University of Kwazulu-Natal

Our Framework For Ai Roi Assessment Artefact Artefact

Bonisiwe Shabane
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our framework for ai roi assessment artefact artefact

In our first article, we established why traditional ROI models fail to capture AI’s unique value dynamics—non-linear returns, delayed benefits, and contextual dependencies. Building on this foundation, we present a structured evaluation framework that enables organizations to quantify AI’s impact across three interconnected tiers: industry context, implementation costs, and multi-horizon benefits. The industry you are operating within heavily influences the expected topline of the AI use-cases you plan to launch. This first gate relies on 3 criteria: the regulatory forces & compliance costs of the industry, the maturity of its specific tech ecosystem and the short to long termism investment culture within. Every AI initiative operates within sector-specific regulatory boundaries that directly shape ROI potential. Let’s take the example of the access to prescription data from healthcare professionals

In our first article, we established why traditional ROI models fail to capture AI’s unique value dynamics—non-linear returns, delayed benefits, and contextual dependencies. Building on this foundation, we present a structured evaluation framework that enables organizations to quantify AI’s impact across three interconnected tiers: industry context, implementation costs, and multi-horizon benefits. The industry you are operating within heavily influences the expected top-line of the AI use-cases you plan to launch. This first gate relies on 3 criteria: the regulatory forces & compliance costs of the industry, the maturity of its specific tech ecosystem and the short to longtermism investment culture within. Every AI initiative operates within sector-specific regulatory boundaries that directly shape ROI potential. Let’s take the example of the access to prescription data from healthcare professionals

As a consequence, the ROI of using prescription data to target healthcare professionals is strong in the US, average in Brazil and limited in Europe, where most of the time, the data is aggregated... [Artefact Article] Rethinking AI ROI: A Framework for Real-World Impact Traditional ROI models often fail to capture AI’s unique value — from non-linear returns to delayed and context-dependent benefits. In this new article, Dr. Christoph Gross, Partner at Artefact, introduces a structured framework to assess AI ROI across three interconnected layers: 🔹 Industry context – how regulation, ecosystem maturity, and long-term investment culture shape feasible AI use cases. 🔹 Enterprise implementation costs – the role of tech stack readiness, data governance, and adoption dynamics. 🔹 Multi-horizon benefits – from short-term topline growth and task automation to long-term strategic decision-making and organizational resilience.

This framework provides leaders with a more realistic, comprehensive way to evaluate AI’s impact—well beyond static cost-benefit analysis. Read the full article here 👉 https://lnkd.in/ey3R2aBr Elevating Trust in AI Agents: Why Observability Is the Real Differentiator In today’s AI-driven world, building an agent is no longer the hardest part. With foundation models and orchestration frameworks readily available, the true challenge lies in ensuring those agents remain transparent, reliable, and accountable once deployed at scale. As a Solution Architect, I believe trust is the currency of enterprise AI adoption—and observability is the engine that powers it. When organizations deploy AI agents without visibility, several risks emerge: blind decision paths, hidden failures, and operational surprises.

Without accountability, trust erodes. Without traceability, debugging becomes guesswork. And without transparency, leaders hesitate to scale adoption. This is why observability must move from “nice-to-have” to architectural baseline. Amazon Bedrock’s AgentCore Observability provides a compelling blueprint. It enables end-to-end telemetry—capturing decisions, tool calls, token usage, and reasoning flows.

It is framework- and model-agnostic, so enterprises avoid fragmentation even as they diversify models or deployment environments. It also balances automatic instrumentation for speed with custom instrumentation for business relevance, allowing architects to track not just performance but meaningful attributes like customer_type or environment. Equally critical is the GenAI Observability dashboard in CloudWatch. By visualizing latency, error rates, and trace spans in one pane, leaders can turn complex AI behaviors into actionable insights. Observability stops being about “logs” and becomes about continuous intelligence. But architecture leadership means going beyond tools.

It means institutionalizing practices: Bake observability into every design document and review. Instrument agents from day one—not post-production. Define business-aligned metrics, not just technical KPIs. Govern for privacy, ensuring observability never leaks sensitive data. Make observability reviews a recurring leadership ritual, using insights to refine both agents and business outcomes. The payoff is significant: faster detection and resolution, improved stakeholder trust, lower operational overhead, and resilient AI systems that scale predictably.

In my view, the winners in this next wave won’t be those with the largest models, but those with the most observable, trustworthy, and governable AI agents. Navigating the AI Landscape: A Strategic Approach for Financial Sectors ####################### As I observe the rapidly evolving AI landscape, one thing is clear: innovation is happening at an unprecedented pace. AI players—OEMs and System Integrators (SIs)—are in a state of experimentation and exploration, launching new capabilities daily. While this is exciting for tech enthusiasts, it poses a challenge for enterprise customers, especially in the "Financial Sector", who need to make informed decisions about long-term solutions. The Perils of Being an Experimental User ------------------------- Enterprise customers often take time to select the right solutions, and rightly so. In the AI space, where technologies are still maturing, it's crucial to avoid becoming experimental users.

The risks are twofold: firstly, investing time and resources in solutions that may not deliver expected results; secondly, potential disruptions to business operations. Leveraging Standard AI Tools for Employees Productivity Improvement and Growth ------------------------- That being said, there are opportunities to leverage basic AI tools to drive employee productivity and business growth. Solutions like Copilot can be effective in streamlining processes, enhancing employees experiences, and gaining insights from data. These tools can be quickly integrated into existing workflows and apps, providing tangible benefits while more advanced capabilities are developed. Building In-House Solutions for Advanced Capabilities --------------------------- For more advanced AI capabilities, such as agentic AI, new machine learning algorithms using by (e.g.. LLaMA, GPT, Mistral, Gemini and etc), and specialized models, developing in-house solutions can be a strategic imperative.

This approach offers several benefits: - Control and Customization: In-house solutions can be tailored to meet specific business needs, ensuring seamless integration with existing systems and processes. - Guardrails and Governance: By developing solutions in-house, organizations can implement robust guardrails and governance frameworks, ensuring AI systems operate within defined boundaries. - Intellectual Property: In-house development allows organizations to retain ownership of their IP, reducing dependence on third-party vendors. Key Considerations ------------------------------ - Adopt a Crawl-Walk-Run approach: Start with basic AI tools and gradually move to more advanced capabilities. - Develop a robust governance framework: Establish clear guidelines and guardrails for AI development and deployment. - Invest in talent and upskilling: Build a team with the necessary skills to develop and deploy AI solutions.

- Partner strategically: Collaborate with AI players who can provide valuable expertise and support. #InnovationStrategy #AIGovernance #InHouseAI #FutureOfWork Claude Haiku 4.5 is redefining AI efficiency with blazing speed, near-frontier intelligence, and unmatched cost performance—setting a new standard for real-time, scalable AI systems. Developed by Anthropic as part of the Claude 4.5 family, Haiku 4.5 delivers up to 5x faster inference than Sonnet 4.5 while achieving 90% of its coding and reasoning quality—at just $1/million input tokens... 🔑 Key Advantages • Extended Thinking Modes: Adaptive reasoning—short (chat), interleaved (tool use), deep (logic)—for dynamic task handling. • Multi-Agent Orchestration: Run multiple Haiku instances in parallel under Sonnet’s strategic guidance—ideal for automation pipelines.

• Enterprise-Grade Safety: Rated AI Safety Level 2 (ASL-2)—safe for production in regulated environments. • Cost Efficiency: One of the most affordable high-performance models—perfect for startups and high-volume use cases. • Broad Deployment: Available on Claude API, Amazon Bedrock, and Google Vertex AI—seamless integration into existing infra. 🚀 Use Cases Coding Assistants: Real-time syntax feedback, doc generation, CI/CD integration. Customer Support: Instant response bots that scale during peak traffic. Agentic Workflows: Automated incident response, testing, and data triage.

Research & Triage: Fast classification with optional deep analysis. Productivity Tools: Lightweight AI for daily workflows. 📊 Performance Leadership SWE-bench Verified: Excels in code editing and debugging. Terminal-Bench: High efficiency in CLI and system tasks. OSWorld & MMMLU: Strong in multi-language reasoning and tool use. Safety Alignment: Improved over predecessors—ideal for sensitive deployments.

🛠️ Implementation Strategy Start with Haiku: Use as default for chat, summarization, and light reasoning. Orchestrate with Sonnet: Let Sonnet plan, Haiku execute—optimize speed and depth. Adjust Thinking Budgets: Use deep mode only when needed—balance cost and latency. Deploy via Cloud Platforms: Leverage Bedrock or Vertex AI for scalability and monitoring. Monitor & Iterate: Track routing, safety, and performance—refine with human feedback. ✅ Best Practices ✔ Use Haiku as the “first responder” in AI pipelines ✔ Escalate complex tasks to higher-tier models ✔ Fine-tune extended thinking thresholds ✔ Audit safety logs—especially in regulated industries How is...

Share below. 👇 #ClaudeHaiku45 #ClaudeAI #Anthropic Read the full article here: https://lnkd.in/dtp_bFXM Scattered pilots and quick wins won’t deliver sustainable impact; success requires AI to be woven into core operations. Poor governance and low data maturity are the main barriers to scaling AI rather than the algorithms themselves. https://lnkd.in/gnr2CPWu [📄 Artefact blog | Our framework for #AI ROI assessment] by Dr.

Dr. Christoph S Gross, Partner at Artefact. Dr. Christoph Gross is Partner at Artefact and leads our Zurich office. With a doctorate from ETH Zurich and research at Harvard Medical School, he combines scientific rigour with strategic insight to drive #AItransformation, particularly across Pharma & Life Sciences in German-speaking Europe. 👉 Read the full article here: https://lnkd.in/ey3R2aBr 💡 How can organisations truly measure the #ROI of AI when its returns are non-linear, context-dependent, and evolve across multiple time horizons?

The article presents a holistic approach to assess #AIROI across three interconnected tiers: 1️⃣ Industry context: How regulatory constraints, ecosystem maturity, and long-term planning cultures shape AI feasibility and returns. For example, prescription #data use in healthcare has high ROI potential in the US, medium in Brazil, and limited in Europe. 2️⃣ Enterprise implementation costs: Why tech stack readiness, #datagovernance maturity, and #employeeadoption capacity define how quickly #AIusecases scale from pilot to value. 3️⃣ Multi-horizon benefits: From short-term gains (e.g., Netflix’s recommendations increasing engagement by 30%) to long-term strategic decision superiority and organisational resilience. 🎯“AI ROI cannot be assessed like traditional investments. Its true value lies in its compounding effects across time, business processes, and decision-making agility.” At Artefact, we believe AI investments require a new strategic lens to guide prioritisation and scale responsibly.

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