Nine AI-fuelled business models that leaders can’t ignore

AI is fundamentally changing how companies create, customise and scale products and services.

AI isn’t just improving corporate productivity and efficiency. It’s poised to reshape how value is defined, delivered and captured with its ability to create content, ideas and logic, and solve problems autonomously. The result: new AI-fuelled business models are challenging long-standing business assumptions and further blurring industry boundaries, contributing to massive new value pools and growth opportunities that will emerge over the next decade and beyond.

While past tech shifts — like cloud, mobile and the internet — also enabled new business models and rapid-growth industries, AI is notable in its potential to transform the economics of product and service creation, customisation and scale with its ability to reason, to continuously learn and to use natural language to interact with humans. In many cases, the large language models at the foundation of GenAI allow companies to create additional units of output — the design of a personalised product, a customer service response or a digital offering — more efficiently, and often at near-zero marginal cost. 

As a result, companies can create customer value that is less constrained by production costs, including labour. Expanding the scope of how products are produced, distributed and purchased might no longer introduce more operational complexity. And managing all types of capital — financial, physical assets and talent — may no longer be limited by data bottlenecks. Instead, companies can act on data at the speed it’s generated for adaptive decision-making. 

The breakneck pace of GenAI adoption offers an early indication of the technology’s promise. In Ireland, 98% of organisations have started their AI journey according to the PwC GenAI Business Leaders Survey, and 62% have realised tangible value in the past 12 months. Furthermore, 81% expect productivity gains of more than 5%, and 43% anticipate profitability improvements. However, it’s still unknown how broadly and deeply these new AI-fuelled business models will take hold. Some will be heavily influenced by the pace of AI adoption, shaped by the degree of trust in the technology, supporting governance models, and emerging capabilities to verify, authenticate and validate underlying data, transactions and parties — human or AI.

Nonetheless, PwC’s GenAI Business Leaders Survey also shows that Irish leaders are optimistic: 86% believe AI will positively impact the economy within five years, and 71% expect changes in value creation within three years. As past technology transformations have shown, fast movers won’t just gain an edge — they’ll reset the baseline for customer experience, putting pressure on the rest of the market to follow. 

To help leaders seize these new opportunities and avoid being caught off guard, we lay out nine new business models that fall into three broad categories: scaling services, increasing product scope and access, and rapidly activating all types of capital as events unfold. We also note key business moves organisations will likely need to make and explore foundational questions leaders can ask to pivot quickly when the time is right.

Nine business models to watch

AI is already reshaping services business models, extending expertise, advice and support without adding employees while reducing the cost per additional unit of service (per hour, session or procedure). As these services become more personalised based on customer context and history, companies will need to rethink everything from supply chains to organisational designs in order to achieve the intelligence and capacity to adapt any product to each customer’s needs.  

Services as software. Companies that sell physical products can now offer real-time, contextual AI services alongside them — across thousands of product variations. Doing so equips organisations to turn one-time sales into ongoing customer value while reducing the cost per service hour. A consumer packaged goods company, for instance, might offer an AI nutrition assistant that doesn’t just cite product nutritional data, but answers real-world questions like ‘Which snack is a good option if I’m avoiding gluten and need to lower my blood sugar?’ or ‘Which protein bars are best before my Tuesday weight training and Thursday cardio classes?’ These services could be monetised through micro-payments — priced per use, query or output — or embedded in the product experience to boost differentiation and perceived value. 

Key business moves 

Manufacturers can start by reassessing how they approach value-added services, including those that were formerly impractical without AI’s ability to precisely answer highly varied service questions. Monetisation opportunities range from launching new services businesses for a broad product portfolio to building fees into the current business on a product-by-product basis. In some cases, companies may offer new services at no charge to prevent commoditisation. All of these efforts may affect service workflows. For example, putting AI in place to interact with customers in real time, across channels, requires new rules for when to escalate customer inquiries to human service representatives. 

All companies must continuously re-evaluate the cost-benefit trade-offs as AI models improve. 

The new agentic AI advisors

Agentic AI advances the capabilities of traditional AI agents, creating systems that don’t just respond to inputs, but proactively solve problems, coordinate tasks and continuously learn through their work. Companies that offer industry-focused advisory services, such as financial, wealth management, health and wellness, and legal services, can use agentic AI to create autonomous AI team members (we call them agentic AI advisors) that can guide human advisors and customers at a fraction of the cost of human staff. Today’s task-specific B2C AI agents, such as AI-powered budgeting apps and AI personal health assistants, represent the first wave of offerings in this area. Expect organisations to create even more customised packages using humans and agentic AI capabilities to span multiple areas (for example, combining health and financial planning) and linking advice directly to actions, such as executing a financial transaction based on the guidance. These services can be scaled to benefit hundreds of thousands of customers on a subscription-based, fee-for-service or per-session basis. 

Key business moves 

Certification requirements for many professional services roles will require human advisors to remain central, with AI agents in a supporting role. One important consideration for leaders: how to build, structure, train and incentivise teams for that operating model. This effort will include re-evaluating the number of direct reports per manager to account for both people and AI agents; performance metrics and incentives when human advisors team with AI agents; and capacity and recruiting targets as human advisors scale their customer base with AI. Don’t forget: managers will also likely need new skill sets to oversee, validate and enhance advice from staff working with AI agents. 

Robotic service providers

Imagine the next generation of robotic vacuum cleaners able to understand and fulfil a request like ‘clean up the spot in the living room where we tracked in dirt,’ without any further direction. Robotic lawn mowers that could autonomously adjust their schedules based on weather reports and yard usage. Robotic nursing assistants that could alert hospital staff when a patient is confused. These are a few of the advanced AI-powered aides that industries, including facilities maintenance and construction, could create to perform labour-intensive, dangerous or precise manual tasks. These robotic aides represent more than just a product upgrade. Because of their ability to learn user preferences and dynamically respond to changes in the environment, they can be hired out to provide ongoing services through product leases or subscriptions (charged per task, per engagement or per hour), or generate revenue from the data they collect. 

Key business moves 

Ethnographic research can help leaders gauge the required investment and road map implications by surfacing insights into customer environments, operating conditions and potential challenges robotic aides may face. Equally important: pre- and post-sales support. How will product sales need to change to support a service-led engagement? What types of service-level agreements will be needed to manage performance expectations over time? How will the organisation monitor, guide and govern these systems as they interact in unpredictable, real-world environments outside of its control? 

Emerging business models in this category use AI’s capability to deliver more personalised products — both what you offer and how you fulfil it — with fewer operational trade-offs. Each model in this category could deliver significant value on its own. Combined, they could create a supply network that adapts in real time and deliver hyper-personalised products to customers everywhere.  

Mass customisers

Some personalisation at scale is already here, with manufacturers providing customers with dozens of choices, of colours, patterns, finishes, materials and other options. The mass customisers of the future, however, will personalise products at incredible speed and with the kind of customer relevance that no menu of options can offer today. Think personalised vitamin packs based on blood panels, or modular furniture manufactured based on your room dimensions, lifestyle and taste — at your doorstep in days, not weeks or months. AI will be able to dynamically tailor designs, materials and fulfilment for each customer, and orchestrate production and distribution across partners or internal systems, without increasing cost or delivery times and without sacrificing margin.

Key business moves

Companies should begin by reorientating their design and manufacturing processes and their supply chain for speed and adaptability instead of cost efficiency. This might include, for example, expanding their supply chain ecosystem with partners able to communicate through AI systems or real-time integration of workflows across the value chain — including raw materials sourcing, product design, manufacturing, multi-modal logistics, and channel models, pricing and packaging.

Reverse auction marketplaces. Imagine asking an AI shopping agent to find a phone with specific features within a budget — and receiving competitive offers. In this model, consumers broadcast what they want and are willing to pay, and sellers bid to win the sale — flipping the script on traditional e-commerce dynamics. We’re already seeing early signs of this shift in models like ‘name-your-price’ insurance, legal platforms that match lawyers to fixed-fee cases, and dynamic ticketing systems that adjust prices based on demand. The next wave of agents won’t be restricted to a particular category; instead, they will search and transact this way across all types of categories, reducing transaction costs and friction during retail purchase without the need for more staff.

Key business moves

Some companies may build platforms to facilitate these AI-to-supplier negotiations. Most will likely need to ensure their products appeal to both human customers and AI agents tasked with making purchasing decisions on their behalf. This work can include expanding the depth and breadth of available product information, such as offering more detail on the types and sourcing of product materials; building advanced personalisation capabilities to offer discounts based on the AI agent’s queries; and re-architecting pricing models to support dynamic, market-based exchanges, rather than traditional pricing tiers.

Autonomous delivery anywhere. This model expands how quickly — and where — goods and services can be delivered using increasingly autonomous fleets of cars, trucks, drones, aircraft and ships. Imagine the possibilities for a retailer or manufacturer when a cargo truck can run 24/7, drones can make last-mile deliveries, and self-driving delivery vehicles are in every town. Same-day, even same-hour, delivery becomes accessible for more businesses and in more locations, without traditional labour constraints (and while reducing traffic congestion and energy usage). More dynamic customer experiences, such as being able to alter an order mid-delivery, will likely emerge. Companies can charge usage fees (such as ride fares, delivery fees or leasing autonomous machines) or higher prices for autonomous-capable integrated distribution and product models. This business model has the longest on-ramp because AI-powered vehicles must reach a high level of safety and efficiency, which they can only do by encountering a lot of situations and learning from the data.

Key business moves

Leaders must decide how much of an autonomous vehicle delivery system to build or partner on. Some might plug into existing networks to ship products faster and less expensively. Others may seek to create more branded, differentiated experiences — white-labelling existing autonomous delivery networks or developing their own fleets in key markets — in order to gain more data, operational control and potentially higher margins. For airlines, logistics, trucking and other transportation companies, the question will be when and how to integrate autonomous vehicles into existing fleets — and how their inclusion could reshape pricing tiers and guarantees.

The previous two categories focus on how companies will likely create and deliver products and services to expand their offerings, but this last category of business models addresses how companies will optimise capital decisions related to financial assets, physical assets and talent. These business models tap into AI’s ability to glean contextual cues from huge volumes of data, simulate outcomes, and become smarter and more efficient with every user, transaction and interaction — enabling companies to monetise high-frequency activity that has been too complex to manage. Because these business models are rooted in broad collaboration across industries, regions and businesses, they will be more dependent on trust and trust solutions — with shared standards and automated, scalable mechanisms for authenticating the participants and the data at every layer of the exchange

Precision capital allocation-as-service

Quantitative hedge funds already demonstrate what reliable AI-enhanced algorithmic trading looks like by optimising portfolios at enormous scale. This business model taps into AI to expand how and where financing organisations can deploy capital to match financing and investment needs in real time across a portfolio of projects. It can empower, for example, a business to coordinate hundreds of thousands of loans in an AI-powered lending marketplace in real time, adjusting rates based on individual risk or dynamically personalising repayment terms based on an individual’s or company’s monthly income. This type of offering can be applied across corporate investments, loans, venture funding, insurance contracts, and even public budgeting and talent financing.

Dynamic asset-monitoring utilities

Predictive maintenance isn’t new. But AI’s ability to efficiently make sense of second-by-second complex sensor and IOT data, extract insights from broad data sets (including historical data), and identify cause and effect will support the delivery of new asset-monitoring utilities that help owners prevent adverse outcomes for any asset (e.g., a factory machine, an oil pipeline or an office building) that can break, overheat, catch fire, leak or otherwise pose risk. These asset-monitoring utilities could, for example, alert a property manager that a water heater is experiencing increased power usage and unusual vibration and has a 90% probability of causing a flood event within 48 hours. Companies offering these services can charge asset owners or insurers a per-site or per-asset service fee or implement analytics-as-a-service contracts, helping their customers to improve maintenance schedules and reduce unplanned downtime, safety incidents and, potentially, insurance premiums, while aligning costs with actual consumption.  

Talent on tap

This model anticipates a world where almost any service — from skilled consulting to everyday errands — can be obtained on a pay-per-use basis. It goes beyond current gig platforms, which are often limited to one type of service, like ride-hailing or freelancing. Instead, these platforms, which will likely be based on commission fees, use AI to handle the heavy logistics for every type of job a company may need, drastically reducing friction for both providers and consumers. AI’s contributions include finding available providers (including freelancers, contractors, agencies and firms) with the desired skill, scheduling them, setting a price and handling payments on demand, and ensuring quality through ratings or monitoring.

Key business moves

Because customers often judge service quality based on resource availability, credibility, reliability, objectivity and frictionless execution, companies should begin by embedding these principles in automated processes for personalising recommendations, matching parties, and tracking activity and payments. To decide whether to adopt existing solutions or form strategic partnerships to access these capabilities, leaders will need to weigh key trade-offs — such as how much control to retain over the customer experience, whether to complement or replace existing software or build in-house alternatives, and how much to invest as the market evolves.

The business models outlined above aren’t linear or incremental extensions of today’s approaches. They would fundamentally change the economics of customer value creation — and businesses that embrace them could reshape the market dramatically. A good way to understand what this could mean for your business is to start with these four questions.

Conclusion

Although the list of increasingly powerful large language models continues to grow and hundreds of millions of people use them in their daily lives and at work for a variety of tasks, the readiness to use AI to reshape how value is created, delivered and captured varies dramatically across industries and companies. So too does the level of trust. Lower-risk innovations, such as an AI-powered robot vacuum or AI product advisors, are poised to leapfrog first as they may be able to grow in most environments, whereas higher-risk autonomous decision-making systems will likely require governments, industries and society to align on responsible use of AI worldwide. Evaluating what your biggest competitor might do, how your business may evolve and what capability investments are needed can help your organisation not only contemplate (and prepare for) new AI-enabled business models’ effect on your future viability but also surface opportunities to improve how you compete today. 

We’re here to help you 

Navigating these AI-fuelled business models requires more than understanding the technology; it demands a fundamental reimagining of how your organisation creates value. At PwC, we take a business-led, tech-powered approach that puts strategy before systems. Our multidisciplinary teams combine deep industry expertise with advanced technical capabilities to help you identify which business models matter most for your organisation, assess the commercial viability and risk profile of each opportunity, and build the roadmap to make transformation real.

From designing customer experiences that work seamlessly with AI agents to re-engineering supply chains for mass customisation, restructuring workforce models for human-AI collaboration, and establishing the governance frameworks that build trust, we help you move from insight to implementation. With our global network and proven track record in delivering complex transformations, we are uniquely positioned to be your partner in seizing AI’s strategic opportunities while managing the operational, regulatory and governance complexities that come with them.