Taming AI. How to take the first steps to apply AI in your business? | AI in business #119

How easy is taming AI in a company? Introduction

Although Artificial Intelligence (AI) is gaining popularity among businesses in Poland, there are still many companies that are not fully exploiting its potential. According to a KPMG study (https://kpmg.com/pl/pl/home/media/press-releases/2023/07/media-press-sztuczna-inteligencja-w-firmach-w-polsce-potencjal-do-wykorzystania.html), only 15% of companies in our country currently use AI solutions, while the global average is 35-37%. At the same time, up to 62% of companies that have implemented AI do not monitor the effectiveness of these implementations – i.e. they do not know what impact, if any, they have had.

These figures show the huge untapped potential of artificial intelligence in Polish business. On the other hand, 13% of companies planned to implement AI by the end of 2023, which could be a sign of the coming wave of adoption of this disruptive technology. Indeed, companies see numerous benefits from AI, such as increased productivity, improved product and service quality, better financial performance and a strengthened competitive position.

Step 1. Understand the difference between AI, machine learning and generative artificial intelligence

If you are considering taking the first step towards implementing AI in your business, it is worth learning the basics of this group of technologies. Before you can realize the potential of AI in your business, you need to understand the key difference between Artificial Intelligence (AI) in its broadest sense, Machine Learning (ML) and Generative AI. These terms are often used interchangeably, but they actually describe slightly different concepts.

AI refers to the general ability of programmed machines, such as computers or robots, to ‘think’ in a similar way to humans and to mimic intelligent behavior. AI systems can assimilate, analyze and use knowledge from the real world to derive new information. Examples of AI-based technologies include speech, image and facial recognition.

On the other hand machine learning (ML) is a field of AI in which computer systems learn from data and make decisions without direct human intervention. A key feature of ML is the ability to continuously self-improve and adapt algorithms based on new input data.

With the rapid development of generative AI, the main sign of which is the crazy popularity of ChatGPT, it is also important to understand this new trend. Generative AI is capable of generating new data, such as text, images, video and audio, or even computer code. It does this by learning from large amounts of training data. Language models, such as ChatGPT, learn the patterns and rules inherent in the input data and then use this knowledge to create new, unique texts that resemble those written by humans.

The power of generative AI lies in its flexibility and ability to creatively remix and synthesize information in innovative ways.

Define business needs

The second step is to identify the specific needs of your business that can be met by implementing artificial intelligence and machine learning. This process starts with an in-depth analysis and careful consideration of several questions:

1. What specific results do you want to achieve? It could be increased revenue, optimisation of the supply chain or better customer service.
2. What are the main obstacles to achieving these goals?
3. How can AI and machine learning help you overcome them?
4. How do you want to measure the success of such an initiative? It is worth planning from the outset how the results will be evaluated, especially given how many companies skip this key step. This can be based on KPIs, direct financial gains or other metrics defined specifically for this implementation.
5. What kind of data do you already have? Data is a key resource that a company’s newly implemented AI will use. Ask yourself, what additional data will you need to harness the full potential of AI?

To fully understand the value of answering these questions, let’s look at a practical example. Imagine a small accounting firm that was struggling with lengthy, manual processes for handling client documents. They defined their goal as “to automate accounting to speed up processing and increase productivity”.

The main obstacles were the time spent on tedious tasks and the large volumes of documents that needed to be processed. After reviewing these challenges, the team identified AI-based document processing as a potential solution – Natural Language Processing (NLP) technology that could automatically extract and categorize relevant financial data, reduce errors and speed up processes.

Ways to measure the impact were, in this case, an increase in the number of documents processed per month and a reduction in the average processing time per order. It was also important to assess the data resources – in this case, the volume of receipts, invoices and other financial documents needed to train the AI systems.

This example illustrates the importance of clearly defining your business needs at the beginning of the AI implementation process. Only in this way can you identify the right solutions and implement them properly to deliver maximum value to your business.

Source: DALL·E 3, prompt: Marta M. Kania (https://www.linkedin.com/in/martamatyldakania/)

It is worth reaching out to tools such as SensID Cognitive Automation (https://4semantics.pl/produkty/sensid-cognitive-automation/), Microsoft AI Builder (https://learn.microsoft.com/pl-pl/ai-builder/overview) or Docsumo (https://www.docsumo.com/).

SensID Cognitive Automation uses Natural Language Processing (NLP) technology to automate the understanding of document content, which is key to robotic tasks and decision-making processes. Once the text has been analyzed, the system aggregates the collected data and presents it in a structured form, ready for use in robotic process automation (RPA) and analytics applications. With the technology we have developed, it is possible to efficiently create models that interpret the information contained in a wide variety of business documents.

SensID Cognitive Automation enables the integration of data from a variety of textual sources, including structured data (such as databases), semi-structured data (such as forms, csv, html, etc.) and unstructured data (such as doc, pdf, etc.), providing a unified view of information.

Microsoft AI Builder is part of the Microsoft Power Platform. With it, you can create and use AI models to help optimize your business processes. You can use a pre-built model that is ready for many common business scenarios, such as document recognition, or create a custom model to meet your company’s specific requirements.

Another option worth trying is Docsumo which uses OCR (Optical Character Recognition) to read documents and is trusted by major companies such as PayU and Hitachi.

Step 3. Find out how AI can help your business

After identifying your business goals and challenges, the next logical step is to identify the specific ways in which AI can add value and profit to your business. Sometimes the path may not be obvious, so consider the wide range of possible benefits.

One of the key value factors of AI is to increase the value delivered to customers. With the power of machine learning and advanced data analytics, AI can help companies better understand consumer preferences and behavior. This allows for a more personalized and satisfying shopping experience.

Another key factor is AI’s potential to increase employee efficiency and productivity. By automating repetitive, time-consuming tasks, AI can deliver significant cost savings and allow teams to focus on more strategic, creative activities, as well as significantly improve job satisfaction. In fact, 59% of those working in management roles believe that the use of AI in the workplace improves job satisfaction (https://www.thehrdirector.com/business-news/ai/ai-increase-job-satisfaction/).

Finally, we should not forget the direct business gains that often result from implementing AI solutions. By optimizing processes, improving operations and making better use of data, organizations can maximize revenues and profits.

So will AI increase your customers’ satisfaction? Will it maximize employee productivity? Will it contribute to revenue growth? If the answer to any of these questions is “yes”, then AI certainly deserves your attention.

Source: DALL·E 3, prompt: Marta M. Kania (https://www.linkedin.com/in/martamatyldakania/)

Step 4. Assess your own capabilities to implement AI

With an understanding of the huge potential of AI, you now face the biggest challenge – assessing and preparing your own organizational capabilities and resources to effectively implement new technologies. Unfortunately, there is often a significant gap between what we want to achieve and what we can actually deliver within a given time and budget.

If you see numerous opportunities to use AI in your company, you need to start with an honest assessment of your competences and tools. Ask your IT professionals to answer the following questions honestly:

• Do we have an in-house development team with the right skills to build a bespoke AI solution from scratch?
• If not, should we consider buying an off-the-shelf AI product offered by external suppliers?
• Or would it be more cost effective to strategically engage with an experienced external partner to jointly develop a solution tailored to our needs?

Due to a lack of internal resources, the best solution may be to outsource your AI implementation project entirely to a specialized external company. Whichever path you choose, a good first step is to thoroughly research the AI solutions available on the market and assess whether any of them could meet your organization’s current needs. Buying an off-the-shelf product may well be a more cost-effective option than building from scratch.

Remember that AI integration is different from a typical new software implementation. It requires expertise in machine learning, big data processing and advanced algorithms. If your organization doesn’t have this expertise, working with external specialists may be unavoidable to maximize the project’s chances of success.

Step 5. Consider consulting a specialist

Despite the enthusiasm for AI technology, many managers are still afraid to take the first steps due to a lack of skills within their organization. If you are one of them, consider bringing in a specialist consultant or external company.

Building AI systems is significantly different from developing typical business applications. It is a highly specialized area of expertise, requiring advanced skills in machine learning, natural language processing, deep learning and big data analysis.

For example, creating an AI virtual assistant that can effectively communicate with customers requires not only a solid full-stack foundation, but also natural language processing technology and generative artificial intelligence.

If your team lacks such specialized skills, it may make more sense to seek outside assistance. Specialized AI consulting firms and agencies can provide not only relevant expertise and experience, but also proven processes and best practices to increase the chances of success for your initiatives.

Of course, hiring external experts comes at an additional cost. However, it is important to remember that improper implementation of AI can lead to even greater financial losses due to errors, downtime, and the need for corrections. Or simply a malfunction of the entire system, which will not perform the tasks for which it was created. That’s why working with specialists is often a wise investment that can save you time and money in the long run.

Source: DALL·E 3, prompt: Marta M. Kania (https://www.linkedin.com/in/martamatyldakania/)

Taming AI – summary

Implementing artificial intelligence in a company is undoubtedly a serious and challenging undertaking, but it is also a huge opportunity for business transformation and growth. It opens the door to countless opportunities to increase efficiency, optimize processes and deliver greater value to customers.

As we’ve already seen, many companies around the world – from small businesses to large enterprises – are successfully using AI to automate tedious tasks, analyze large data sets, and make better decisions based on facts.

Of course, as with any serious business initiative, the path to a successful AI implementation is detailed planning and adherence to proven principles.

Implementing AI is an iterative process. That’s why it’s best to start with a small pilot project, run tests, and gather feedback. Based on this, it will be easier to make decisions about further development or adjustments. Also, don’t forget a key success factor – data. The more quality data you feed your AI systems with, the better they will learn and perform.

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