Industry Applications: AI in Practice

Explore by industry:

• Financial Services
• Private Equity
• Manufacturing
• Retail
• Healthcare
• Law Firms
• Restaurants and Hospitality
• Technology

As a result, while the capabilities of AI are broadly consistent, their application and rate of adoption vary significantly across sectors. AI adoption looks different by industry because each sector has its own data, compliance demands, legacy systems, AI does not exist in a vacuum. Its usefulness is shaped by the realities of each industry: the systems they rely on, the data they generate, the regulatory environments they navigate, and the competitive pressures they face and margin pressures.  

What emerges is a spectrum of maturity, ranging from early experimentation in some industries to deeply embedded, mission-critical deployment in others.

Financial Services: Efficiency, Compliance, and Competitive Pressure

In financial services, AI adoption is driven by a combination of necessity and opportunity. Banks and financial institutions operate in highly regulated environments, with complex processes and significant cost burdens tied to compliance, risk management, and reporting. These characteristics make them ideal candidates for AI-driven transformation. 

AI is being deployed to automate and enhance processes such as: 

• Risk and compliance monitoring 
• Fraud detection and anti-money laundering (AML) 
• Financial reporting and reconciliation 

These use cases are particularly well suited to AI because they involve large volumes of structured data and clearly defined rules. By automating these processes, organizations can reduce costs, improve accuracy, and increase throughput — delivering immediate and measurable value. 

At the same time, the industry is facing increasing competitive pressure from neobanks and fintech firms, which operate with lower cost structures and more agile technology stacks. For traditional banks, particularly regional and community institutions, AI is becoming less a strategic choice and more a survival imperative. 

Looking ahead, a new frontier is emerging at the intersection of AI and digital assets. The convergence of AI, blockchain, and financial infrastructure is enabling new models of transaction and interaction, including agent-driven payments and automated financial workflows.  

While still evolving, these capabilities point to a future where AI participates directly in economic decisions and processes. 

“We are seeing AI in banking moving from experimentation to enterprise execution — its role now is to fundamentally improve productivity and protect returns in a structurally pressured industry.”  

— Jonathan Roberts, Financial Institutions and Specialty Finance Industry Leader 

Private Equity: Accelerating Value Through Speed, Insight, and Efficiency 

AI is rapidly emerging as a high-impact lever for value creation across the private equity lifecycle, although adoption can vary significantly depending on the size of the fund.  

Unsurprisingly, the larger funds (those managing over $40 billion in assets) are leading the way, establishing dedicated AI teams that operate both at the fund level and across portfolio companies. These teams focus on enhancing deal execution, financial reporting, and operational performance. 

Mid-sized funds are taking a more targeted approach, hiring individual AI specialists to drive efficiency, while smaller funds may lack dedicated resources altogether. As a result, smaller firms are increasingly seeking external guidance to identify practical, high-ROI use cases and accelerate adoption. 

Across all fund sizes, AI is delivering value in two core areas: deal execution and portfolio performance. In deal environments, AI significantly accelerates due diligence by analyzing large data rooms, summarizing documents, and even helping teams anticipate and respond to investment committee questions. At the portfolio level, AI streamlines financial reporting and enables more consistent KPI analysis, which can be critical in performance-driven environments. 

Ultimately, AI supports private equity’s core objective: growing EBITDA through revenue expansion and cost efficiency. By improving speed, insight, and operational performance, it enables funds to move faster on deals, manage portfolio companies more effectively, and maximize returns at exit. 

“In portfolio companies, AI is also being applied to streamline reporting and identify operational inefficiencies more quickly — supporting faster decisions that can improve margins over the hold period.”  

— Ernie Saumell, Private Equity Assurance Principal at BDO 

Manufacturing: AI Potential, Legacy Constraints

Manufacturing combines two opposing realities; abundant operational data and structurally constrained systems- making it both one of the most attractive and one of the most challenging environments for AI deployment. 

Data is generated continuously across production lines, supply chains and logistics networks, and internal inventory and demand planning systems, theoretically creating clear opportunities to improve performance. In theory, this creates ideal conditions for applying AI to improve performance. In practice, however, structural factors weigh against AI adoption: 

• Legacy systems, often decades old 
• Fragmented data across disparate internal and business partner systems 
• Limited integration between operational and analytical platforms 

Despite these challenges, AI use cases in manufacturing are well defined. Some varieties of AI, such as machine learning, have been employed effectively in manufacturing for some time now. Proactive maintenance schedules, for example, have long been informed by real-time telemetry compared to historical insights gathered from large data sets.  

In addition to proactive and predictive maintenance, manufacturers are leveraging AI for: 

• Production scheduling, enhancing workflows based on demand forecasts and available materials 
• Quality control, identifying defects in real time 
• Inventory optimization and supplier risk monitoring 

These applications deliver measurable value but often remain confined to the analytics layer, where AI can provide insights without directly influencing execution. The real opportunity lies in moving AI into the execution layer, where it can drive autonomous or semi-autonomous decision-making. For example, AI could dynamically adjust production schedules based on real-time demand or supply conditions.  

This degree of involvement introduces new challenges around trust, governance, and risk. To unlock this next level of value, manufacturers will have to address foundational issues in data quality and system integration. Larger, more mature manufacturers with good data and properly integrated ERPs have a head start over smaller organizations with poorer data and less developed technology integration. AI’s potential won’t be realized until data and management systems are fully integrated and aligned.

“When a client says, ‘I have a particular problem,’ an AI solution often ends up being one of the better answers.”  

— Ashley Hetrick, Manufacturing Principal, Sourcing and Supply Chain Segment Leader at BDO

Retail: Advanced Adoption and the Challenge of Scale

Retail leads most industries in AI adoption, though the level of maturity varies widely across the sector. The most advanced retailers are scaling AI against defined growth and margin objectives. Others remain limited to isolated pilots, constrained by gaps in data quality, performance measurement, or governance that prevent scaling. 

Retailers hold a structural advantage: point-of-sale, e-commerce, CRM, and loyalty systems generate continuous, real-time data that few other industries can match. This data foundation supports AI at scale, though many organizations have not yet fully capitalized on it. 

AI is already established in several core retail functions: 

• Personalization and recommendation engines, which support customer retention and lifetime value 
• Dynamic pricing and promotion optimization, which improve margin and conversion 
• Demand forecasting and inventory management, which reduce markdowns and stockouts

Predictive analytics for demand forecasting and inventory management, which help retailers anticipate demand shifts, reduce markdowns and stockouts, and make more informed planning decisions 

These applications have a direct, quantifiable effect on revenue, margin, and customer economics. That impact is also driving a new set of challenges. Large retail organizations may operate dozens — or in some cases hundreds — of AI tools and agents across marketing, merchandising, operations, and finance. At this scale, retailers face greater governance complexity, customer data privacy risks, fragmentation across functions, and growing difficulty managing costs as vendors shift toward token- and usage-based pricing models. They must also be able to connect AI outputs and investments to measurable business outcomes. 

The priority for retail organizations is shifting from adoption to optimization. AI initiatives now require stronger governance, tighter integration into existing workflows, and clear alignment with business performance. The competitive advantage is no longer in simply using AI, but in how effectively retailers scale and manage it to drive measurable growth, margin improvement, and operational precision across the enterprise. 

“Retailers are moving quickly from experimentation to scaled use cases, but the ability to measure, govern, and operationalize AI consistently still varies widely across the market.”  

— Natalie Kotlyar, National Retail and Consumer Products Industry Leader

Healthcare: Balancing Innovation with Ethics and Compliance

Healthcare is a sector where the potential benefits of AI are significant, but the constraints are equally substantial. The industry generates vast amounts of data, much of it unstructured, and faces ongoing challenges related to efficiency, cost, and workforce burnout. 

AI is already delivering incremental value in healthcare areas such as: 

• Clinical documentation, speeding the creation of patient records 
• Administrative workflows, reducing manual burden 
• Revenue cycle management, improving billing and collections

These applications are particularly important because they address one of the industry’s most pressing issues: clinician burnout. By reducing administrative workload, AI can allow healthcare professionals to focus more on patient care. Major electronic health records (EHR) providers have begun integrating AI into their platforms in an effort to streamline documentation and workflows.  

Healthcare also presents unique challenges. Healthcare providers operate under strict regulatory requirements, with significant ethical considerations surrounding patient data. Any involvement of AI in decision-making processes must be thoroughly understood, justifiable, and explainable. 

AI systems in this context must be accurate, transparent, and auditable. Medical professionals must be able to understand and trust the recommendations provided by AI, especially when those recommendations influence patient outcomes. This creates a higher bar for adoption. While AI can deliver significant efficiency gains, its integration into clinical decision-making requires careful governance and oversight. 

“If AI can get some administrative tasks off clinicians’ desks, we can slow down the burnout and turnover and refocus on patient care.”  

— Jim Murray, Managing Director, BDO Center for Healthcare Excellence and Innovation

Law Firms: Disrupting Traditional Business Models

In professional services firms — especially legal firms — AI is reshaping the fundamental economics of the industry. These sectors are historically labor-intensive, with value often tied to time-based billing models. AI challenges that paradigm by dramatically reducing the time required to complete many tasks. 

Key applications include: 

• Contract analysis and management 
• Legal research and documentation 
• Drafting briefs 
• Performing due diligence 
• Process design 
• Report generation and data analysis

These capabilities can compress work that once took hours or days into minutes. While this creates obvious efficiency gains, it also introduces a deeper question: How should services be priced when effort is no longer the primary driver of value? 

This shift is forcing organizations to rethink their business models. Firms must move from pricing based on time to pricing based on outcomes or value delivered. Alternative fee arrangements, value-based billing, and contingency models are all evolving with a focus on value-driven, measurable outcomes. 

At the same time, AI is democratizing access to capabilities that were once exclusive to large firms. Smaller organizations can now leverage AI tools like Wordsmith, Harvey, and legal-specific tools within Claude, which enable them to compete at a higher level, reducing traditional barriers to entry. 

Despite these changes, human insight and experience remain critical. AI can handle a significant portion of the workload, but the remaining complexity — interpretation, judgment, quality control, and client interaction — still requires domain knowledge and a human touch.

“Those reluctant to use AI are afraid of losing revenue. The billable hour is under severe attack.” 

— Eric Derk, Managing Director, Forensics and Legal Operations at BDO

Restaurants and Hospitality: Targeted Adoption in Complex Environments

In industries such as food service and hospitality, AI adoption is more fragmented, reflecting the diversity and complexity of operations. These sectors often operate with thin margins, making ROI a critical consideration. 

Rather than large-scale transformation, AI is being applied to targeted use cases, including: 

• Labor scheduling and forecasting 
• Inventory and supply management 
• Fraud and opportunity identification 
• Customer communication and engagement 
• Back-office automation

These applications deliver incremental improvements that can have a meaningful effect on profitability, but adoption is sometimes constrained by fragmented data systems, limited technology infrastructure, or resource constraints. Public reception is also a factor. Automation in ordering systems can reduce headcount, for example, but risks alienating customers.  

In this context, AI adoption tends to be pragmatic. Organizations focus on low-risk, high-return opportunities, building momentum through incremental wins rather than large-scale transformation. 

“Everyone’s trying little things, wanting to know what other companies are doing, ultimately searching for a path to engage their guests and drive EBITDA.” 

— Meg Potts, Co-Lead, BDO National Restaurant Industry Group

Technology: Enterprise Transformation, Governance, and Scale

Across the technology sector, companies are adopting AI as a core contributor to performance. Leading firms are using AI to accelerate innovation, increase productivity, and enhance customer experiences, even as they continue to refine their long-term AI strategies. 

Many AI applications are especially relevant to the technology space, such as:  

• Applications that can organize or improve incomplete or messy data sets 
• Tools that replace traditional GUIs with more intuitive, conversational interfaces 
• Agents that coordinate and consolidate diverse intelligence streams  

Software companies are embedding AI into their products to meet evolving customer expectations, while also using it internally to speed development and streamline operations. AI-assisted coding tools, automated testing, and intelligent workflows help development teams deliver products quickly. 

Telecom and media companies are also seeing tangible benefits. AI-powered customer service agents improve response times and customer satisfaction, while recommendation engines, localization tools, and content personalization capabilities transform how consumers discover and engage with digital content.  

Hardware manufacturers are leveraging AI to automate engineering processes, generate material specifications, inform inventory decisions, and support production planning. Emerging AI-enabled robotics and intelligent design tools are further expanding opportunities to increase efficiency and reduce manual effort. 

The biggest potential for disruption is more fundamental: Instead of embedding AI into existing tasks and workflows, forward-thinking companies are beginning to re-examine underlying assumptions about the ways processes and organizations are structured. As we gain confidence in AI’s ability to analyze, decide, and execute, traditional roles and operating models may evolve significantly. 

Technology innovators that pair experimentation with thoughtful governance, change management, and business transformation will be best positioned to turn AI investment into measurable, sustainable value. 

“There’s a misconception that you need to fix your data before you touch AI, but that’s not true. One advantage of AI is that it can look at messy, incomplete data and help you organize it.”  

— Kasra Mojtahedi, Principal and Market Leader, Technology and Transformation at BDO

A Spectrum of Maturity

Taken together, these industry perspectives reveal a key insight: AI adoption is not uniform. It exists on a spectrum, influenced by data maturity, regulatory complexity, and competitive dynamics. 

• Retail and financial services: moving toward scaled, enterprise-wide deployment 
• Manufacturing and healthcare: significant potential, constrained by infrastructure and governance 
• Professional services: rapid disruption of traditional models 
• Mid-market industries: targeted, incremental adoption

Differences aside, there are also common patterns. Across industries, AI delivers real value when it aligns with core business processes, has strong data foundations, and integrates into established workflows. 

Industry context shapes opportunity, but the underlying principle remains the same: AI accelerates business when it serves a strategic purpose.