Artificial intelligence (AI) initiatives have become a major focus for organizations. According to a Gartner report, one-third of companies that implemented or plan to implement AI are deploying it in several business units. This interest didn’t appear out of nowhere. In fact, the shifts that have shaped the current digital landscape paved the way for AI by changing the way businesses operate and build software. Read on to find out what impacted this transformation, how companies are currently using AI and what best practices can help you choose the right solutions for your AI project.
Cloud computing and data availability made all the difference
In recent years, the increasing democratization of computing power and availability of data have significantly transformed the way organizations develop software, use data or even devise their business models. Cloud technology, in general, has enabled companies of all sizes to access meaningful computing resources without major upfront investments. In the past, this kind of capacity was reserved mainly for large organizations who could afford the costs of on-premises hardware and had the necessary knowledge and experience to successfully implement it. Nowadays, high-end cloud infrastructure in the Infrastructure as a Service (IaaS) model is available and feasible even for startups that are just starting to grow their business.
Another technological solution that emerged from these changes is serverless infrastructure. Solutions such as Amazon Web Services (AWS) Lambda enable businesses to run code in response to events without having to manage servers, which makes it much easier to scale solutions and significantly reduces costs. The ability to access high-quality infrastructures and process large data sets has provided a foundation for the current rise of AI.
From an even wider perspective, technology has become available for everyone through products like smartphones and the instant connectivity they offer. This has also inspired many businesses to move from a product-centric to customer-centric approach. Platforms like Uber or Airbnb demonstrate how access to increased cloud computing powers can lead to new successful offerings on the market.
These changes have also caused many companies to move to subscription-based business models. Prominent examples include Microsoft and Adobe, which transformed products that used to be bought as one-time purchases into cloud-based subscriptions. These models ensure a steady stream of revenue and enable organizations to seamlessly deliver necessary updates to their platforms.
AI’s pivotal role in elevating business operations
The rise of data availability has seen organizations not only collect information, but also leverage it to boost data-driven decision making – and they’re using AI solutions to achieve it. AI helps them turn large data sets into actionable insights to elevate various business areas. AI use cases will very likely expand across industries and processes as this technology evolves. But at the moment it’s worth highlighting two sectors that process very high amounts of data and are increasingly implementing AI.
Organizations in financial services – banks, insurance institutions and investment management companies are using AI to optimize their operations, figure out optimal parameters for their agreements and enhance different analyses and reports. This technology also enables businesses to automate many repetitive processes, which saves time and resources.
While financial institutions may handle a lot of information about customers and transactions, the manufacturing industry collects a wealth of data on design and production processes. Businesses in this sector can get more out of this information by deploying AI to more reliably predict when equipment needs maintenance so as to prevent anticipated issues or failures.
However, while this technology has proven to offer considerable value, the AI journeys of some companies may be hindered by doubts or other issues. The most common barriers to implementing AI include insufficient understanding of the capabilities of this technology, low data quality, lack of AI governance and problems with acquiring talent with relevant expertise. Teaming up with a software partner with practical experience in AI development services enables companies to solve these concerns and remain competitive in their markets.
How to choose the right solutions for your AI-powered applications
Interestingly, many of the questions surrounding AI adoption were also cast at early cloud computing implementations. The similarities don’t end there. As with cloud-driven projects, there are some universal best practices worth following if you want to adopt AI to achieve long-term results, scalability and flexibility rather than a short-lived boost.
Currently, there are plenty of available solutions and it may be hard to choose the one that’s right for your company. That’s why your very first step should be to determine what exactly you want to build. Think about the problems you’re trying to solve, the capabilities your solution should have and any other requirements that need to be met. Simply going for AI technology that’s generally considered the best won’t give you the results you’re after – the tools you choose have to align with your goals and needs.
You also need to take stock of your current tech stack. Analyze the technologies you’re already using and verify if there are any you should reconsider. It’ll be easier to start developing functionalities for which you already have tools. Additionally, it might turn out that you can solve the problem you’re targeting with the technologies that are already at your disposal.
If you still need to look for the right AI technology, it’s best to experiment with the most promising solutions. Robust research and development (R&D) processes and a team experienced in conducting them will help you figure out what works for your company and the solution you want to develop. Finally, plan for a strong ecosystem that will surround your AI-powered solution. That includes security, monitoring, developer tools, data management, FinOps and practices for responsible AI. Accounting for these elements will ensure that your solution makes a lasting impact and moves from the Proof of Concept (PoC) stage to a proven product or service.
Cloud infrastructure is the backbone of successful AI projects
Good preparation for AI adoption and optimizing cloud environments for performance and flexibility are key to developing strategic AI solutions. It’s also important to understand all the different aspects around AI development that heavily contribute to the success of any initiative like this. AI projects highlight the significance of a high-functioning environment that combines the cloud, cybersecurity, user experience, data management and efficiency.
Our latest webinar, with special guest Michał Furmankiewicz, Principal Program Manager in the Industry AI Team at Microsoft, explores this role of interoperability in innovative projects as well as explains the common mistakes in AI adoption and other key aspects of optimizing cloud infrastructures for AI-powered solutions. Click here to get access to the full recording and learn more about driving strategic cloud and AI projects in your company.
About the authorMichał Jaworski
Service Delivery Director
A General Manager and Service Delivery Director with over 20 years’ experience in the IT industry, Michał has worked in different management roles, leading infrastructure, cloud, security and data management teams in a range of international organizations such as Citibank, Aviva, AXA Direct and EcoVadis. A proponent of the public cloud, Michał empowers businesses with AI, data analytics and cloud computing expertise, while promoting high software quality and nurturing collaborative partnerships with clients.