Everything AI

The Agentics Co.

The Agentics Co. (Agentics) is a boutique consulting firm that leverages an innovative AI-native transformation approach to assist its clients in achieving incremental growth through innovation, disruption, and bridging the gap between strategy and execution. For details: https://TheAgentics.Co/ Traditional consulting delivers decks. We deliver AI-native growth engines, built to move fast, scale smart, and run on their own with its services: • Everything AI (AI prototyping, MVPs and Integration) • AI-native Transformation Consulting • Enterprise-wide Technology Implementation + AI Products

  1. 6 uur geleden

    The Enterprise AI Pilot-to-Production Playbook 2026 – By The Agentics

    The podcast argues that the biggest challenge in enterprise AI is no longer building pilots, it’s getting them into production. While AI capabilities have advanced rapidly, the majority of enterprise AI agent pilots never generate measurable business value because organisations underestimate what it takes to operationalise AI at scale.     The Core Problem: The podcast highlights a stark reality: Most organisations can successfully build AI proofs of concept.Only a small percentage successfully deploy those systems into day-to-day business operations.The gap is rarely caused by AI performance, it is caused by enterprise execution.Pilots typically demonstrate technical feasibility, but production environments demand reliability, governance, integration, scalability, security, cost control, and business ownership.   Why Pilots Fail The playbook identifies several recurring reasons why enterprise AI initiatives stall: AI projects are launched without clearly defined business outcomes.Data quality and enterprise context are insufficient.AI is layered onto existing processes instead of redesigning them.Governance is treated as a compliance exercise rather than an architectural capability.Ownership between business and IT is unclear.Organisations optimise individual use cases instead of transforming end-to-end workflows.The result is an accumulation of disconnected AI experiments that create demonstrations rather than measurable enterprise value.   The Six Design Constraints The playbook proposes six fundamental design principles that distinguish successful production deployments from failed pilots: 1.      Start with measurable business outcomes, not AI technology. 2.      Design for enterprise integration, ensuring agents work across existing systems rather than as isolated applications. 3.      Build governance into the architecture, including permissions, auditability, and human oversight. 4.      Treat data as a strategic asset, giving AI access to high-quality, contextual enterprise information. 5.      Engineer for scale and operational resilience, including monitoring, observability, security, and cost management. 6.      Drive organisational adoption, recognising that people, processes, and operating models are as important as technology. Validation Before Scale One of the podcast’s strongest recommendations is a Validation-First approach. Rather than attempting enterprise-wide deployment immediately, organisations should: validate on real business data,prove measurable ROI,establish governance,refine operational processes,and then expand incrementally.This reduces risk while creating executive confidence and a repeatable implementation model.   The Role of AI-Native Architecture The podcast argues that enterprises should move beyond deploying isolated copilots or task-specific agents and instead build AI-native operating models. This involves: shared enterprise context,multi-agent orchestration,semantic understanding of business data,governed execution,and a common execution platform capable of serving multiple departments.Instead of dozens of disconnected AI solutions, organisations should establish a single governed AI execution layer supporting Finance, HR, Procurement, Operations, Sales, Compliance, and Customer Service.   Key Takeaways The podcast concludes that moving from pilot to production is not primarily a technology challenge, it is an enterprise transformation challenge. Successful organisations: prioritise business outcomes over demonstrations,embed governance from day one,validate before scaling,redesign business processes around AI,and build a shared enterprise AI platform rather than isolated departmental solutions. To know more: https://theagentics.co/insights/the-pilot-to-production-playbook

    The Enterprise AI Pilot-to-Production Playbook 2026 – By The Agentics
  2. 2 dgn geleden

    EU AI Act 2026: AI Governance & Compliance Field Guide - By The Agentics

    The podcast argues that the EU AI Act is not simply another compliance regulation; it represents a fundamental shift in how enterprises design, deploy, and govern AI systems. Organisations that continue to treat governance as a legal or documentation exercise will struggle to scale AI, while those that embed governance into their AI architecture will gain a competitive advantage.   The podcast explains that the Act introduces a risk-based regulatory framework, with the most stringent obligations applying to high-risk AI systems. For many enterprises, particularly those deploying AI in finance, healthcare, HR, manufacturing, critical infrastructure, and regulated industries, compliance requires much more than policies, it requires technical controls that continuously govern AI behaviour.   A central message is that governance must operate at the same speed as AI. Traditional governance approaches based on policies, annual audits, or manual reviews are insufficient for autonomous agents making thousands of decisions every day. Instead, governance must become an operational capability that enforces permissions, monitors actions in real time, maintains immutable audit trails, and ensures human oversight where required.   The podcast presents five foundational pillars of enterprise AI governance: Clearly defined permission boundariesComprehensive audit trailsFine-grained data access controlsHuman escalation and oversight mechanismsContinuous mapping of AI behaviour to regulatory obligationsTogether, these pillars create a governance framework that is scalable, auditable, and capable of supporting production-grade AI deployments.   The podcast also recommends a four-layer governance architecture spanning business ownership, operational controls, technical enforcement, and regulatory compliance. Rather than placing responsibility solely within IT or legal teams, governance should be shared across executives, business leaders, risk functions, and engineering teams.   Another major theme is the transition from governance-as-documentation to Policy-as-Code. Instead of relying on static policy documents, governance rules should be encoded into software, version controlled, automatically enforced, and continuously validated. This allows AI systems to prevent non-compliant actions before they occur while producing audit-ready evidence automatically.   The podcast warns against three common governance anti-patterns: Building AI first and adding governance later.Depending solely on manual reviews and audits.Treating governance as a compliance checkbox rather than core infrastructure.These approaches increase operational risk, regulatory exposure, and the cost of scaling AI across the enterprise.   Finally, the podcast concludes that successful enterprise AI programmes share one defining characteristic: governance is designed into the architecture from day one. Organisations that embed governance, traceability, human oversight, and compliance into their AI platforms will be better positioned to scale AI safely, satisfy regulators, build stakeholder trust, and realise measurable business value under the EU AI Act and future AI regulations.   To know more: https://theagentics.co/insights/ai-governance-the-eu-ai-act-2026---a-field-guide

    EU AI Act 2026: AI Governance & Compliance Field Guide - By The Agentics
  3. 23 jul

    Agentic AI Layers for U.S. Bundling Compliance Management

    U.S. bundling compliance has become significantly more complex as enterprises move toward personalized pricing, subscription models, omnichannel commerce, and AI-driven selling. While ERP systems such as SAP, Oracle, Microsoft Dynamics, and NetSuite remain the system of record, they were never designed to continuously interpret evolving regulations, validate bundle eligibility, or monitor compliance across thousands of transactions in real time. As a result, organizations increasingly rely on fragmented spreadsheets, custom rules, and manual reviews, creating operational bottlenecks, audit risks, and inconsistent customer experiences.   The podcast argues that the answer is not replacing the ERP, but deploying an Agentic AI intelligence layer above it. This layer combines specialized AI agents with enterprise governance to continuously monitor products, pricing, contracts, customer eligibility, promotional policies, and regulatory requirements before transactions are committed to the ERP. Rather than acting as another workflow tool, the Agentic AI layer becomes an intelligent compliance and decision engine that orchestrates complex bundling scenarios while maintaining full auditability. Key capabilities include: Real-time bundle validation against antitrust, contractual, healthcare, tax, and industry-specific regulations.Continuous monitoring of pricing, discounts, rebates, and promotional campaigns before they create compliance exposure.Cross-functional orchestration across Sales, Legal, Finance, Product Management, and Compliance rather than isolated departmental reviews.Explainable AI decisions with evidence-backed recommendations, complete audit trails, and human approvals for high-risk exceptions.Autonomous policy updates, enabling the system to adapt as regulations or internal commercial policies evolve.The podcast positions Agentic AI as a governance and execution layer, rather than simply another AI assistant. Multiple specialized agents collaborate to interpret regulations, analyze contracts, validate pricing structures, assess customer eligibility, identify exceptions, and recommend compliant alternatives before transactions reach the ERP. This enables enterprises to automate routine compliance decisions while escalating only genuinely complex cases to human experts. From a business perspective, the architecture delivers measurable outcomes: Reduced compliance risk and regulatory exposure.Faster approval cycles for complex commercial bundles.Lower dependence on manual compliance reviews.Consistent application of pricing and bundling policies across channels.Improved audit readiness through complete traceability of every AI recommendation and business decision.Greater commercial agility without compromising governance.The broader strategic message is that ERP systems should remain the enterprise’s trusted system of record, while Agentic AI becomes the system of intelligence and governed decision-making. Instead of embedding increasingly complex compliance logic directly into ERP customizations, organizations can introduce a reusable AI orchestration layer that works across multiple enterprise applications, continuously reasons over changing regulations, and ensures every bundle offered to customers is commercially optimized, operationally efficient, and legally compliant. This architecture represents a practical path toward AI-native enterprise operations without requiring costly ERP replacement programmes.  To read / download the complete paper: https://theagentics.co/insights/agentic-ai-layer-inside-the-erp-for-u.s.-bundling-compliance-problem by The Agentics

    Agentic AI Layers for U.S. Bundling Compliance Management
  4. 30 jun

    The Enterprise Agentic AI Landscape 2026 - By The Agentics

    The Enterprise Agentic AI Landscape 2026 by The Agentics Enterprise AI in 2026 is no longer a question of whether organisations adopt agents. It's whether they can make agents safe, measurable and business-relevant at scale. Across every major 2025–2026 enterprise AI study from Deloitte, Celonis, Massachusetts Institute of Technology, Digital Commerce 360, one pattern is identical: "Adoption has outrun Readiness." Some of the numbers from the research: → 74% of enterprises expect to use AI agents at least moderately by 2027. Only 21% have a mature governance model for autonomous agents. (Deloitte) → 85% aspire to become an "agentic enterprise" within 2–3 years. But 60% say they cannot adapt operations fast enough to realise ROI. (Celonis) → 82% of leaders say AI only delivers ROI if it understands how the business actually runs. 45% struggle to give AI that business context. → 84% of companies have not redesigned a single role around AI capabilities. → 95% of GenAI pilots delivered no measurable P&L impact in 2025. (MIT) The gap isn't model capability. It's execution architecture i.e. Governance, Data quality, Process context, IT-business alignment and Operating-model redesign. The organisations reporting AI-driven P&L gains in 2026 are not the ones with better models. They're the ones that stopped running new pilots and fixed their data, process and governance foundations first. We've just published our full Enterprise Agentic AI Landscape 2026 Analysis, synthesising the leading enterprise AI research with our own POV on Validation-First delivery, governed execution and orchestration over single agents. With named patterns by segment (B2B vs B2C, mid-market vs enterprise) and the 5 failure modes that recur across every stalled programme we audit. Read the full landscape ↓ #EnterpriseAI #AgenticAI #AIGovernance #ValidationFirst #Cortex

    The Enterprise Agentic AI Landscape 2026 - By The Agentics
  5. 20 apr

    Agentic AI for Autonomous Supply Chain and Logistics

    "$17.96B → $707.75B by 2034" → That's the AI-in-logistics market, compounding at 44.4% a year. And in the Middle East & Africa, it's growing even faster → 50.2% CAGR, the fastest of any region globally. But the real story isn't the market size. It's that the technology behind it is no longer a dashboard or a predictive model. It's intelligence that acts. In our latest insight, we break down how Agentic AI is already transforming 11 operating domains across Supply Chain, Shipping & Logistics, drawn from live enterprise deployments with clients across Europe and the Middle East: → Customs processing: 2–4 hours → under 90 seconds → Transport orchestration: 8–15% cost reduction, planner productivity up 2.5–4x → Fleet maintenance: 25–40% less unplanned downtime → Freight procurement: 100% invoice audit coverage (vs. the usual 15–20% sample) → Warehousing: 20–40% uplift in pick productivity with no new infrastructure → Legacy modernisation: zero operational disruption across a $10.7B, 40-country operation → ESG compliance: 90% cost reduction, CSRD + CDP + #GRI + #EcoVadis from a single data input. The ROI case is no longer speculative. McKinsey confirms 5-15% logistics cost reductions and 20-30% inventory reductions for companies deploying AI at scale.The question is no longer whether to deploy Agentic AI in supply chain. It's how quickly and with whom. Read the article 👇 https://theagentics.co/insights/agentic-ai-for-autonomous-supply-chain-shipping-and-logistics #AgenticAI #SupplyChain #Logistics #Shipping #AITransformation #MultiAgentSystems #ESG #CSRD #EnterpriseAI #Freight #EU #TheAgentics #Ecoratings #AINative #Sustainability

    Agentic AI for Autonomous Supply Chain and Logistics

Info

The Agentics Co. (Agentics) is a boutique consulting firm that leverages an innovative AI-native transformation approach to assist its clients in achieving incremental growth through innovation, disruption, and bridging the gap between strategy and execution. For details: https://TheAgentics.Co/ Traditional consulting delivers decks. We deliver AI-native growth engines, built to move fast, scale smart, and run on their own with its services: • Everything AI (AI prototyping, MVPs and Integration) • AI-native Transformation Consulting • Enterprise-wide Technology Implementation + AI Products

Suggesties voor jou