Anticipating the continuous growth of the AI market, companies and entrepreneurs rush to implement “the latest tech” and “advanced AI models” without too much prior strategizing. To ride the profitable and beneficial wave of AI adoption, however, having a roadmap is paramount.
This article goes into detailed AI adoption statistics and real-world use cases by industry and task. It provides insights into how exactly you can use available AI opportunities and maximize the efficiency of your decision.
Contents
Custom AI Models: Accessible and Cost-Effective
The Current State of AI Adoption
AI Adoption Trends Across Key Industries
Phases And Milestones In The AI Adoption Roadmap
Analyzing Job Exposure and Disruption Risks
How AI Drives Business Transformation
Enter the AI Future with Confidence
Global AI Market Overview
Analysts project sustained global AI market growth, which is set to surpass $240 billion in total value. AI adoption by the industry is growing by up to 20% each year. In the 2023–2024 stretch alone, the use of generative AI jumped from 55% to 75%, with companies getting a 3.7x ROI for every buck they invest in GenAI and related technologies.
The leading adopters of AI solutions like IBM, Shopify, and Coca‑Cola have moved from using the technology for purely routine tasks to directly boosting employee productivity and speeding up top-line growth. AI-driven productivity tools remain the clearest path to commercial return.
Source: 2024 Business Opportunity of AI
Business Growth Through AI
The AI adoption curve is still climbing, but how far it goes depends on whom you ask. Goldman Sachs projects a 15% boost to global GDP from AI over the next decade. J.P. Morgan is more restrained, expecting an 8% to 9% increase. Daron Acemoğlu, MIT economics professor, takes a much more cautious view of AI’s global economic impact, projecting only a 1–1.5% boost.
What’s clear is that most value today comes from getting the basics right. You’re more likely to make AI work for your business if you design it with the intent to:
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Reduce operational costs
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Drive business growth
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Differentiate customer experience
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Ensure secure and reliable operations
According to Max Belov, CTO at Coherent Solutions, AI doesn't need to be revolutionary but must first be practical. Too often, companies rush to adopt shiny new tools as part of transformation plans, only to discover they’ve overspent on systems without clear goals or an execution path.
Max Belov, CTO at Coherent Solutions
for TechRadar Pro
Organizations need to first sit down, establish realistic goals, and evaluate where AI can support their people and how it can be incorporated into their business objectives.
The AI niche used to be very concept-driven back in the day, with investor-seeking designs, Proofs-of-Concept, ICOs, and crowdfunded campaigns reigning supreme.
We see real models bringing tangible results and respective profits to their owners and creators. For instance, Accenture reports that AI should bring a 35% productivity boost to the US labor sector by 2035 (as well as to other economies, 36% for Finland, 37% for Sweden, etc.).
The boundaries of AI adoption across industries expand even further due to an opportunity to take clean slate AI models and tune them into whatever you need. Customizable AI is readily available today, which means you can save a ton of automation expenses without going far or going broke.
This is why a majority of organizations surveyed by Accenture plan to expand beyond pre-built AI solutions to customized or custom-built AI workloads, which brings us to another point.
The Current State of AI Adoption
According to global AI adoption studies by several sources:
| 75% |
92% | 20% |
|
of firms have employed AI by 2025 vs. 55% by 2024 |
of companies look to invest more in AI in 2025–2027 |
of tech budgets will be allocated to AI in 2025 |
Within the top 25% of AI spenders are healthcare, financial agencies and banks, media and telecom, manufacturing, and retail. Following those, energy and materials, consumer goods and ecommerce, hardware engineering, travel, transport, and logistics are all powered by AI to a certain extent.
AI Adoption Trends Across Key Industries
We can typically see the highest rates of AI adoption among the operations that must generate or digitize large sets of structured and unstructured data. The greater the data available, the more effectively AI models can be trained, refined, and scaled. So, this data focus works both ways.
IT and Telecom
Symbiosis with AI is projected to potentially earn $4.7 trillion in gross value added for IT and telecom by 2035. This is a spacious niche where providers can develop and integrate AI platforms to run a range of internal, technical, and consumer services.
Source: 2025 Global Telecommunications Outlook
We can already witness the pioneers in these niches, like the AI-RAN Alliance, launched back in February 2024. The AI-RAN Alliance gathers the top telecom and tech market leaders to focus efforts on merging AI with cellular technology. The ultimate goal of the alliance is to achieve new advancements in the RAN (radio access network) technology.
Other use cases for AI adoption in IT and telecom:
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Network planning and optimization
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Network security
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CX enhancement
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Predictive maintenance
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Network slicing
Healthcare
Numerous healthcare facilities and brands rely on the custom development of AI solutions. For example, custom AI tools enable safer and hyper-precise drug development and testing, highly detailed medical imaging, and automation of a ton of administrative work.
Source: AI Adoption in Healthcare Report 2024
Importantly, the generative AI adoption rate is still only maturing in settings as complex and responsibility-driven as medicine. However, we can already see impressive implementations:
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At Coherent Solutions, we tapped into the creation of AI for healthcare with the RX transcription tool for an eyewear manufacturer company. This solution helps interpret optical prescriptions and select the right glasses.
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Tempus is a precision medicine platform that uses AI to analyze clinical data and personalize cancer care and other treatments.
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PathAI uses deep learning to improve the accuracy of pathology diagnoses for faster, more precise treatments.
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The NMDP Donor Readiness Score helps predict individual stem cell donor availability.
Other use cases for AI adoption in healthcare:
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Developing drugs
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Clinical documentation
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Clinical trials
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Medical imaging
Finance and Banking
The productivity of knowledge workers, from accountants and managers to researchers and developers, can be boosted dramatically by automating mundane tasks. Like routine mortgage reviews, market inspection, answering generic customer queries, etc.
AI tools, like innovative authentication systems, have also been developed to reinforce the security of access and interaction with valuable assets. At Coherent Solutions, we had the pleasure of building a feature-rich identity authentication platform that restricts access based on real-time user behavior monitoring and analysis.
In dry figures, the financial sector can get up to $1.2 trillion extra GVA thanks to mass AI adoption in financial services in 2035. But that’s if the market players are not slowed down by the emerging governance of AI adoption in central banks and other associated risks too much.
Use cases for AI adoption in banking and finance:
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Anomaly detection
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Payments
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Robo-advisors (portfolio management)
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Algorithmic trading
Manufacturing
Robotics and IoT help accelerate AI adoption in manufacturing with intelligent systems that connect directly to construction sites and enable remote opportunities. Accenture’s research shows that AI could enrich the manufacturing sector with an extra $3.8 trillion GVA in 2035. But there are more promising stats.
Source: Taking AI to the Next Level in Manufacturing
The 2025 State of AI in Manufacturing Survey, which indicates that:
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More than 77% of manufacturers have implemented AI to some extent (as compared to 70% in 2023).
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AI in manufacturing is mostly employed in solutions for production (31%), customer service (28%), and inventory management (28%).
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Rather than fully autonomous AI bots, most manufacturing specialists (53%) would prefer working with collaborative bots or “copilots” (AI agents that support human workflows instead of fully replacing them).
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The leading investment niches for AI in manufacturing are supply chain management (49%) and big data analytics (43%).
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56% of manufacturers are still unsure whether their existing ERP systems are ready for full-on AI integration.
Use cases for AI adoption in manufacturing:
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Cobots (collaborative robots)
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Generative design
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Quality assurance
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Predictive maintenance and demand forecasting
Retail
According to Deloitte’s 2025 US Retail Industry Outlook, GenAI is really coming in handy in commerce. In particular, retailers saw 15% higher conversion rates after using chatbots during the Black Friday weekend.
Furthermore, IBM’s research states that organizations working with retail and consumer products will be making the most extensive use of AI across 2025 and beyond. Solutions like Spokn AI have been developed to help ease global consumers’ widespread adoption of AI features.
Spokn AI is a tool for in-depth speech analytics in a contact center, which helps analyze sentiment and gain insights from customer conversations to find ways to improve their experience.
Other use cases for AI adoption in retail:
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Hyper-personalized shopping
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Inventory management
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Fraud detection
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Voice and visual search
Phases And Milestones In The AI Adoption Roadmap
A white paper by the World Economic Forum provides a breakdown of the stages companies typically follow to adopt AI. The last two stages are predicted to become the new normal in the near future:
| Phase 1 | Phase 2 | Phase 3 | Phase 4 | Phase 5 |
|
Lack of AI (or AI ban) |
Pilot AI adoption, experimentation |
Functional reinvention via AI |
Enterprise-level changes via AI |
Reinvention and boost of cross-industry processes |
|
What we used to get |
What we get today (2025) |
What we’ll get in the near term |
What we’ll get in midterm |
What we’ll get in the long term (2029-2030) |
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Regulatory barriers, security risks, and other obstacles prevented companies from adopting AI reasonably. |
With real-world implementations gradually becoming a norm, companies actively experiment with AI, integrating it across new operational facets. |
Companies should get visible results and value from AI adoption, which can be measured, i.e., in respective profits. |
Companies should move on integrating now more mature AI solutions across more areas, including fundamental infrastructure, to a fuller extent. |
Continuous AI adoption should seep into more internal and external processes, offering value for entire chains of operations. |
You’ll need more than typical steps to maximize AI’s efficiency and profitability.
As more businesses start using AI, a few big questions arise. How much energy do these systems use? How might they affect jobs and workplace safety? What does it really mean when AI starts making decisions on its own?
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AI Energy Dilemma
The autonomous performance of AI has a cost—energy. Depending on the task it handles, an AI model can consume up to 500+ Wh (watts/hour).
On top of that, enterprise- and industrial-scale AI solutions need to be powered by dedicated data centers. This must be why, according to Deloitte, AI operations can easily eat up to 40% of all power required to run a data center.
Coherent Solutions’ ML Engineer, Victoria Serghievici has shared that:
Victoria Serghievici, Machine Learning Technical Lead and Group Manager at Coherent Solutions
for SiliconRepublic.com
Data centers feeding AI with massive amounts of data are projected to consume about 3-4pc of the world’s electricity by 2026.
Victoria also says that if data centers are about to start consuming around 1,189 TWh (4pc) each year, we are looking at 4x more electricity consumed than the entire country of the UK in a year.
AI adopters should calculate their costs in-depth and balance out expenses, as well as measure the cost ratio of AI performance versus human specialist’s work. The latter may not always need to be replaced entirely.
Analyzing Job Exposure and Disruption Risks
AI continues to replace human talent in many types of workflows. As adoption scales, more jobs face disruption or even full replacement.
AI solutions should affect 60 million US and Mexican jobs in just a year. Specifically, about 43 million (US) and 16 million (Mexico) jobs are highly exposed to AI within a one-year time horizon.
Source: Artificial intelligence will affect 60 million US and Mexican jobs within the year
According to the index above, various iterations of AI will touch 980 million jobs worldwide, affecting about 26% of the global workforce in some way or another. Give it another five years, and that figure should grow to 38% and in 10 years to 44%.
Agentic AI
Agentic AIs are advanced models that can run without human intervention while handling a range of complex, multistep tasks and reasoning problems. As such autonomous AIs improve, they could impact all known industries, both heavy and precise, from supply chain management and manufacturing/construction to medicine and education.
Marking the widespread focus on AI agents, Igor Epshteyn, President and CEO of Coherent Solutions, says:
Igor Epshteyn, President and CEO at Coherent Solutions
for Clutch
2025 will mark a significant milestone in AI agent adoption across industries such as finance, supply chain, sales, services, marketing, and tax… OpenAI’s recent announcement of the “Operator” framework and Amazon’s Bedrock Agents framework, which will enable companies to incorporate AI agents into their enterprise, highlights this trend.
However, the main positions that such AIs can automate are those of knowledge workers (e.g., engineers, accountants, analysts, etc.). There are more than 100 million knowledge workers based in the US and over 1.25 billion globally. Today’s most relevant Agentic AI use cases have already started replacing them in aspects such as:
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Customer support
AI agents handle customer inquiries that regular support chatbots cannot, including complex issue resolution, like providing personalized step-by-step instructions. They can also analyze calls and interactions to help boost customer satisfaction.
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Cybersecurity
Agentic cybersecurity systems help detect attacks autonomously, they generate reports that help pinpoint flaws and improve security, and automate up to 90% of other human expert’s workload.
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Regulatory compliance
Emerging agentic AIs can analyze corporate documents and regulations, providing fast and precise compliance checks for companies and startups in industries from finance to healthcare.
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Agent builders and orchestrators
A number of startups gradually introduce tools and platforms for setting up custom multi-agentic systems that can be tuned for any purpose. These include the Google Vertex no-code tool for creating individual agents and LangChain for multi-agentic solutions.
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How AI Drives Business Transformation
Businesses of all scales, as well as startups, initiatives, and projects of any purpose, leverage AI to reinvent and upgrade the usual and outdated processes. Whether it’s a project for small-to-medium business or an enterprise AI adoption cycle, companies and startups of all scales can use AI capacities for:
- Customer service chatbots and agents. A lot of burden can be relieved from human support agents with virtual assistants that handle routine (or more complex) customer queries and inputs.
- Predictive analytics to forecast demand. Specialized AI models dig into historical sales data, seasonal dynamics, and market trends to forecast a service’s or product’s demand.
- Predictive maintenance. Connected to hardware or vehicles, AI-based systems for maintenance can track equipment performance in real time, pinpoint anomalies, and predict accidents or failures.
Still, many AI adopters looking to digitize workflows or other processes fail to achieve success with their intentions, mostly due to misplacing AI tools or rushing to adopt them without researching their applications first.
Igor Epshteyn, President and CEO at Coherent Solutions
for Digitalisation World
Less than 30% of tech businesses succeed with digital transformation strategies. On their bumpy roads to success, many zero in on adopting more technologies, mistakenly viewing that as a silver bullet for digital transformation. But solely solving technology problems is not enough. Businesses should overhaul their strategies, processes, and mindsets to ride the wave of digital transformation.
Main AI Adoption Challenges
Based on the survey by MIT Technology Review, the top five challenges of adopting AI faced by most companies and projects are:
To overcome these challenges, you need a seasoned provider ready to fulfill your lack of AI software skills, deliver individual guidance, and implement AI solutions end-to-end.
Enter the AI Future with Confidence
The global outlook for AI adoption is very much in favor, and the possibilities are vast. However, competition is fierce, and a high level of expertise and proven results is needed to make your product stand out and reach sustainable profitability.
If you are exploring AI for any part of your tasks, consider partnering with an experienced technology and software provider that delivers flexible AI services, tailored to meet specific needs and maximize your business's potential.
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