What You Need to Know About Implementing AI in Business Operations to Achieve Long-term Results

The intense rise of interest in artificial intelligence (AI) is not just a temporary phenomenon. The generative AI market is projected to reach $188.62 billion USD globally by 2032 growing at a compound annual growth rate (CAGR) of 36.10%. Businesses are looking into this technology because AI offers them a boost to efficiency, productivity and customer experience – but the road to successful implementation and sustained benefits might not be straightforward without sufficient expertise. Read on to learn best practices for implementing AI in your company and find out which business areas might be good places to start your AI journey.

AI can be applied to a wide range of business processes

According to McKinsey’s latest report, The State of AI, organizations are developing the structures and processes that will lead to significant value from GenAI. Generative AI is also motivated 40% of companies who adopted this technology to invest more in AI in general. Where and how businesses choose to apply these tools depends on their long-term goals, growth strategy and resources. However, when you’re trying to identify specific processes, it helps to focus on the value AI can provide and start with smaller, less complex tasks.

The potential for different AI use cases is high in many organizations across industries because this technology is fit to support various departments – from customer service through research and development (R&D) to finance and accounting. AI solutions are well-suited to handle tasks that involve:

• analyzing big data sets,
• generating reports and insights,
• providing summaries and recommendations,
• personalizing content,
• detecting patterns and anomalies.

When it comes to data analysis, you can use AI to examine customer behavior, identify trends and screen datasets for specific criteria. Enhancing these processes enables you to drive your decisions with in-depth analytics and insights backed by data. For example, in finance, this might include assessing risk and generating financial scenarios to optimize investments. Or in human resources (HR) departments, teams could get additional help in analyzing long-form feedback and data-based metrics for performance reviews.

AI can also help you with data monitoring. For instance, it could boost legal teams by checking for regulation updates to ensure constant compliance. In software development, AI solutions can be used for identifying and diagnosing software bugs to maintain high code quality. Companies in the financial services sector might be interested in applying AI to track and detect suspicious and fraudulent activity. These capabilities of AI tools can strengthen the security of your processes, products and customers.

Conversations about generative AI and large language models (LLMs) often focus on this technology’s capacity to generate text. Businesses can leverage this to summarize long documents, extract key insights and create high-level overviews for faster decision-making. For example, AI can support employee onboarding by providing key information about the company, its policies and benefits. It could also boost R&D processes by reviewing and summarizing academic literature so that employees can easily stay updated on the latest industry trends and developments. You could also use LLMs to automate the generation of documents like legal drafts, software documentation and financial reports.

Read more:  How Artificial Intelligence Transforms Business Decisions

Generative AI business use cases

Generative AI has quickly progressed from theoretical potential to delivering concrete, industry-specific results. Companies now show significant returns on their AI implementations, highlighting tangible benefits beyond initial experimentation.

Finance

According to Reuters, financial giant Morgan Stanley has deployed a custom generative AI assistant, powered by OpenAI, to its 16,000 wealth managers. The tool instantly sifts through and synthesizes hundreds of thousands of market research documents and investment reports. This allows advisors to find answers to complex client questions in seconds rather than hours, freeing them up to focus on strategy and client relationships, thereby boosting productivity and the quality of financial advice.

Manufacturing & engineering

Through a press release, Siemens announced that it has partnered with NVIDIA to utilize generative AI to create photorealistic, physics-based digital twins of factories. This technology enables engineers to design, simulate, and optimize entire production lines and robotic workflows in a virtual environment before investing in expensive physical construction. As a result, factory design has become faster, planning errors have been minimized, and energy consumption has been optimized. One notable client, HD Hyundai, successfully employed this technology to design a new ship engine manufacturing plant.

Biotechnology

Nature Biotechnology reported that Insilico Medicine, based in Hong Kong, has achieved a significant milestone. Their generative AI platform has identified a novel drug candidate for idiopathic pulmonary fibrosis (IPF), a severe lung disease. This drug, named INS018_055, has now entered Phase II clinical trials. By utilizing AI to design a new molecule from scratch, Insilico significantly reduced the initial discovery time from years to 18 months. Such an advancement demonstrates a remarkable acceleration in pharmaceutical research and development, offering new hope for patients.

Sports betting

In the sports betting industry, data and technology companies use generative AI to quickly convert raw, real-time game data into engaging narratives and insights. Their AI models analyze live data feeds as sporting events progress to produce human-like commentary, player performance updates, and predictive analytics. This content is then distributed directly to their media and sportsbook partners, such as the NFL, DraftKings, and FanDuel. The main advantage is the ability to automate and personalize content on a large scale, providing partners with a continuous stream of data-driven material that enhances fan interaction and informs betting decisions, without requiring a team of human analysts to cover every single game.

GitHub Pilot – AI tool for enhancing software delivery processes

As some of the mentioned examples show, AI is also used to boost software development. A good example is GitHub Copilot, an AI-powered pair programming tool that suggests code based on plain language prompts or provided code. Released in 2021 by GitHub and OpenAI, it has already proven beneficial to development teams. According to GitHub’s research, 88% of surveyed developers who used GitHub Copilot felt more productive, and 96% reported they worked faster on repetitive tasks.

This tool is managed through personal accounts with GitHub Copilot for Individuals or through organizational accounts with GitHub Copilot for Business. Both versions require paid subscriptions. To start using GitHub Copilot, you need to install the extension it belongs to into your desired programming environment, log in with your GitHub account and start coding. GitHub Copilot offers suggestions for a wide range of programming languages and frameworks, focusing on Python, Ruby, Go, JavaScript, TypeScript, C# and C++.

GitHub Copilot supports engineers by suggesting relevant code snippets based on the context of what they’re working on. By presenting the most suitable solutions, it saves time and helps ensure that the final code is accurate and efficient. For example, it can convert code comments into runnable code and autocomplete repetitive sections, methods and functions. Additionally, this tool adapts to a developers’ coding style, detects patterns in how they code and continuously learns from their feedback – adjusting recommendations to better match coding preferences. You can accept the tool’s suggestions, keep typing to see more ideas or provide more context to help the AI generate more relevant recommendations.

Keep in mind that GitHub Copilot’s creators clearly state that it’s the responsibility of programmers and their teams to ensure the security and quality of their code. As such, they recommend taking the same precautions when using code generated by GitHub Copilot that developers would if they used code they didn’t write themselves. This is a recurring disclaimer with every AI tool that provides real-time programming assistance.

The latest AI-coding tools worth knowing

Various companies have introduced AI code assistants to streamline programmers’ workflows and boost their coding efficiency. There are a few industry leaders worth mentioning.

Claude Code

Claude Code is Anthropic’s specialized AI model designed for programming tasks. Integrated within the Claude ecosystem, it generates, explains, and optimizes code. With its large context window, Claude Code can understand entire codebases, offering more accurate and contextually aware assistance for complex software development challenges.

Cursor

Cursor is an AI-first code editor designed for pair programming with artificial intelligence. It allows developers to edit, debug, and generate code by chatting with an AI with full project file context. It enhances productivity with features like codebase-wide questions and AI-assisted code generation.

Gemini

Gemini is a powerful multimodal AI model developed by Google. It can generate, explain, and debug code in a variety of programming languages. Integrated into services like Google AI Studio and other developer tools, Gemini utilizes sophisticated reasoning to assist with tasks ranging from simple scripts to complex algorithms.

Windsurf

Windsurf Editor is widely known as the first AI agent-powered IDE. It moves beyond simple code completion by using autonomous AI agents to handle complex, multi-step tasks. Developers can delegate high-level objectives, such as building a new feature or refactoring code, letting the agent execute the entire workflow.

You can find a detailed analysis of other AI tools in our white paper, Tools, Tips and Tactics for Implementing AI in Your Business.

Proven strategies for incorporating AI in processes and teams

Taking an organized approach to implementing AI is key to effective deployment and long-term advantages. Thoughtful preparations will increase the chances of success of a project and – in the long run – enable you to turn a proof of concept (PoC) into organization-wide adoption. Here are some best practices worth following when you’re planning to implement AI in your company.

Identify the right use cases and tools. Before deciding on the right AI tool for your organization, it is essential to assess your business operations to pinpoint areas where AI can add value. Additionally, you should make sure to choose an LLM that suits your specific needs. The process of selecting an AI tool should take into account factors such as task complexity, model capabilities and resource requirements.

Prepare data and fine-tune models. Gathering and preprocessing relevant data to refine your chosen AI tool ensures it aligns with your business goals and produces accurate, domain-specific results. Fine-tuning maximizes an AI model’s support by delivering higher quality results than those produced from prompting.

Monitor and evaluate performance. Establishing measurable and quantifiable key performance indicators (KPIs) is essential to ensuring your AI investment pays dividends. Continuously track the performance of implemented AI, using metrics such as accuracy, response time and user satisfaction to identify areas for improvement.

Address ethical and privacy considerations. Be mindful of potential ethical and privacy concerns related to AI deployment, ensure compliance with data protection regulations and responsibly use AI technologies. This means being transparent about data – how you collect it and what you intend to do with it – and regularly auditing AI technologies for bias and discriminating tendencies.

Read also: Generative AI vs Large Language Models

Plan for scalability and maintenance. AI is developing at a rapid rate and transforming how organizations operate almost as fast, which makes the task of designing a strategy that effectively keeps pace with AI’s growth difficult. To overcome these challenges, organizations should invest in their data strategies, as data is the fuel that drives an AI engine.

Treat AI as an assistant, not a replacement. AI capabilities are increasing on a weekly, if not daily, basis. However, AI should be regarded as a way to augment human efforts, not eliminate them. Though the proven abilities of AI, especially as regards automation and content creation, are clear, there are still limitations. Currently, it’s worth remembering that even advanced AI possesses a limited understanding of context, lacks emotional intelligence, has inadequate creativity and may perpetuate biases that exist in the information and data it is trained on.

Scaling AI projects within the organization

The industry knows that the “Just add AI to it” strategy will not work. AI-related projects must be thought out thoroughly and scaled according to the company’s needs.

Changes in MLOps

Modern MLOps has evolved significantly beyond the simple deployment of models into production. It now involves continuously monitoring and managing AI assets to ensure they deliver long-term value while minimizing risks. Current strategies focus on automated monitoring to detect performance degradation, data drift, and concept drift. When a model’s effectiveness declines, this system triggers alerts or initiates automated retraining pipelines.

This proactive approach includes robust governance frameworks that manage model versions, track lineage, and continuously assess ethical considerations such as fairness and bias. Today’s MLOps aims to create a resilient, self-healing AI ecosystem where models remain accurate, compliant, and trustworthy throughout their entire lifecycle.

Return on investment (ROI) for AI

Determining the ROI for AI requires looking past technical performance metrics and focusing on tangible business outcomes. Leading organizations are developing sophisticated methodologies that tie AI implementations directly to KPIs. This involves quantifying improvements in operational efficiency, such as cost reductions from automated processes or time saved on manual tasks. It also means tracking revenue generation through AI-driven personalization engines or enhanced sales forecasting. By creating a clear framework connecting model outputs to financial results and strategic advantages, businesses can justify continued investment and ensure their AI initiatives deliver measurable, meaningful value.

AI’s ethical and regulatory considerations

Landmark regulations like EU AI Act are establishing a new global standard, shifting the landscape from friendly recommendations to mandatory compliance. This legislation introduces a risk-based approach, categorizing AI systems based on their potential for harm. High-risk applications, such as those in hiring or finance, now face strict requirements concerning data governance, transparency, and the necessity of human oversight. This means businesses must be able to prove their AI systems are fair, safe, and accountable by design.

Compliance is not merely about avoiding the significant fines associated with these new laws but about building foundational trust with your customers, partners, and the public. When users are confident that their data is protected and that AI-driven decisions are made ethically, they are far more likely to adopt and engage with your services. This trust becomes a decisive competitive advantage.

Embedding these legal and ethical principles into an AI strategy from the outset ensures sustainable growth, mitigates reputational damage, and future-proofs your operations against an evolving regulatory environment. Ultimately, responsible AI should not be viewed as a barrier to innovation but as a critical enabler of its long-term acceptance and profitability in the marketplace.

Leverage AI to boost efficiency and create new revenue streams

The impact of AI technology on different industries is hard to ignore. By getting started on implementation early, businesses boost their chances of achieving better results and staying ahead of their competition. In fact, it is believed that companies have only 3 to 5 years to adapt to the solutions offered by the new wave of AI if they want to remain competitive in their market. That’s why more and more organizations are looking for software partners with proven AI expertise like Software Mind to consistently design, deliver and maintain innovative solutions.

To learn more about effectively adopting AI, download our new white paper, Tools, Tips and Tactics for Implementing AI in Your Business and get the full list of best practices and recommended tools, as well as examples of real-world AI use cases.

FAQ

How is AI used in business operations?

AI is rapidly transforming various business operations. In customer service, AI-powered chatbots and virtual assistants offer round-the-clock support, while machine learning algorithms personalize customer experiences and product recommendations. Marketing and sales teams use AI for predictive analytics to forecast trends, precisely target advertising, and optimize pricing strategies. The latest LLMs optimize inventory management and streamline logistics in the supply chain, leading to enhanced efficiency and cost reductions. Human resources departments are implementing AI to automate repetitive tasks, streamline the recruitment process by screening candidates, and tailor employee training programs. Additionally, in finance, AI algorithms are critical in detecting fraudulent transactions, assessing credit risk, and automating accounting processes. By integrating AI into core business functions, companies can substantially improve productivity, decision-making, and overall competitiveness. This technological integration enables employees to focus on more strategic and creative tasks, fostering innovation and growth.

Can you do operations with AI?

AI is fundamentally designed to perform a wide range of operations, from complex corporate operations to intricate physical procedures. In the business context, AI automates and optimizes functions such as supply chain management, customer service through chatbots, and personalized marketing campaigns by analyzing vast amounts of data. Beyond the corporate sphere, AI’s role is increasingly important in other critical fields. For example, it is becoming integral to surgical procedures, where it enhances a surgeon’s precision by guiding robotic arms and providing real-time analysis of medical images during operations. At its core, AI operates by performing numerous mathematical and logical calculations every second. This computational power enables AI to process language, recognize patterns, and make predictions. From managing a global logistics network to assisting in delicate surgeries, AI’s ability to execute operations is a transformative force that drives efficiency and innovation across various industries.

What is AI in operations management?

AI in operations management refers to integrating artificial intelligence technologies to enhance and automate the core processes of a business. It involves using intelligent systems to plan, organize, and supervise the production of goods and services more effectively. The primary goal is leveraging AI’s analytical power to create more efficient, resilient, data-driven operational strategies. This is achieved through capabilities like machine learning for predictive forecasting, optimization algorithms for scheduling and logistics, and computer vision for quality control on production lines. By analyzing vast amounts of real-time data, AI can identify patterns, predict disruptions, and recommend optimal actions that would be impossible for humans to compute. For instance, it enables predictive maintenance to prevent equipment failure, optimizes inventory levels to reduce waste, and streamlines supply chain routes to cut costs and delivery times. AI is an intelligent layer that boosts productivity and empowers managers to make smarter, more proactive decisions, transforming traditional operations into highly responsive and efficient systems.

What is a key benefit of using AI in business operations?

A key benefit of using artificial intelligence in business operations is the significant increase in efficiency and productivity. AI excels at automating repetitive, time-consuming tasks that traditionally require human intervention. This automation spans various functions, from data entry and report generation in administrative roles to managing inventory levels and optimizing logistics in supply chain management. By delegating these routine processes to AI, businesses can minimize the risk of human error, ensure tasks are completed around the clock, and accelerate overall workflows. This operational efficiency directly translates into substantial cost savings and allows for the strategic reallocation of human talent. When freed from mundane duties, employees can focus on more complex, creative, and strategic initiatives that drive innovation and growth. For example, customer service agents can handle more challenging inquiries while AI chatbots manage common questions. Ultimately, this enhanced efficiency allows organizations to scale their operations more effectively, respond faster to market changes, and gain a significant competitive advantage by doing more with greater speed and precision.