Article
by Dragan Gajic, CTO and Ken Tilk, Head of Data & AI
From luxury to necessity: Leveraging AI
With the advent of large language models (LLMs) and foundational artificial intelligence (AI) technologies, the AI landscape has undergone a seismic shift. AI is democratizing and making its benefits accessible to companies and organizations of all shapes and sizes.
Traditionally, the barrier to entry for AI adoption was high. Organizations needed substantial amounts of quality data, robust and expensive infrastructure for training models, and a skilled workforce proficient in machine learning and data science. Consequently, only big tech companies could afford the investment required to harness the power of AI effectively.
However, the emergence of large language models has drastically reduced these barriers, making AI accessible to businesses of all sizes and industries. Creating the right AI solution for your organization doesn’t have to take significant investments in teams and hardware anymore. And with the competition in the GenAI market heating up, the price is pressed to come down even more in the next five years.
Today, AI is no longer a luxury but a necessity for gaining a competitive edge in the market.
How to win with AI
In Gartner’s 2023 poll of more than 1,400 executive leaders, 45% reported that they are in piloting mode with generative AI, and another 10% have put generative AI solutions into production. And as an assurance to their investments, an Accenture study reveals that companies that adopt AI early and comprehensively show higher profit margins and improved performance compared to their peers. But beyond mere competitiveness, organizations leverage AI to streamline internal processes and enhance operational efficiency. Business processes are the very core of every organization and making these more efficient is what really makes the difference. The real win comes from making your organization work better.
However, like often with many technologies, in business, much of AI is not a one-size-fits-all solution. Even more, it is important to focus on the problem that needs to be solved rather than the approach that will be used. The biggest value lies in knowing your business and having the tools to solve possible problems and make the processes more efficient. When we understand the challenge, the most appropriate approach will emerge for addressing it, be it a combination of different LLM models to achieve the best quality or an on-premises solution to protect your data.
The importance of data in AI
There is no AI without data. That is what it is trained on; that is what it eats. However, a popular saying goes: Garbage in, garbage out. It even has its own often-used acronym GIGO. In short, poor data produces poor results. If the data you use for your AI tool is of poor quality or faulty, it is unlikely that good or truthful results will be achieved by applying LLMs to the data. It is important not to overlook the significance of having a robust data architecture, high-quality data, and well-designed interfaces for interacting with the data.
Seamless experiences: AI shaping productivity and ways of working
We believe that at the core of this AI-driven transformation lies the concept of seamless experiences. Whether it’s improving customer interactions or enhancing employee productivity, the goal remains the same: to create frictionless experiences. In EY’s 2023 Work Reimagined survey of both employee and employer survey respondents, a net positive 33% see AI’s potential benefits for productivity and new ways of working, with a net positive 44% seeing benefits to the reality of flexible working. By leveraging AI-driven tools and solutions, organizations can streamline mundane tasks, leading to a rise in strategic actions and personalized experiences.
That is also driving AI toward near-future developments centered around human-machine collaboration. By delegating monotonous and time-consuming tasks to computers, we free up valuable time and cognitive resources to focus on strategic decision-making.
Gartner predicts that by 2026, over 100 million humans will engage robocolleagues to contribute to their work. And that by 2027, nearly 15% of new applications will be automatically generated by AI without a human in the loop. This is not happening at all today.
For software companies, integrating AI into their own work processes and solutions is no longer optional but imperative. Data and AI have become indispensable software development components, enabling companies to deliver innovative, intelligent, and value-generating solutions to their customers. According to Accenture, AI is projected to boost labor productivity by up to 40% by 2035, unlocking new opportunities for growth and innovation across industries.
Get on board or get left behind
Where to get started with your AI adoption? There is a common myth that AI projects take years to complete. But the evolution of AI is so fast that you cannot afford to have huge, 2-year projects. But unlike a myth, the fact is that a lot can be delivered with 2-3 months.
Before the wide public adoption of large language models, following the leading edge with ChatGPT as the frontrunner, we built our data and analysis solution, Tark. Tark has helped our customers analyze and visualize their structured and unstructured data and generate insights and recommendations for continuous improvement.
Learn moreCatalyst for organizational change
To create seamless experiences, we must be technology agnostic and early adopters. Because of this belief and the actions behind it, we can leverage our long experience in data and AI projects to save time and money for our customers. AI is not merely a technology but a catalyst for organizational transformation. To thrive in today’s fast-paced and competitive landscape, businesses must be agile, adaptive, and efficient. Embracing new technologies and leveraging data-driven insights are critical to achieving operational excellence and driving sustainable growth.
In the Gartner poll, 78% of respondents believe that the benefits of generative AI outweigh its risks, and this number has risen since the previous poll. However, privacy should not be pushed aside to take advantage of GenAI. We believe that fully understanding the importance of the discovery phase can mitigate many of the risks – to truly know your company’s goals, where it stands, and the limitations of your AI project. For example, it is possible to choose which data to use, run your GenAI solution completely on-premises if you do not feel comfortable using the cloud, or create a hybrid solution in which some of the data sits on the cloud and more sensitive data is kept on-premises.
The key is knowing your possibilities and tailoring your GenAI solution to your exact needs with the right authorization matrix.
The same goes for ethics. While AI holds immense potential, it also raises critical ethical concerns that demand careful consideration. Generative AI models are usually trained on vast amounts of uncontrolled data that can amplify existing biases or breach copyright laws. While data diversity and model governance can alleviate some of the risks, it is important that the models be transparent, that clear human oversight still remains over their results, and that clear accountability and liability mechanisms are established.
Even though AI is evolving quickly, it is still considered an emerging technology with its own risks. Therefore, in delivering AI solutions, we know it is important not to overpromise, be transparent, and work closely with the client.
The key to unlocking AI’s full potential is its application to real-world business cases. From automating repetitive tasks to optimizing resource allocation, AI-powered solutions can address a myriad of challenges faced by organizations across various industries. By collaborating with AI experts and leveraging cutting-edge technologies, businesses can unlock new opportunities, drive innovation, and stay ahead of the curve.
