Balaji Sreenivasan is the founder and CEO of Aurigo Software.
Technology moves fast. Businesses are used to adapting to new technological trends to expand their reach and capability, but the pace of change we are experiencing at the moment is unprecedented.
In the past few years alone, we’ve witnessed game-changing breakthroughs in generative AI, machine learning (ML), natural language processing (NLP) and predictive analytics. This has led to what McKinsey refers to as a "perpetual learning culture," in which businesses must not only strive to become tech-literate but also consider their technology investment options carefully when considering potential use cases.
Gone are the days when every business would jump onto the latest technology trend. Today’s trends are myriad, and the options are overwhelming. CTOs have the unenviable task of identifying which technologies, in an ocean of digital change, will make for a worthwhile investment and lead to positive business outcomes.
Understanding which technologies will offer the most significant return on investment will become crucial to maintaining a competitive advantage in the coming years, and that’s no mean feat given the vast array of services on offer and the number of ways in which they can succeed or fail.
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Somewhat paradoxically, technology itself could provide CTOs with some much-needed guidance on where to make strategic investments. Machine learning, once a nascent technology to be explored in the same way we are now exploring generative AI, has now reached maturity.
Algorithms can sift through enormous volumes of historical data, identify patterns and trends across multiple deployments and provide CTOs with a detailed overview of where their investment priorities should be focused and why.
According to Gartner, by 2025, tech investors and CTOs will prioritize data science and machine learning over "gut feel" when it comes to making investment decisions. Aside from the obvious advantage of having an algorithm work with large data volumes to simulate investment outcomes, it also creates a trail of accountability for CTOs, allowing them to better explain how or why they arrived at a particular investment decision beyond "hit and hope."
Leveraging ML As An Investment Tool
Machine learning algorithms, including regression, decision trees and neural networks, offer robust and versatile tools for data analysis. By learning from historical data, these algorithms can forecast future trends, predict outcomes and uncover hidden patterns in complex data sets that might otherwise remain undiscovered.
Regression models, for instance, can help us understand the relationship between different factors and their impact on our investment returns. These models can assess the probability of a particular outcome based on various inputs, aiding decision makers in quantifying the risks—and rewards—associated with each potential investment.
Decision trees help to illuminate the decision-making process, demonstrating how different decisions might lead to various outcomes. By providing a graphical representation of potential investment scenarios, decision trees can simplify complex strategic dilemmas and help tech leaders make more informed choices. This can also help CTOs demonstrate the logic behind certain decisions, combining ML insights with their own experience and judgment.
Neural networks, inspired by the human brain's neural structure, can learn from vast amounts of data, identifying patterns too complex for humans or traditional algorithms to grasp. By applying these algorithms to historical and real-time data, CTOs and CIOs can predict future trends, identify lucrative opportunities and stay ahead of the technology curve.
Making Informed Investments
Several industry leaders are already leveraging ML to guide their investment strategies. For instance, companies are using ML to predict market trends and consumer behavior, which in turn aids in investing in the right marketing channels and tech stacks. Furthermore, tech companies are utilizing ML to forecast product demand and supply, enabling them to optimize their supply chain and reduce overhead costs.
In the public sector, machine learning is being used to identify fraud and improve financial management. By predicting fraudulent activities before they occur, organizations can save considerable resources and mitigate risk.
As an example, let’s consider the role of a CTO at a government agency or asset management company overseeing the development of new projects. The U.S. recently unveiled the Infrastructure Investment and Jobs Act (IJJA), a national project dedicating $1.2 trillion to the repair and overhaul of critical assets such as roads, bridges, ports, water systems and more. It’s a huge capital investment, and one of the things fund allocators will be looking for is the ability of agencies such as the Department of Transportation to be transparent and accountable.
With machine learning algorithms at their disposal, an agency CTO will be able to create what-if scenarios for unlimited variables to assess their impact on project resources, budgets and timelines. The CTO’s experience and intuition will still play a crucial role here, but the inclusion of ML means that CTOs have more real-world data insights to draw from to guide technology decisions for agency leaders and their projects.
Futureproofing Investments
The value of machine learning extends beyond merely processing historical data and predicting trends. It’s an iterative process that learns from every new piece of data it encounters, continually improving its predictive accuracy over time. As a result, machine learning can adapt to changes in real time, making it an important tool for future-proofing investments.
As the market changes, the balance of supply and demand shifts, or new technologies become available, the algorithm can adapt and course-correct so that, while the direction of travel may change, no investment capital is wasted.
It's also worth noting that as ML technology matures, its accessibility and usability have increased dramatically. Many providers now offer machine learning as a service (MLaaS), which allows organizations to leverage these powerful algorithms without the need for extensive in-house expertise.
The capability of businesses tomorrow depends on the investment decisions they make today. Machine learning offers an effective means to drive data-driven decision making, helping CTOs and CIOs determine where to best allocate resources for maximum ROI.
By leveraging machine learning algorithms, tech leaders can make more informed, proactive decisions that secure their organization's future in an increasingly competitive technology landscape.
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