- AI spend is exploding. IDC forecasts worldwide AI spending will reach $632 billion by 2028, with the U.S. alone accounting for over half of that. my.idc.com
- Generative AI is eating the AI software market. Forrester projects 36% annual growth in generative AI spend to 2030, capturing 55% of the AI software market. Forrester
- Value is concentrated in a few functions. McKinsey estimates generative AI could add $2.6–4.4 trillion annually, with ~75% of the value in customer operations, marketing & sales, software engineering, and R&D. McKinsey & Company
- Adoption is high, but scaling is hard. The 2025 McKinsey Global Survey finds 88% of organizations now use AI in at least one function, yet only a small subset attribute more than 10% of EBIT to AI. McKinsey & Company
- Many firms still struggle to see returns. BCG’s 2024 research reports 74% of companies struggle to achieve and scale AI value, but “AI leaders” expect over double the ROI of others and generate 45% more cost reduction and 60% more revenue growth from AI. BCG Global
- AI-led processes are paying off. Accenture finds organizations with AI-led processes achieve 2.5× higher revenue growth, 2.4× greater productivity, and are 3.3× more successful at scaling generative AI. Accenture Newsroom
- #1 profit engine: marketing & personalization AI. Recommendation and “next best experience” models typically drive 5–30% revenue uplift in consumer businesses; Amazon’s recommendation engine alone is estimated to generate about 35% of its sales. madgicx.com
- Customer service AI agents are slashing costs. IBM research cited by Forbes shows AI virtual agents can cut customer service costs by up to 30%, while Gartner expects generative AI in CX to reduce agent headcount 20–30%. Forbes
- Developer copilots show some of the clearest ROI. A randomized trial on GitHub Copilot found developers completed tasks 55.8% faster with the tool; McKinsey highlights software development as a top value driver for generative AI. Microsoft
- Industrial AI remains a quiet profit machine. Predictive maintenance and preventive maintenance can deliver up to 10× ROI and 545% returns per dollar spent, with unplanned downtime often costing >$100,000 per hour. assets.new.siemens.com
- AI agents are becoming huge businesses themselves. Salesforce’s AI agent platform Agentforce has already hit ~$540 million in ARR, helping push AI-related ARR to $1.4 billion, with 114% year-over-year growth. Reuters
1. Why “the most profitable AI model” in 2025 is really a portfolio question
If you came here expecting one magic model (say, “GPT-X”) to be the most profitable AI model for every business, the short answer is: that’s not how value shows up in 2025.
Across industries, profitability is dominated not by one foundation model, but by repeatable patterns of applied models:
- Recommendation & personalization engines in consumer businesses
- Pricing & revenue management in travel, retail, and marketplaces
- AI-powered marketing and “next best experience” engines
- AI agents in customer service and internal operations
- Developer and knowledge-worker copilots
- Risk & fraud models, predictive maintenance, and supply chain optimizers
McKinsey captures this well: about 75% of generative AI’s potential value sits in four business functions—customer operations, marketing & sales, software engineering, and R&D. McKinsey & Company
So this 2025 ranking focuses on model types + use cases that:
- Are widely deployed today
- Have strong, independently documented ROI
- Can be implemented by most organizations without building their own foundation model
We’ll still talk about frontier models and agents. But if you’re a business leader asking “Where do we actually make money with AI next year?” these are the models that matter.
2. Methodology: how this 2025 ROI ranking was built
This ranking draws on:
- 2023–2025 research from McKinsey, BCG, Accenture, IDC, Deloitte, Forrester, MIT, and others Channel Impact
- Market forecasts for AI and generative AI (IDC, Bloomberg Intelligence, Forrester) Forrester
- Real-world 2024–2025 case studies and news coverage (Amazon, Salesforce, major banks, hospitality, retail, etc.) SuperAGI
Each model type is scored qualitatively on:
- Direct revenue or cost impact (uplift, savings, or margin improvement)
- Time-to-ROI (months to see measurable impact)
- Scalability & repeatability (usable across units/markets)
- Maturity & risk (how battle-tested and well-understood it is)
3. 2025 ROI Ranking: The 7 Most Profitable AI Model Types for Business
1️⃣ Marketing & personalization models
(recommendation engines, next-best-offer / next-best-experience, dynamic promotions)
2️⃣ Customer service AI agents & virtual assistants
(LLM/RAG chatbots, agentic workflows for support & sales)
3️⃣ AI copilots for software and knowledge work
(code assistants, document/office agents, workflow copilots)
4️⃣ Fraud detection & risk-scoring models
(anomaly detection, graph models, credit & AML scoring)
5️⃣ Supply chain, demand forecasting & inventory optimization models
6️⃣ Predictive maintenance & industrial optimization models
7️⃣ Horizontal AI agents embedded in SaaS & productivity platforms
(CRM agents, “Office” agents, AI-native workflows)
Now let’s dig into what each model actually does, what the numbers look like, and when it should rank at the top for your business.
4. #1 – Marketing & Personalization Models
(Recommendation engines, next-best-experience, dynamic offers)
What these models do
These are the systems that decide:
- What to show (product/content recommendations)
- Who to target (propensity & churn models)
- When and how to contact customers (next-best-action / next-best-experience)
- How much to offer (promotion & discount optimization)
Under the hood, they mix collaborative filtering, deep learning, uplift modeling, and sometimes generative AI for creative content.
Why they top the 2025 ranking
- Direct revenue engine.
- Amazon’s recommendation engine is estimated to drive around 35% of its sales. Head of AI
- Multiple industry analyses show AI product recommendations can increase e‑commerce sales by 20–30% and boost conversion rates by ~23% on average. zignuts.com
- Broad horizontal applicability.
Retail, media, travel, financial services, B2B SaaS – if you have a catalog and customers, this applies to you. - Stacked ROI: content + targeting + experience.
McKinsey’s 2023 generative AI report estimates that marketing & sales is one of the top four functions where generative AI can create the most value, with use cases from personalized content to dynamic campaigns. McKinsey & Company Their 2025 “next best experience” (NBX) work shows AI-powered NBX can:- Increase revenue by 5–8%
- Reduce cost to serve by 20–30%
- Improve customer satisfaction by 15–20% McKinsey & Company
Expert voices
McKinsey summarizes the opportunity bluntly: “Generative AI is poised to unleash the next wave of productivity.” McKinsey & Company
Forrester adds that generative AI’s rise is comparable to “the launch of social media, the smartphone, and the internet,” projecting it will capture most of the AI software market’s growth. Forrester
When this should be your #1 priority
- You’re in retail, e‑commerce, streaming, media, travel, or financial services
- You already have traffic but aren’t personalizing deeply
- You have enough data (hundreds of thousands of customers or more)
For many B2C companies, this is the single highest-ROI AI investment in 2025.
5. #2 – Customer Service AI Agents & Virtual Assistants
What these models do
Think LLM-powered agents and RAG chatbots that:
- Answer customer queries
- Handle transactions (reset passwords, change bookings, track orders)
- Assist human agents with suggested replies, knowledge lookup, and summarization
- Deflect repetitive tickets before they ever reach the contact center
Models often combine LLMs, retrieval-augmented generation (RAG), and intent classification with existing CRM tools.
ROI picture in 2025
- IBM research (via Forbes) finds AI “virtual agents” can reduce customer service costs by up to 30%. Forbes
- A McKinsey-cited large-scale study of 5,000 agents found a generative AI assistant increased issues resolved per hour by 14% and cut handle time by 9%. McKinsey & Company
- Gartner projects that by 2025, 80% of organizations will use generative AI to improve customer experience, reducing human agents by 20–30%. Forbes
- Case studies show AI support reducing response times by up to 97% with 50% of tickets resolved automatically. usepylon.com
Market and news signals
- Salesforce’s Agentforce AI platform – heavily used for service and workflow automation – has already exceeded $500 million in ARR, with AI-related ARR near $1.4 billion, growing 114% year-over-year. Reuters
- Research from Sprinklr highlights a Stanford/MIT study where a generative AI assistant boosted agent productivity by 15% in issues resolved per hour. Sprinklr
When this should be your #1 priority
- You run contact centers, help desks, or high-volume support (telecom, banking, e‑commerce, SaaS)
- Labor costs are high, satisfaction scores are under pressure, or you’re struggling to scale support globally
- You already have documented knowledge bases and call logs to train RAG systems
In 2025, many enterprises see customer service AI as the fastest route to hard-dollar savings and measurable CX improvement.
6. #3 – AI Copilots for Software and Knowledge Work
What these models do
These are LLM-based copilots and agentic tools that help humans:
- Write and refactor code (GitHub Copilot, Claude Code, etc.)
- Draft and edit documents, presentations, and spreadsheets
- Analyze data, generate queries, and automate reporting
- Summarize meetings, emails, and long documents
What the evidence says
- A controlled GitHub Copilot experiment showed developers completed a coding task 55.8% faster with AI assistance. Microsoft
- McKinsey highlights software development as one of the top four functions where generative AI can create value, with potential to materially increase the speed and quality of R&D and code. McKinsey & Company
- New experiments at Microsoft, Accenture, and others similarly find large productivity gains for high-skilled workers using generative AI assistants. MIT Economics
Beyond dev tools, Microsoft has started rolling out agentic “Office Agent” and Agent Mode for Excel and Word, powered by OpenAI’s GPT‑5 and Anthropic models. These break complex tasks into live, interactive workflows inside Office. The Verge
Big-company 2025 signals
- An internal Microsoft memo (reported by Business Insider) tells staff that “using AI is no longer optional” and indicates AI usage may factor into performance reviews. Business Insider
- U.S. banks report measurable coding productivity lifts: Citigroup’s incoming CFO says AI has already delivered a 9% productivity increase on the coding front. Reuters
When this should be your #1 priority
- You’re a software-heavy or knowledge-heavy organization (tech, finance, consulting, professional services, government)
- Developer time is a major cost center or bottleneck
- You already use GitHub, VS Code, or Microsoft 365 at scale
Developer and knowledge-work copilots often generate clear, quantifiable time savings within weeks, making them one of the cleanest business cases for AI in 2025.
7. #4 – Fraud Detection & Risk-Scoring Models
What these models do
These are mostly non-generative models:
- Anomaly detection models flag unusual transactions or behaviors
- Graph neural networks (GNNs) connect entities to uncover complex fraud rings
- Machine learning models score credit, AML risk, and suspicious cases for investigation
ROI in the real world
- AI-based fraud and anomaly detection can improve detection performance by up to 40%, reduce false positives, and save organizations millions of dollars annually. lucid.now
- AI-enabled fraud analysis and investigation is cited by IDC as a top AI use case driving strong growth in AI spending in Asia-Pacific. IDC
Sector context
Banks and fintechs were among the earliest adopters of AI for credit risk and fraud; they’re now layering generative AI for case summarization and investigation notes on top of traditional models. McKinsey estimates generative AI alone could add $200–340 billion in value annually for the banking industry. McKinsey & Company
At the same time, U.S. bank executives are openly describing AI as a key productivity driver. JPMorgan’s Marianne Lake told investors that AI has doubled the bank’s productivity growth rate from 3% to 6%, with operations specialists expected to see 40–50% productivity gains. Reuters
When this should be your #1 priority
- You operate in banking, payments, insurance, marketplaces, or any high-fraud environment
- Your fraud losses or false-positive review costs are material
- You have large historical transaction datasets
Because fraud losses hit the bottom line directly, even modest accuracy improvements can translate into huge ROI, pushing this model type high up the profitability ranking, especially in financial services.
8. #5 – Supply Chain, Demand Forecasting & Inventory Optimization Models
What these models do
These models forecast demand at SKU or location level and then optimize inventory, replenishment, and logistics decisions:
- Time-series forecasting and deep learning models for demand
- Optimization engines for inventory levels, safety stock, and replenishment
- Sometimes combined with generative AI for scenario explanation and “what-if” planning
ROI evidence
- Studies of AI-driven demand forecasting show retailers achieving 15% reductions in stockouts and 20% reductions in excess inventory carrying costs. SuperAGI
- Research on AI-driven forecasting highlights the cost of overstock and stockouts – both directly linked to profitability and customer satisfaction. wjarr.com
In supply chain and inventory management, McKinsey’s 2024 AI survey found these functions are where companies most often report meaningful revenue increases (>5%) from generative AI, while analytical AI continues to drive strong cost benefits in service operations. McKinsey & Company
When this should be your #1 priority
- You manage complex inventory (retail, CPG, manufacturing, aftermarket parts, pharma)
- Working capital and logistics costs are major constraints
- You already have historical demand and inventory data
For asset-heavy sectors, this is often the first or second most profitable AI investment, even though it’s less visible than flashy chatbots.
9. #6 – Predictive Maintenance & Industrial Optimization Models
What these models do
Predictive maintenance uses AI to anticipate failures of machines or assets and schedule intervention before they break:
- Models on sensor/IoT data detect early patterns of failure
- Optimization models schedule maintenance, spare parts, and technician routes
- Often combined with computer vision for visual inspections
The money side
- The global predictive maintenance market is growing rapidly (CAGR ~17%), partly because median unplanned downtime costs exceed $100,000 per hour in many industrial contexts. iot-analytics.com
- Studies and vendor analyses suggest up to 10× ROI from intelligent predictive maintenance when you factor in avoided downtime, reduced over-maintenance, and asset life extension. OpenText
- Preventive maintenance more broadly has been shown to deliver 545% returns for every dollar invested in some contexts. Verdantis
When this should be your #1 priority
- You run factories, fleets, utilities, energy assets, mining, aviation, or logistics networks
- Downtime is extremely expensive and safety-critical
- You already collect sensor or machine data (or can economically start)
Predictive maintenance may not trend on social media, but in heavy industry it is often the single most profitable AI model, especially when paired with supply chain optimization.
10. #7 – Horizontal AI Agents Embedded in SaaS & Productivity Platforms
What these models do
This category exploded in late 2024 and 2025:
- CRM agents (Salesforce Agentforce, Oracle agents) that autonomously follow up with customers, manage cases, and orchestrate workflows
- Productivity agents in Microsoft 365, Google Workspace, and others that can act on documents, spreadsheets, and emails, not just summarize them The Verge
- Internal AI agents that move data between systems, trigger actions, and handle “swivel-chair” tasks across tools
Market signals
- Salesforce’s CEO Marc Benioff calls their AI products “momentum drivers,” noting that Agentforce and Data 360 have reached nearly $1.4 billion in ARR with 114% year-over-year growth, and that AI agents are driving new revenue forecasts. Reuters
- Amazon’s internal forecast suggests its AI shopping assistant Rufus could generate over $700 million in indirect operating profit in 2025 and $1.2 billion by 2027, via improved recommendations and ad monetization. Business Insider
- Menlo Ventures estimates enterprise buyers will spend $4.6 billion on generative AI applications in 2024, an almost 8× increase over the prior year, largely driven by app-layer and agentic use cases. Menlo Ventures
When this should be your #1 priority
- You’re already standardized on Salesforce, Microsoft 365, Google Workspace, ServiceNow, or similar platforms
- Vendor-native AI agents are available and can be turned on with configuration rather than large internal build-outs
- You want broad, incremental productivity across functions rather than a single big use case
In 2025, AI agents are where the platform vendors are racing hardest. Early adopters are seeing outsized gains – but also dealing with governance, quality, and workforce change issues.
11. Frontier models vs. applied models: where does “build your own LLM” fit?
With data centers consuming enormous investment – the Financial Times estimates AI-driven data center projects are now a major driver of U.S. GDP growth but are also running into power and community constraints Financial Times – it’s tempting for boards to ask whether they should build proprietary foundation models.
What 2024–2025 data suggests:
- McKinsey’s 2024 AI survey finds about half of gen AI uses still rely on off‑the‑shelf models, but high performers are more likely to significantly customize or build their own – and they attribute more than 10% of EBIT to AI. McKinsey & Company
- However, building or fine‑tuning large models requires massive capex, data engineering, MLOps, and risk management, which only a small subset of enterprises can justify today.
For 90%+ of organizations in 2025, the most profitable path is:
Apply existing foundation models to the high-ROI patterns above, rather than competing with hyperscalers on model training.
12. Sector-by-sector: which AI model ranks #1 for you?
Retail & E‑commerce
- Marketing & personalization models (recommendations, NBX)
- Supply chain & inventory optimization
- Customer service AI agents
Shein-style ultra-fast fashion and global marketplaces already use AI to manage product discovery and demand, but are under scrutiny for environmental and labor impacts, showing how profitability must be balanced with responsible AI use. TIME
Banking & Financial Services
- Fraud detection & risk models
- Customer service agents and AI copilot tools
- Marketing & personalization (offers, credit, cross-sell)
U.S. banks are publicly telling investors that AI is boosting productivity and will accelerate job reductions in some areas, while improving coding productivity and real-time support. Reuters
Manufacturing, Energy, Transportation
- Predictive maintenance and industrial optimization
- Supply chain forecasting
- Computer vision for quality inspection
Given the huge cost of downtime and asset failure, predictive maintenance routinely tops the AI ROI list in these sectors. iot-analytics.com
SaaS & Professional Services
- Developer & knowledge-worker copilots
- Horizontal agents in CRM, ERP, productivity suites
- Customer service AI agents
Accenture’s recent partnerships with both OpenAI and Anthropic – including a dedicated “Accenture Anthropic Business Group” training 30,000 employees on Claude-based tools – are emblematic of consulting’s bet that AI copilots and agents will reshape their business. Business Insider
13. How to choose your own “most profitable AI model” in 2025
A simple decision flow for leaders:
- Map profit levers.
Where does your P&L move most: acquisition, retention, pricing, cost to serve, downtime, fraud, or speed-to-market? - Match to model types from the ranking.
- Revenue side: look at marketing & personalization, dynamic pricing, and AI agents in sales & service.
- Cost side: service agents, fraud, supply chain, predictive maintenance, copilots.
- Validate with external benchmarks.
Use numbers from the studies cited above (e.g., 5–30% revenue lift, 10× ROI, 30% cost reduction) as sanity checks, not promises. - Design for workflow change, not “AI features.”
High performers don’t just drop models into old processes. McKinsey’s 2025 AI survey notes that they redesign workflows and apply AI in more functions, including risk and finance. McKinsey & Company - Tie initiatives to clear business goals.
A Google Cloud & NRG survey (summarized by Brian Heger) reports that 74% of executives achieve ROI within year one when AI initiatives are tied directly to business goals, with 39% seeing productivity at least double. Medium
14. Risks, backlash, and what can go wrong
It’s not all upside:
- Inaccuracy and hallucinations.
McKinsey finds inaccuracy is the gen‑AI risk organizations most frequently report experiencing, with 44% having at least one negative consequence. McKinsey & Company - Developer backlash and trust issues.
GitHub users have expressed frustration at Copilot features they can’t turn off, raising concerns about quality, privacy, and control. TechRadar - Infrastructure and sustainability concerns.
Massive AI-driven data center expansion is straining power grids, sparking local opposition, and prompting talk of an “AI infrastructure bubble,” even as the long-term value of these assets is debated. Financial Times - Workforce disruption.
Economists estimate that jobs where nearly all tasks can be automated have already seen employment declines, and U.S. banks openly discuss AI as a driver of future job cuts. Penn Wharton Budget Model
Companies seeing the highest AI ROI typically also invest more in change management, skills, and responsible AI – not just technology. Tip of the Spear Ventures
15. Outlook: what changes by 2027?
By 2027, several shifts are likely if current trends hold:
- AI agents move from early adopters to mainstream, especially in CRM and productivity suites. Medium
- Verticalized copilots (for law, medicine, engineering, etc.) become the main way professionals interact with software.
- More value migrates toward organizations that can redesign processes around AI rather than simply adding tools – Accenture calls this “total enterprise reinvention.” accenture.com
- Regulation and governance catch up, especially around model transparency, IP, and safety.
But one pattern is very likely to stay:
The most profitable AI model for your business in any given year will be the one that is closest to your real profit levers and most deeply integrated into how people actually work.
