Is AI always the Right Direction for an IT Project? Considerations and Choice Criteria.

Most of us marvel at generative AI tools that can create text, graphics, or video in seconds. Software developers employ code snippet creators to make their work easier. AI is on the perfect path to doing much of the work for humans.

According to the Stanford HAI report, AI systems have already outperformed people in visual reasoning, image classification, or basic-level reading comprehension. Yet, humans still prevail in many advanced activities, such as competition-level mathematics.

In 2023, investments in expanding the role of artificial intelligence totaled more than $25 billion, but as intelligent solutions become more widespread, so do the costs of maintaining and training them.

The functioning of AI requires more and more resources every year and generates a massive carbon footprint (GPT-3 produced the equivalent of 500 tons of CO₂ between 2020 and 2023). A significant issue is the lack of transparency of AI models, which can cause serious trouble, especially in regulated industries.

How can your company use AI to perform better and leave the competition behind? Check out the most popular roles of AI and Machine Learning in business.

ChatGPT, Dall-E, Copilot, and other AI applications have revolutionized the approach to content creation. We no longer need advanced copywriting, graphic design, or programming skills to produce basic social media content or a piece of code to simplify our daily tasks.

Generative AI creates output based on input and training data. LLM models speed up creative tasks and reduce their costs. With precisely written prompts, employees can get a personalized marketing strategy, social media graphics, or product descriptions in seconds.

Despite its many advantages, LLM models are imperfect, and output is not always appropriate for publication on a company’s website or in an offer. There is also the debatable issue of profiting from AI-generated content and its copyright.

Another application of the technology is discriminative AI, which uses training data or input for:

• data classification,
• content recognition (facial/image/voice),
• trends forecasting,
• credit scoring,
• spam filtering,
• user analytics and segmentation

and similar tasks.

Discriminative AI often works in opposition to generative AI, finding and eliminating harmful content generated by fraudsters or social engineering organizations. It usually achieves satisfying results in identifying false or mismatched information, but rarely with 100% accuracy.

Discriminative AI works best with specific, well-defined scenarios. Going beyond training patterns can create errors that reduce the solution’s effectiveness.

AI is a great business support. 26% of companies interviewed by McKinsey reported that using AI in software development has allowed them to automate the first line of contact with customers. 23% have increased content personalization with GenAI, and 22% leverage AI for customer acquisition.

But will artificial intelligence do the job for any task? Let’s examine the benefits of using AI and its alternative, traditional algorithms.

AI-based solutions are particularly effective in performing large-scale data processing tasks. These include detecting patterns, making predictions, and rapid data analysis. Integrating AI into the above business processes can drastically increase productivity and efficiency.

AI algorithms can quickly adapt to rapidly rising computing power and data volume, but any increase must be carefully supervised. You should be aware of errors or biases that can accumulate and produce incorrect or skewed results.

Despite the growing popularity of AI in software engineering, classical algorithms are unbeatable in some cases. They are better suited to structured, rule-based environments where consistency, reliability, and 100-percent accuracy are crucial. Due to their transparency, they also cause fewer regulatory problems.

Non-AI software algorithms are typically less complicated to implement and cheaper to maintain. As there are established processes for implementing and running them, it is easy to integrate them with other systems and run on legacy infrastructure. To operate non-AI systems, you don’t need to hire AI experts, who are hard to find (and retain).

How do you decide if your solution or business needs to be AI-powered? There are many aspects to consider, so let’s go over them in more detail.

First, review whether implementing AI can help you meet a business need, such as large-scale data analysis or manual process automation. Could it bring significant business benefits? One way to answer this is to decide whether you need AI for your core business functions or want it as an add-on to existing services.

As you consider integrating AI into your business, you need to be sure that this solution will pay off in the long term. There will be significant costs to bear at the start, and with the need for constant development, they may continue for a while. However, new functionalities and business advantages might be worth the initial expense.

This is a critical consideration for your AI model. AI is not always 100% accurate. How important is it for your business to have precise results every time?

For example, customer service chatbots or virtual assistants can successfully operate without achieving 100 percent accuracy. Plus, this lower precision level allows the algorithm to adapt to changing conditions and continually learn.

However, in biotechnology, medicine, or finance, you must consider only solutions with the highest accuracy. If the AI model can’t provide adequate compliance, user safety, or financial data, it’s not the right choice.

Before you hire developers to focus on AI development, perform an audit of your company’s infrastructure. Verify if:

• your IT environment supports an AI solution in terms of storage and computing;
• your current systems and applications are compatible with AI;
• your data are in the amount and quality required by selected AI model(s);
• you keep the data in a format that AI can process.

AI technologies require a robust infrastructure, including high computing power and extensive data storage capabilities. Many small and medium-sized companies may not have the financial resources, equipment, or processes to invest in new infrastructure. Traditional approaches often require less expertise and are more forgiving in terms of resources, so they may be an adequate starting point if your organization is not yet AI-ready.

Note also that successful AI implementation often depends on the availability of large data sets and the ability to provide data into AI systems for training and improvement. Migrating data to artificial intelligence-compatible formats can be challenging, especially if the data is incomplete or in non-digital formats.

You need a skilled development team to grow your business consistently based on artificial intelligence. But does your company have people with the expertise to implement and manage AI solutions? This is not just AI but also business expertise, so the team can train the model precisely to meet your organization’s requirements. Designing, training, and maintaining effective artificial intelligence systems is crucial.

AI experts are now highly valued in the job market. Organizations hiring these professionals must consider strategies for retaining them and plans for securing and transferring knowledge in case of leaving.

47% of respondents interviewed by Deloitte say they intend to implement dedicated strategies for upskilling and reskilling employees in AI skills. While this may be a good workaround to the talent gap, you should still think of people and knowledge retention strategies, as well as continuous learning and growth as AI technologies evolve.

In addition to verifying financial and technical readiness, you must prepare your employees for the technological transformation before introducing artificial intelligence-based solutions. Due to conflicting information circulating in mainstream media, AI causes reluctance and skepticism in many people.

This may lead to resistance from employees used to traditional work methods. People fear that modern technology will eliminate their jobs or indirectly make them adapt too slowly to a different way of doing things.

Strong change management practices, ongoing communication, and training will go a long way toward getting your people on board with the new products or services.

Budget is a condition that can make or break your entire project. Before you start your estimates, analyze your requirements and research whether a model you need already exists on the market. Using an existing solution will be much cheaper than building a custom model – and starting from scratch doesn’t guarantee that the finished solution will meet specific needs.

This initial setup is not all that goes into the cost calculation. The total cost of ownership (TCO) includes data management, model training, computing capacity, system upgrades, and employee education. As the technology evolves, it will require updates and maintenance, while your experts will need ongoing training. You need to assess if the potential advantages justify this investment. If not, maybe AI is something best left for another time.

Alternatively, you can start with traditional algorithms. They aren’t as costly to implement and maintain and can help you secure the necessary return on investment. Once this solution pays for itself and brings more value, you can consider expanding it with AI-based components.

Whether or not you should pursue AI depends on many factors. Just because it’s the latest hype, it may not necessarily bring the value you expect. Depending on your organization’s needs and capabilities, you may be able to get the results you need with traditional algorithms. Stay tuned for my next post, where I will demonstrate how our team just did that for one of our clients.

I hope that the above criteria will help you make the right decision. By carefully weighing all the pros and cons, it will be easier to avoid potential risks and note profits sooner. If you are looking for professional support and want to know if AI has the potential in your company, don’t hesitate to contact Scalo experts!