AI in Digital Transformation: The Impact of Artificial Intelligence and Machine Learning on Modern Enterprises

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Still wondering whether your company should invest in artificial intelligence? Sorry to upset you, but you’re almost too late. With thousands of businesses getting into the game at breakneck speed, you are doomed to lose the competition if waiting a bit longer — because now is the time for AI digital transformation.

According to McKinsey global surveys that involved 1,000+ organizations representing the full range of regions, industries, and business sizes, AI adoption by the companies has hovered at about 50% over the past six years. Suddenly, the 2024 report reveals an impressive jump to 72%, with respondents in nearly every region claiming their organizations use AI tools in at least one business function.

Understanding the spike in AI digital transformation

On the one hand, we are amid global digital transformation racing, where AI enables businesses to yield particularly disruptive results. Companies seek to adopt digital technologies for cost reduction, improved customer experience, higher employee performance, increased process efficiency, better decision-making, and enhanced compliance — and AI-powered solutions hold promise to check all the boxes.

On the other hand, the current AI implementation surge is driven by the increasing computational power provided by modern technological advancements, such as high-performance computer chips, cloud computing, 5G, and so on. Coupled with the expanding availability of big data, they gave rise to deep learning techniques, which enabled more sophisticated AI systems ranging from image recognition software and natural language processing tools to autonomous vehicles and smart home devices. Simply speaking, today we finally have technology powerful enough to fulfil the AI promise.

Moreover, organizations that happened to adopt AI earlier can now prove the benefits of AI-driven digital transformation with figures at hand. Based on the above-mentioned McKinsey survey, a cost decrease and revenue increase from advanced analytics are observed by 35% and 58% of respondents, respectively. The report also shows that generative AI allows for reducing costs in 39% of the organizations and helps 44% of the participants generate higher profits.

So, nowadays we have the perfect factor combination to boost the demand for AI/ML algorithms, proven by the steadily growing market size. Estimated at about USD 215 billion in 2024, the AI market is expected to reach USD 1,339 billion in 2030 at a remarkable CAGR of 35.7%. While both public and private sectors are investing heavily in AI-led digital transformation initiatives, businesses that lag behind are at risk of losing their competitive edge instead of arming themselves against increasing customer demands and operational challenges.

Understanding the relationship between artificial intelligence and big data

Although it’s high time to start a digital transformation journey, it’s also crucial to move in the right direction — to locate the most effective ways of implementing AI in business operations. The simplest approach is to optimize existing workflows by applying AI to internal data, such as offering staff training programs based on employee information.

However, in our experience, extensive data analysis involving external sources and large datasets boasts a much bigger potential. Information about clients, products, competitors, and market trends creates many more opportunities for AI usage across your organization and allows for building high-value AI applications that can significantly impact profit.

Actually, we are talking about much-hyped big data, which has finally stopped waiting in the wings. Before the AI advent, we couldn’t effectively derive actionable insights from such large chunks of data, but now it would be irrational to miss out on the emerging opportunities.

Trained on vast datasets, AI systems can better understand patterns, context, and nuances to address issues faced by most businesses:

• Customer engagement and retention challenges
• Inefficient, time-consuming tasks within existing business processes
• Error-prone manual data processing
• Limited customer support availability and delayed response times
• Difficulty forecasting trends and making informed decisions

Spoiler: we are going to tell you more about AI helping deal with vast amounts of data in the next article of our AI/ML trends series. Now, it would be fair to say that real work with big data started thanks to the evolution of AI tools and back up this statement with some real-life examples.

AI and digital transformation: Cases that work

Surely, artificial intelligence is not a silver bullet, but we hope that the following use cases of AI technology will explain how your business can solve some common problems with its help.

• Problem: Low customer engagement
• Solution: Personalisation

Thanks to Netflix, we all know what personalisation is — tailored recommendations generated by AI based on customer behaviour analysis. Nowadays, personalisation spans online and in-app ads, web and social media content, email newsletters, product recommendations, and dynamic pricing, improving customer experience, fostering loyalty, enhancing engagement, and boosting sales. The accessibility of real-time data coupled with powerful machine learning capabilities has recently unlocked hyper-personalisation, splendidly illustrated by Burger King — the fast-food giant used AI algorithms for its Million Dollar Whopper Contest to dynamically generate thousands of ads customized to individual interests and preferences.

• Problem: Time-consuming tasks in support functions
• Solution: AI-driven automation

AI can automate routine workflows like scheduling, reporting, ticketing, and other business processes that are not directly related to the production of core company products and services. One of the examples from our practice is a reporting app for an agriculture company, which combines computer vision with drone technology to deliver up-to-date information on the state of rice fields. The app recognizes plants, automatically calculates them, and generates reports, thus saving the company plenty of time, elevating employee experience, and reducing costs through the augmented workforce and improved productivity.

• Problem: Manual data extraction from large volumes of documents
• Solution: Intelligent document processing

Intelligent document processing combines automation tools like RPA with AI technologies, such as NLP, computer vision, ML, and generative AI, to automatically classify, extract, and validate data coming in various formats. AI tools can unleash data embedded in documents, emails, or PDFs to make it usable in digital operations. Intelligent document processing relieves employees from mundane, tiresome work while reducing errors and processing time. A good example of IDP efficiency is our pilot project for a Japanese multinational IT service provider, which showed business process accuracy of 100% against the planned 98% and 83.6% manual effort reduction against the targeted 50%.

• Problem: Limited customer support availability
• Solution: Conversational systems and bots

Unlike humans, AI-powered conversational systems can work 24/7, providing continuous support for customers by finding answers to their questions, responding to their queries, guiding them through troubleshooting steps, or offering prompts in challenging situations. Armed with natural language processing technology, chatbots can handle inquiries of various complexity in real time, improving customer satisfaction, unloading customer service teams, reducing customer support costs, and eliminating delayed response times. Apart from delivering more responsive service to customers, they can boost employee productivity and satisfaction when used for internal support like our iDocIt assistant. Namely, our customers report a 15% increase in productivity, a 9% decrease in operational costs, and a 45% improvement in document retrieval time.

• Problem: Poor decision-making
• Solution: Advanced analytics and predictions

By analysing historical data, AI can forecast trends, predict customer needs, deliver sales outlooks, and prompt stock replenishment to optimize inventory. For example, a mobility solutions company leveraged big volumes of data to receive up-to-the-minute insights into traffic patterns and complemented them with predictive analytics. By building a powerful cloud infrastructure for big data processing and predictive models deployment, the company was able to provide more accurate and timely information to its customers. Apart from improving its service quality, the company enabled proactive decision-making powered by predictive analytics to optimize its operations.

As you can see, AI solutions bring benefits in various business areas, but we believe that some industries can gain the most value from leveraging AI/ML algorithms.

Competition: machine learning examples by industry

IT, finance, and healthcare are the domains most frequently listed among leading AI adopters. However, we’d like to share less obvious AI use cases to inspire businesses from other industries to digital transformation.

Artificial intelligence in the automotive sector

• Companies can facilitate inventory management using AI to classify car parts from various suppliers for their catalogues. ML algorithms will accurately identify and categorize items, eliminating time-consuming and error-prone manual work.
• AI can generate recommendations for optimal car configurations and compatible accessory components, thus saving time on manual searches, improving customer satisfaction, and promoting sales.
• Chatbots will come in handy for both customers and suppliers in many scenarios, for example, addressing queries, scheduling appointments, or helping them navigate extensive catalogues, which relieves support teams while ensuring responsive service.
• Predictive analytics enables proactive maintenance by spotting patterns that could lead to breakdowns, which translates into better safety and reduced maintenance costs. AI can also predict demand to help companies optimize their inventory.
• Manufacturers will benefit from generative AI prompting new concepts, expediting the development cycle, creating realistic simulations for testing hypotheses, and optimizing production lines to reduce costs and improve quality.

Artificial intelligence in logistics

• Shipping companies can train ML algorithms to analyse traffic, road conditions, and fuel consumption to optimize delivery routes and arrive at fuel savings and reduced delivery times. If powered by streaming data, AI is also capable of adjusting routes in real time to respond to changes.
• With advanced analytics and smart predictions, logistics companies can anticipate demand fluctuations and streamline inventory management, ensuring timely product delivery to customers while reducing operational costs.
• Companies can leverage AI-powered simulations to test warehouse configurations and optimize space utilization. By analysing data on item placement, usage, and demand patterns, ML models help determine the best product layout.
• Solutions based on computer vision enable automated quality control in warehouse operations. Namely, they allow managers to identify damages, evaluate their size and type, and take necessary steps before the situation escalates and leads to customer churn.
• Dynamic pricing is another thing reshaping logistics business models, as it allows companies to flexibly respond to changes in demand, supply, subsidiary product prices, and competitors’ offerings. AI applications can analyse current trends and match them with customer historical data to adjust prices in real time.

Now that we know how AI works in real-world scenarios across different industries, let’s move to how to make it work as intended.

Getting started with artificial intelligence in your organization

Despite the bright ML opportunities and the growing number of AI adoption success stories, it’s not easy to translate the enormous potential of AI into financial benefits. For example, the latest prediction by Gartner holds out little hope for the accomplishment of at least 30% of GenAI projects — they won’t make it from proof of concept to production. If you want to ever see real-world AI applications driving digital transformation initiatives across your organization, you need to sidestep the most common pitfalls derailing AI implementation.

Best practices for integrating machine learning into business operations

Without much diving into technical details, we offer the following set of actions to properly adopt AI in your company:

• Identify problems that can be effectively solved using AI.

Gartner lists unclear business value among the top reasons behind failed AI projects, and our research reveals the same challenge. Whether you want to improve operational efficiency with intelligent automation or elevate customer experience with personalized product suggestions, you should be able to define corresponding workflows, formulate the desired outcomes, and specify internal metrics to measure success.

• Make sure you have access to the data needed to adequately train an ML model.

Both data quality and quantity are crucial factors for machine learning algorithms to deliver accurate results. Since insufficient data can lead to poor generalization and hamper the model’s ability to learn, you should consider collecting additional datasets specific to the project. To avoid flawed algorithms and unreliable outputs, your data needs to be cleaned and organized through ETL/ELT processes before going into ML models.

• Assess your current technological infrastructure and skill set.

AI is a complex field of computer science, which requires AI engineering talent to build, train, and tune ML models. If your company lacks in-house expertise, you may need to opt for a third-party vendor to bring your digital transformation initiatives to life. You should also have specialists in data science who can, among other things, review your current data architecture, optimize existing data warehouses, or even switch to another data management solution, such as a data lakehouse. AI implementation will also require sufficient computational resources and data protection measures.

• Define which models and deployment options are best suited for your project.

There is a range of modelling techniques that can be used for building custom machine learning algorithms (linear regression, k-means clustering, decision trees, and so on) as well as pre-trained models developed by third-party vendors and made available to customers via API. Both options can be deployed on-prem or in the cloud depending on the specifics of your project and business.

• Take your time to properly train, test, and refine AI algorithms.

ML models have to be trained on large datasets and fine-tuned on smaller, task or domain-specific data. During these stages, AI engineers adjust model parameters and hyperparameters, try out different features, and run numerous tests to evaluate models and select the best-performing option for deployment. Don’t expect it all to happen overnight, and set realistic timelines.

• Ensure close collaboration between tech and business stakeholders when integrating the validated model into intended apps and business processes.

AI models are usually used in combination with other technologies, so their deployment requires much interaction and mutual understanding between different IT teams to ensure smooth integration. Besides, the developers perform further testing, including user acceptance tests, to fix any problems with integration and performance where they will need assistance from business users.

• Monitor and optimize your model.

AI tools require continuous monitoring, maintenance, and optimization to remain accurate and effective. While ML models can evolve and improve over time, the unpredictable nature of real-world data and fast-changing business needs may result in their failure to produce reliable and relevant outputs. Continuous monitoring and timely adjustments up to retraining and redesigning will keep your AI solution impactful and trustworthy.

This is our basic outline for getting started with ML, but obviously, you will need to take many more actions to launch full-on AI-powered digital transformation. While there are many other vital things to include in your digital transformation strategy, such as employee training and change management, we’d like to highlight the point closely related to the very lifeblood of AI — data.

The role of a powerful data platform in successful artificial intelligence integration

In our blog, we have already discussed how data architectures correlate with organizations’ readiness to become data-driven and emphasized that rigid infrastructures and legacy systems create obstacles for a seamless flow of information, including data required to power AI systems. In this article, we have also mentioned the option of redesigning your data architecture if it cannot deliver the data quality, data integration, data governance, and data management capabilities you need for smooth AI implementation. In fact, the lack of these capabilities is cited among the main roadblocks to the successful use of AI for digital transformation by 300 manufacturers surveyed by Microsoft and MIT Technology Review Insights.

Big data poses another challenge to organizations seeking to embrace AI-powered processes. These large, complex, and fast-growing data collections hide a wealth of meaningful insights gathered from IoT devices, social media feeds, application logs, web pages, and other sources. Moreover, big datasets are extremely suitable for training AI models. But because of their volume, velocity, and variety (since big data can come in structured web forms, semi-structured emails, and unstructured videos), traditional data processing tools cannot handle the load.

Conversely, a modern, robust data management platform ensures the effective consolidation of data from various sources and proper data preparation for use in ML models regardless of data types and formats. It can ingest, transform, and process big data in real time, enabling immediate insights like those required for fraud detection or dynamic pricing. It also provides scalable computing and storage for resource optimization and supports data quality monitoring. Advanced platforms boast integrated ML and AI toolkits to facilitate, speed up, and streamline the development and deployment of AI models.

Understanding the importance of data quality and data architecture for the implementation of AI and digital transformation, our data engineers tested various data platforms to handpick three leading solutions that bring together data quality management, storage, processing, and model training:

• Databricks, a unified, open data analytics platform optimized for AI/ML and big data while offering vast collaborative opportunities for data engineering;
• Snowflake, a cloud data platform with AI and ML integrations, allowing users to build data-driven applications and derive insights in real time;
• Microsoft Fabric, a powerful tool for developing and managing cloud applications that can integrate with data services and AI tools on the Azure platform.

Having vast experience in building AI systems, BI tools, and modern DWH solutions on these platforms, we can expertly recommend them for disruptive ML projects. We are also proud of our partnership with Microsoft, which gives IBA Group fast access to the latest technology in the data science field. Another strategic partnership we have recently made toward enhanced Al implementation across various industries is a collaboration with Data Monsters, an NVIDIA Elite Partner with 16 years of experience in artificial intelligence.

Conclusion

In our next article, we will explore emerging AI/ML trends for businesses, but is it really worth waiting any longer before jumping on the AI bandwagon? While you are chewing over ML perspectives for your business, your competitors may be already reaping the multiple benefits that AI-led digital transformation offers. IBA Group will eagerly support your data management, analytics, and AI endeavours with professional services, yet the main question is — are you ready to start acting toward digital transformation in your company right now or going to let the rivals leave you in the dust?

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