Our Framework For Ai Roi Assessment Artefact

Bonisiwe Shabane

-Dec 27, 2025, 8:30 PM

our framework for ai roi assessment 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

Calculating the return on investment (ROI) for AI initiatives represents one of the most critical yet challenging aspects of digital transformation. While 85% of executives believe AI will give them a competitive advantage, only 23% have successfully measured its actual business impact. This comprehensive framework provides a systematic approach to understanding, calculating, and optimizing AI ROI across your organization. Traditional ROI calculations, designed for tangible assets and linear processes, often fail to capture the full spectrum of AI's impact. Unlike conventional technology investments, AI implementations generate value through multiple channels: direct cost savings, revenue enhancement, risk reduction, and strategic positioning benefits that compound over time. The complexity deepens when considering AI's iterative improvement nature.

Unlike static systems, AI solutions become more valuable as they process more data and learn from interactions. This creates a value curve that accelerates over time,a phenomenon traditional ROI frameworks struggle to accommodate. Furthermore, AI investments often require fundamental changes to business processes, organizational structures, and employee capabilities. These transformation costs and benefits extend far beyond the technology itself, creating a web of interconnected impacts that demand a more sophisticated measurement approach. Understanding AI ROI begins with comprehensively mapping all associated costs across the implementation lifecycle. Our analysis of 200+ AI implementations reveals that organizations typically underestimate total costs by 40-60%, leading to unrealistic ROI expectations and project failures.

Measuring the ROI in AI projects can be difficult, however it's essential for ensuring you're getting the most out of your investments. Here's how we do it. In the blink of an eye, Artificial Intelligence (AI) has shifted from potential to essential; a tool that many companies rely on to improve efficiency, assist with decision-making, and build a competitive advantage. Despite the widespread excitement surrounding AI, many executives struggle with a fundamental question: how do we know if our AI investments are delivering the revenue impact we expect? Determining and tracking return on investment (ROI) for AI initiatives is critical to ensure that these technologies are delivering tangible value. Without this clarity, AI projects risk becoming expensive experiments rather than transformative assets.

Why is measuring AI ROI crucial? What are the challenges in measuring the ROI of AI projects? How can you decide on measures to help you track the ROI of these investments? Let’s dive in and figure it out. AI initiatives represent significant investments in terms of time, resources, and money. Sometimes, it’s easy to measure the return delivered: intelligent or dynamic pricing projects produce projections of increased sales and revenue that are easily verified, for example.

Other times, AI projects may enhance existing processes or products in less direct ways, making it more difficult to determine the value returned. In my early years at Amazon Web Services (AWS), I created a tool for building cloud business cases that went beyond measuring just total cost of ownership and now forms the basis of our... I later co-authored the Cloud Value Framework (CVF) which focusses on measuring cloud value across four areas: cost optimisation, risk reduction, increased agility, and resource efficiency. So it should come as no surprise that I often get asked by Boards and the executives I meet “How do we decide if we should make an AI investment and how do we... Traditional financial metrics like Return on Investment (ROI), Internal Rate of Return (IRR), Net Present Value (NPV), and payback period provide a starting point for measuring value. However, these are all lagging indicators - they measure value only after it has been created.

While boards regularly make strategic decisions under uncertainty based on their risk appetite, AI has low barriers to entry and provides an opportunity to foster an experimental culture that can help organisations build confidence... The current AI landscape offers numerous opportunities for low-cost, low-friction pilot projects that can demonstrate value quickly. For example, using generative AI services like Amazon Q for specific business functions, implementing document processing automation for a single department, or testing AI-powered customer service tools in a limited support queue. These pilots can deliver quick wins with minimal upfront investment, show tangible benefits within days rather than years, and provide the evidence needed to support larger strategic investments. AI initiatives create value in uniquely powerful ways. They can enhance decision-making and customer experience across multiple business functions simultaneously.

Their benefits often compound over time as systems learn and improve. And perhaps most importantly, they can fundamentally transform how work gets done, opening up entirely new possibilities for innovation and growth. When it comes to measuring this value creation, boards and executives can easily get lost in a sea of metrics - from technical performance indicators to total cost of ownership calculations. But in my experience working with boards across multiple industries, there are really only five things they consistently care about: can we innovate and enter new markets, are we delivering value to our customers,... As an early adopter in the AI market, you’re at the forefront of innovation, ready to harness the transformative power of artificial intelligence. But with great potential comes great responsibility – especially when it comes to justifying your AI investments.

Let’s dive into a comprehensive framework that will help you estimate and maximize the ROI of your AI initiatives. The 360-Degree View: Embracing the Full Spectrum of AI Value Imagine a kaleidoscope of possibilities, where each turn reveals new facets of AI’s potential. That’s what our 360-degree framework offers – a holistic view that captures both tangible and intangible benefits across eight primary assessment categories. As an early adopter, you’re not just implementing AI – you’re paving the way for widespread acceptance. Here’s how this framework helps you cross the chasm:

By embracing this 360-degree framework, you’re not just estimating ROI – you’re crafting a roadmap for AI-driven transformation. Here’s how you can leverage your early adopter status: For all the buzz about AI’s potential to transform business, many organizations struggle to ascertain the extent to which their AI implementations are actually working. Part of this is because AI doesn’t just replace a task or automate a process — rather, it changes how work itself happens, often in ways that are hard to quantify. Measuring that impact means deciding what return really means, and how to connect new forms of digital labor to traditional business outcomes. “Like everyone else in the world right now, we’re figuring it out as we go,” says Agustina Branz, senior marketing manager at Source86.

That trial-and-error approach is what defines the current conversation about AI ROI. To help shed light on measuring the value of AI, we spoke to several tech leaders about how their organizations are learning to gauge performance in this area — from simple benchmarks against human... 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 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. Interesting framework. How do you decide which AI initiatives to prioritize for both short- and long-term ROI?

Venture capital continues to flow into AI-driven drug discovery, as shown by Flagship Pioneering’s $50 million launch of Expedition Medicines, which was announced in October 2025. Despite these large-scale investments in generative AI, many enterprises face the challenge of results not expectations, often due to fragmented and unstructured data rather than shortcomings in algorithms. A key takeaway is that the return on advanced AI investment is achieved only when foundational data pipelines are prioritized. Recent industry experiences have shown that even the most advanced AI models fail to deliver when data remains siloed across business units or is locked in legacy formats. Addressing this requires the design of a strategic, scalable data pipeline to: • Cleanse and normalize disparate data sources • Unify structured and unstructured datasets • Ensure ongoing data quality and accessibility • Enable... With this in place, AI initiatives can drive innovation, streamline critical operations, and deliver measurable business impact.

#AI #DataStrategy #DigitalTransformation #PharmaInnovation #EnterpriseTech #ASF 🇪🇺 Apply AI Strategy: a strategic milestone for Europe’s digital future The European Commission has released the Apply AI Strategy, a comprehensive plan to embed Artificial Intelligence across key sectors—from healthcare and manufacturing to... 📘 The strategy positions AI not just as a tool, but as strategic infrastructure—a production factor that can reshape how industries operate and how public services are delivered. As an AI and Digital innovation strategy professional, I consider this a strong signal: - A systemic vision that aligns innovation with European values. - A commitment to open, interoperable, and trustworthy AI ecosystems. - A clear push toward skills, governance, and scalable adoption.

⚠️ Of course, there are challenges we must face. In a fast-evolving landscape, ensuring responsible and effective AI adoption—especially for SMEs, which are the backbone of the Italian and European economy—requires practical tools, clear guidance, and shared frameworks. This strategy opens the door. Now we must walk through it—together. 📎 Full document available in the attached PDF. #AIstrategy #DigitalLeadership #ArtificialIntelligence #AIfirst #Innovation #SMEs #EuropeanInnovation #PolicyThinking #StrategicVision

Our Framework For Ai Roi Assessment Artefact

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