭𝐫𝐚𝐭𝐞𝐠𝐢𝐜 𝐂𝐨𝐧𝐬𝐢𝐝𝐞𝐫𝐚𝐭𝐢𝐨𝐧𝐬: Entities involved in AI need to develop robust governance frameworks early to align with the Act’s requirements. As AI technologies and legal standards evolve, staying informed and adaptable is crucial.
🔹𝐆𝐥𝐨𝐛𝐚𝐥 𝐏𝐞𝐫𝐬𝐩𝐞𝐜𝐭𝐢𝐯𝐞: Unlike the EU’s comprehensive approach, the UK is currently opting for a non-binding, principles-based framework for AI regulation. This divergence highlights varying international stances on AI governance.
For businesses and professionals involved in AI, the incoming AI Act represents both a challenge and an opportunity to lead in responsible AI deployment and innovation.

More on: https://bit.ly/4bN8gM0

𝐹𝑜𝑙𝑙𝑜𝑤 𝑚𝑒 𝑜𝑛 𝑋: @𝑀𝑖𝑔𝑢𝑒𝑙𝐶ℎ𝑎𝑚𝑜𝑐ℎ𝑖𝑛

EU Sets Global Precedent with Comprehensive AI Act

Featured

Posted on by
Reply

The European Union has just reached a landmark agreement on a comprehensive AI law, poised to set a global precedent. This new regulation, known as the AI Act, is one of the first of its kind and aims to manage the rapidly evolving AI technology with a risk-based approach.

Key highlights of the AI Act include:

  • Risk-Based Regulation: The AI Act will categorize AI systems based on their level of risk, with the most stringent regulations applied to high-risk models. This includes popular large AI models like ChatGPT.
  • Enforcement Across EU: All 27 member states will be involved in enforcing the law, with certain aspects taking up to 24 months to become effective.
  • Global Impact: The legislation is expected to influence AI development worldwide, serving as a model for other countries.
  • Comprehensive Prohibitions: The AI Act will ban AI use for social scoring, manipulating human behavior, and exploiting vulnerable groups. Strict restrictions are also placed on facial recognition technology and AI systems in the workplace and educational institutions.
  • Significant Fines for Non-Compliance: Companies that fail to comply with these new rules could face fines of up to €35 million or 7% of global revenue.
  • Two-Tier Approach for AI Models: The Act establishes transparency requirements for general-purpose AI models and stronger requirements for those with systemic impacts.
  • Encouragement for Innovation: Despite strict regulations, the Act aims to avoid excessive burdens on companies, promoting a balance between safeguarding AI technology use and encouraging innovation.
  • Future Perspectives: Looking ahead, this legislation is a crucial step in shaping the global AI regulatory landscape, with implications for AI legislation and automated decision-making rules in other jurisdictions, including Canada, the United States, and beyond.

The EU AI Act is much more than just a set of rules; it’s a catalyst for EU startups and researchers to lead in the global AI race. With this act, the EU becomes the first continent to establish clear rules for AI use, potentially guiding future global standards in AI regulation.

More on: https://bit.ly/486f0n3