When it comes to AI, it often feels like there’s more smoke than fire. Gartner’s hype cycle makes this painfully clear. We see waves of inflated expectations – generative AI, foundation models, synthetic data – and then the inevitable slide into disillusionment.
For businesses, this hype creates a real problem. Leaders don’t need buzzwords, they need clarity. They want to know which AI use cases can genuinely bring value and which will waste resources. In our conversations with clients, we often hear the same frustration: “Where do we even start?”
That’s exactly why we built the AI Opportunity Framework – a framework to declutter the noise and make AI choices more grounded.
Our AI Opportunity Framework forces you to ask structured questions:
Does this application save us time today?
Could it reshape our competitive edge tomorrow?
Does it improve how we work internally, or does it transform the customer experience?
These are simple questions, but they prevent expensive mistakes.
We’ve seen this in practice. A healthcare provider once considered rolling out a generative AI tool for patient communication. On the surface, it sounded modern and customer-friendly. But after mapping it against their framework, they realized that internal clinical documentation automation would save far more time and create higher impact in the short term. The framework shifted their focus to a less glamorous, but far more valuable use case.
AI Opportunity Framework
Once you agree on the need for a framework, the AI Opportunity Framework gives you a clear visual map of where applications sit. It divides use cases into four quadrants, across two axes:
- Everyday AI vs. Game-Changing AI
- Internal Operations vs. External Usage
This structure helps leaders see the full landscape and map out opportunities more logically:
⬅️ Everyday AI: small wins like automating invoices, handling basic customer queries, customer chat, or back-office support.
➡️ Game-Changing AI: bold moves like dynamic pricing, predictive analytics, or AI-enhanced R&D.
⬇️ Internal Operations: back office efficiency – finance, HR, compliance, supply chain.
⬆️ External Usage: customer-facing improvements – chatbots, marketing, product features, new services.
When companies put their AI projects on this radar, two things usually happen. First, they see gaps: areas of opportunity they hadn’t considered. Second, they see imbalances: maybe they’re investing only in flashy customer applications, while ignoring internal productivity gains.
For instance, in financial services, we see Everyday AI in Internal Ops through automated compliance checks, saving legal teams hours per week. Meanwhile, a retailer might pursue Game-Changing AI in External Usage with personalized product recommendations that lift conversion rates by double digits.
AI Adoption Matrix 2025
Now that we’ve outlined the AI Opportunity Framework, the next step is to see how it translates into practice. The market today offers plenty of signals about where companies are focusing, which quadrants are already crowded, and which ones still carry untapped potential. Let’s look at each part of the radar in detail and examine the AI Adoption Matrix in 2025.
Everyday AI + External Usage
Customer-facing Everyday AI has become standard in many industries. Chatbots, multilingual virtual agents, and automated marketing campaigns are now baseline expectations. They may not differentiate a business, but they help keep costs down and response times fast. In the front office – sales, CX, marketing, customer service – these tools are less about dazzling innovation and more about keeping the lights on. They ensure basic queries are handled instantly, campaigns are personalized at scale, and support teams are not drowning in repetitive work.
The AI adoption radar gives us a sense of where companies really are. Chatbots and virtual agents show the highest uptake – nearly every major consumer-facing brand now runs some version of them, even if quality varies. Personalized campaigns also rank high, especially in retail and eCommerce, where customer engagement depends on relevance. By contrast, customer analysis lags behind with only medium AI adoption. This gap signals that while businesses are comfortable automating surface-level interactions, they’re still slower to invest in the deeper data work that could make those interactions smarter.
Airlines, for instance, are increasingly relying on AI chatbots to handle flight changes, seat upgrades, or loyalty-point inquiries. One European carrier reported that over 60% of rescheduling requests are now handled entirely by AI. That freed their human staff to resolve complex cases, improving customer satisfaction where it actually mattered.
Everyday AI + Internal Operations
This quadrant is where the AI adoption trend is furthest along. It may not grab headlines, but it delivers real and measurable efficiency. Everyday AI in back-office functions now covers automated processing, contract and compliance checks, knowledge management, and day-to-day task automation. These are the areas where companies already see steady returns without needing experimental pilots or years of R&D.
Looking at adoption scores, the picture is clear: automated processing, knowledge management, and daily task automation are all rated “high”. Businesses have moved quickly to implement them because the benefits are immediate and tangible – faster invoice handling, fewer manual data errors, quicker HR workflows. Compliance and contract AI adoption trend is still “medium”, reflecting the fact that regulation and legal nuance make automation more complex. But even there, momentum is building as tools become more sophisticated.
A global logistics firm, for example, applied AI to invoice matching and error detection. Billing mistakes fell by more than half, and staff time shifted from fixing issues to planning new routes and capacity. This wasn’t a flashy AI “breakthrough” but a down-to-earth win – exactly the kind of improvement that justifies early investment. Companies that focus here often find that unglamorous efficiency gains add up faster than expected, freeing capital and talent for larger transformation projects.
Game-Changing AI + External Usage
This quadrant represents the frontier. Here, AI doesn’t just make processes smoother – it reshapes how a business earns revenue. In the radar, three big themes dominate: AI-powered product recommendations, dynamic pricing, and AI-driven digital advisors. These are not incremental tools but strategic levers, capable of redefining how companies compete and capture value.
Looking at adoption scores, it’s clear that we’re still early. Dynamic pricing shows the strongest uptake (medium adoption), led by industries like retail, travel, and eCommerce where margins are tight and demand fluctuates rapidly. AI product recommendations and AI-powered advisors remain a low adoption trend, signaling their complexity and the higher barriers to entry. That said, even at lower AI adoption, these technologies punch above their weight. A well-designed product recommendation system can lift conversion rates by double digits, while AI-powered advisors could open entirely new revenue streams in sectors like wealth management or insurance.
An eCommerce platform piloted AI-based dynamic pricing across thousands of SKUs. Instead of static pricing updated weekly, the system adjusted prices in real time. The outcome wasn’t just more sales volume – it also preserved margins, creating a rare “win-win” for both top line and profitability. This kind of application shows why external game-changing AI attracts so much attention, even if it remains complex to scale. For leaders, the takeaway is simple: while adoption levels are uneven, this is the quadrant where bold bets have the potential to pay off most dramatically.
Game-Changing AI + Internal Operations
This is the quadrant where organisations start to transform how they run. It’s not about small wins but structural change. On the radar, we see loan processing, operations optimisation, analytics & predictions, and offer processing as the main AI use cases. These are deep, core functions – the kind that can redefine an organisation’s efficiency and resilience if implemented well.
The adoption scores tell an interesting story. Loan processing and analytics & predictions both show medium adoption. This suggests industries like banking and supply chain management are already testing these tools, with some moving into production. Operations optimisation also sits at medium, reflecting its cross-industry appeal but also the technical challenge of integrating it with legacy systems. Offer processing remains at low AI adoption, a sign that while the potential is clear, businesses are still cautious about letting AI directly drive sensitive, high-stakes workflows.
When these projects do land, though, they shift the economics of entire functions. A European bank that introduced AI-based loan approvals cut processing times from days to minutes. That wasn’t just operational streamlining – it redefined their competitive stance against fintech challengers. Customers no longer compared them with other banks, but with the fastest digital lenders in the market. Similarly, manufacturers experimenting with AI-powered supply chain optimisation have reported faster response to demand shocks, which is becoming a strategic advantage in volatile global markets.
Wrap Up
The AI Opportunity Framework is a decision map. It shows you where quick wins live, where longer bets might pay off, and how the market is shifting. More importantly, it forces an honest conversation about balance: are you investing in internal efficiency, external differentiation, or both?
Michael Barskyi
Head of S-PRO Switzerland
Have an AI idea?
Let’s map it on the AI Opportunity Framework and choose 2–3 priorities to pursue.
In 2025, the companies that thrive with AI won’t be the ones experimenting everywhere. They’ll be the ones using frameworks like this to focus, prioritise, and take confident steps. AI is too broad to chase blindly, but with the right radar in hand, it becomes a field where leaders can see not just hype, but direction.
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