Harnessing Digital Transformation with AI

The artificial intelligence revolution has created thousands of opportunities for businesses to reshape their operations and boost efficiency. In fact, research analysts at Nielsen recently found that AI tools could increase employee productivity by a staggering 66%.

In some ways, this is nothing new: digital transformation (the process of adopting new technologies to improve efficiency) has been around for decades. However, the popular use of AI in digital transformation has changed the current situation dramatically. Companies can now enjoy significant benefits from digital transformation projects much faster by utilising AI tools that are easy to use and applicable in a broad range of industries and situations.

Wondering how your company can take advantage of AI automation for your digital transformation projects? This guide can help you get started. Below, we’ll examine a range of topics related to AI in digital transformation, including:

Examples of the AI tools that modern businesses are using.
How to effectively teach an AI to use datasets.
The role of AI in decision making.
Regulations relating to the implementation of AI tools in the workplace.
The four key steps to successfully implementing AI in your next digital transformation project.

Read on to discover more about the power of AI, and how it can reshape your business operations.

Artificial Intelligence Tools in Business

Although there has been some level of AI usage in various industries since the late 2010s, artificial intelligence truly entered the public’s consciousness (and the business world) in 2022, with the release of ChatGPT from OpenAI.

Generative AI tools like ChatGPT have been consistently marketed as having a wide number of use cases for companies of all sizes, making them an attractive prospect for executives looking to improve their digital transformation projects.

And many businesses are already taking the plunge. According to a 2024 study by business consultants and analysts McKinsey, 65% of businesses are actively using AI in some capacity. Using AI in digital transformations means employing features like machine learning and natural language processing to help with:

Automating repetitive manual tasks.
Generating code to support development teams.
Providing data-driven insights to support executive decision-making.
Building a personalised customer experience and powering intelligent chatbots.

Examples of AI Automation Tools

Below, we’ve provided some more specific examples of AI tools that businesses are implementing during their digital transformation projects, alongside relevant use cases.

ChatGPT: 97% of business owners believe ChatGPT will help their business, according to a study by Forbes Advisor, which is likely because the generative AI tool has a very wide range of uses. But unlike some of the other tools below, which are examples of AI automation and require little manual intervention, ChatGPT does not offer passive efficiency benefits to your business. It must be actively incorporated into your processes by asking it to generate assets. For example, it can generate templates for written content, suggest resources for staff training, or even write code for software development.
Otter.ai: Video meetings are a fact of life in modern business, but taking manual notes may mean missing crucial details, and recording calls means spending time re-watching past discussions. Otter.ai is an advanced, AI-powered virtual assistant which automates note-taking. Its basic function is to transcribe discussions, but with artificial intelligence, it can even create summaries of your meetings, or generate “action points” so you remember the tasks discussed in your call.
HubSpot Marketing: Large software providers like Hubspot are taking advantage of the AI boom by including AI functionality in their existing offerings. For example, the Hubspot Marketing Hub now includes AI-powered functions such as automatic caption generation for social media posts, or SEO recommendations for website building. Using machine learning, Hubspot’s AI can even offer suggestions on how to maintain your brand tone when writing content.
Midjourney: Image generation is a very popular way to use AI, particularly with small businesses that may lack the funds to hire graphic designers for things like brand logos or marketing assets. Midjourney is one of the leading AI image generators, but (like Hubspot) larger brands are implementing their own AI graphics tools, such as Canva AI and Adobe Firefly.

Machine Learning: Using Data to Teach AI

The term “machine learning” is often used in AI-related discussions: it refers to the process by which software systems can autonomously make decisions based on past data.

Machine learning is one of the most powerful features of artificial intelligence, as it reduces the level of human input required for implementing AI tools in business processes. The power of machine learning has not gone unnoticed. According to a 2024 survey by Rackspace Technology, 34% of IT professionals say their employers are prioritising the application of machine learning in 2024.

One of the easiest ways to leverage machine learning as part of a digital transformation is by adopting chatbots for customer service. Chatbots are a great basic example of AI automation that learns from past data: the more questions they are asked, the more answers they will have for future conversations, and the more accurate their answers will be.

Machine learning is also a great asset for personalising a customer journey. For example, your digital transformation might start with training an AI on manually generated customer profiles based on surveys and insights you’ve gathered from customer interactions. Using these profiles, the AI can suggest products and discounts to shoppers and ask for feedback on the suitability of the suggestions. As the AI learns from the wealth of customer behavioural data it will be exposed to, it will then be able to make more accurate recommendations — creating a more personalised customer experience and improving customer satisfaction.

AI in Decision-Making

When discussing AI in digital transformation, the common misconception is that it is primarily for use by front-line employees, and should really only be used for improving the efficiency of processes like selling or administration.

In fact, AI can be highly effective at a C-suite and managerial positions as well by using it to aid in decision-making. Using AI in a decision-making capacity typically means using it to provide insights or summarise data regarding a particular situation or business function. For example, it may be that leaders of a product development team need to decide on new designs for product offers. An AI tool analysing customer surveys, product reviews, and sales data would be able to offer guidance on this decision much faster than a human team completing the same analysis.

However, it’s important to retain some level of human oversight when utilising AI for decision-making. AI tools cannot account for every variable, and you will likely also have a better idea of your company’s priorities than an AI will. Additionally, it may be difficult to fully understand why an AI has come to a particular conclusion, which has led to significant corporate interest in explainable AI.

Explainable AI

The technology giants at IBM found in their 2023 AI Adoption Survey that one of the biggest concerns around AI usage was trust and transparency. This issue was cited by 43% of IT professionals as the reason their employer was not exploring or implementing generative AI.

The lack of transparency typically comes from the fact that as AI becomes more advanced, it becomes much more difficult for humans to retrace how an AI algorithm came to a specific result. The AI calculation process is turned into what is commonly referred to as a “black box" that is impossible to interpret.

So, engineers and data scientists are working to build explainable AI models. These models are essentially AI tools that show their working and can offer either a data-focused or plain-language explanation for why they made a particular decision.

The business value in explainable AI models is clear — an explainable AI needs less human oversight and can be trusted more to be used in decision-making processes. Less manual intervention means increased speed and efficiency, which is a notable market advantage for your business. Although explainable AI is still in the early stages of development, it is likely to be a common asset for your digital transformation projects in future.

Regulatory Requirements of Using AI Automation

Using artificial intelligence is a relatively new business practice, and as with most cutting-edge technologies, it takes some time for legislation to catch up. While there are some legal regulations surrounding AI that companies must adhere to, the rules are regularly updated and differ from country to country. To help you comply with AI usage regulations during your digital transformation projects, we’ve listed some of the most prominent regulations below.

Regulations in The EU

In 2024, the European Union finalised the first EU AI Act, which seeks to ensure that “AI systems used in the EU are safe, transparent, traceable, non-discriminatory and environmentally friendly”. One of the Act’s primary objectives is to regulate AI by outlining areas where using AI without significant human oversight poses significant risks:

Minimal risk: Using AI to automate low-priority administrative tasks or leisure activities, such as AI in email spam filters or video games.
Limited risk: Using AI in consumer-facing applications like customer service chatbots. Also applies to some uses of generative AI.
High risk: Using AI to evaluate job applications or for credit and insurance purposes.
Unacceptable risk: Using AI for behavioural manipulation, social scoring by public authorities, or real-time remote biometric identification for law enforcement.

As well as defining risk, the EU AI Act provides a set legal definition for artificial intelligence, so that future legislation is easier to create and apply. Finally, the act outlines the obligations that companies need to fulfil should they implement AI in a high-risk capacity. These obligations include (but are not limited to):

Completing a fundamental rights impact assessment (FRIA) before deploying the AI system.
Installing a system for human oversight by trained individuals.
Retaining any automatically generated system logs.

Regulations in The UK

Unlike the EU, the United Kingdom has not yet published a legally binding set of laws or regulations that businesses need to comply with if they want to use AI. Instead, in 2023 the UK government published a whitepaper entitled, AI Regulation: A Pro-Innovation Approach. Following feedback from various public and private sector entities, the UK government also published a response letter that clarified several points of the initial whitepaper.

The key feature of the UK stance on AI is that it is not centrally regulated. Instead, a number of different industry regulators will now also regulate AI usage in their particular sectors. For example, the Financial Conduct Authority and the Bank of England would oversee and take action on any AI misuse in the financial services and banking industries, but the Office for Standards in Education, Children’s Services and Skills (Ofsted) would oversee AI usage in schools.

The lack of a central regulator or a set of clear laws in the UK AI whitepaper is a central part of the pro-innovation approach they are trying to promote. Although regulation prevents the misuse of AI, it can also slow innovation. Looser regulations can promote creative thinking and broader usage of AI to improve efficiency.

Regulations Abroad

Around 37 countries have proposed AI-focused legal frameworks to help guide the creation of future AI regulations, as well as manage AI use in the present. Multinational companies will need to keep an eye on all of these frameworks: while following guidelines is easier than complying with multiple legal regulations, it’s important to stay ahead of any potential rule changes.

To help you stay abreast of international AI news, we’ve listed some of the key AI rules in large industrial nations below:

Like the UK, the United States does not yet have a comprehensive AI regulation, but numerous frameworks and guidelines exist at both the federal and state levels. As such, any business using AI in the US will need to check the regulations of the individual states they operate in as well as overall national laws.
The Australian government has not created any AI-focused legislation, instead choosing to apply existing regulatory frameworks for technology to compensate for a lack of laws and policies governing AI in particular. Instead, there is a set of AI Ethics Principles which businesses can voluntarily adopt.
In India, the government has created a task force which will make recommendations on any ethical, legal and societal issues related to AI, as well as establish an AI regulatory authority.
Japan’s AI stance is similar to Australia’s, where any AI rules they create are voluntary. The system, known as "agile governance," means the government provides non-binding guidance but allows the private sector to self-regulate.

Four Steps to Successfully Utilise AI in Digital Transformation

1. Collecting and Organising Data</h3

As we’ve mentioned above, any AI system requires a significant amount of initial data for it to be able to effectively complete a task. So, the first step of using AI in digital transformation must be the identification, collection, and organisation of relevant data. Some of this may be internal data, while some may be third party or customer data.

Data quality and strong data governance practices are key in this stage. If the data you use is not accurate or well-managed, it will not be of use when training your AI systems. If you do not have the skills or infrastructure to ensure good data governance, you may want to outsource this task to experts or invest in data management solutions.

When organising data, it’s also important for managers and AI team leads to determine data ownership, as well as setting clear data security and data usage guidelines. Following these procedures will help you with regulatory compliance.

2. Training or Building AI Models

Using your newly gathered and organised data, you can now build, train, and fine-tune an AI to complete a set task (or even a variety of different tasks). This can be done internally if you have sufficiently skilled engineers on staff, but you may instead want to outsource the job of training or building an AI model. Due to the boom in AI usage among businesses, there are a significant number of trusted third-party vendors that can assist with AI training.

If you don’t need a proprietary AI tool, you won’t need to build it from scratch and can skip straight to implementation. The same is true for businesses using third-party AI automation tools.

3. Implementing AI Into Your Processes

Once your AI is ready, it can be integrated into your operations. It’s important that the workflows the AI will be used for have been previously identified, and relevant personnel have been trained in using or overseeing the artificial intelligence. It’s also important to make sure the AI works well with any existing technologies or processes (unless the AI is designed to replace those technologies as part of your digital transformation).

Effective implementation requires significant collaboration: IT and data engineering teams will need to work alongside frontline workers so that feedback can be shared and the AI system can be leveraged effectively. Management should also take an interest in the implementation as it may cause a shift in roles and responsibilities in certain business functions.

4. Reviewing Your Transformation

As with any business project, it’s important to review after completion so that you can improve future projects. AI is no different: look at any bottlenecks during the training or implementation stages to see what slowed things down and what was critical to success. Any blockers to collaboration are particularly important.

You may want to take some time following implementation before fully reviewing your AI-powered digital transformation, as this will allow you to gather better data on the impact of your AI tools. Statistical evidence of success is important — especially if the AI implementation is a trial and you need to present findings to senior leadership.

Working With Future Platforms to Supercharge Your Digital Transformation

Did you know that nearly 90% of marketers say AI improves content quality to some degree? Or that AI usage could result in revenue increases of up to 10%? Future Platforms can help you access these incredible benefits.

We design digital products, from appealing brand apps to sleek modern websites. We also work closely with many of our clients on designing internal systems to help boost efficiency. If you’re looking to begin a large-scale digital transformation project and want to experiment with artificial intelligence or machine learning tools, we can help.

Get in touch with our team to learn how you can take part in the AI revolution today.