Artificial Intelligence

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





Artificial Intelligence


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

Published: 2023/10/20

6 min read

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 (an increase from $8.65 billion USD in 2022), 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

McKinsey reports that 60% of companies who have adopted AI are already using generative AI regularly. Generative AI has 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.

One of the recommended AI tools 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 recommed 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.

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.

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.

About the authorDamian Mazurek

Chief Innovation Officer

A certified cloud architect and AI expert with over 15 years’ experience in the software industry, Damian has spent the last several years as a cloud and AI consultant. In his current role he oversees the technology strategy and operations, while working with clients to design and implement scalable and effective cloud solutions and AI tools. Damian’s cloud, data and machine learning expertise has enabled him to help numerous organizations leverage these technologies to improve operations and drive business growth.

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