Daniel Knauf is Chief Technology Officer at Material.
It’s 2024, and virtually no one in the business world has heard of AI and the expected revolution it will bring. Speculation on changes in work, dramatic increased productivity and even the “threat” of your Double foxglove stealing your work is all talk around the virtual water cooler. But how much of this growing conversation is hype, and where are the opportunities to leverage AI to unlock real value and business impact?
To fully realize the potential of AI, business leaders must not fall victim to “shiny object syndrome,” but rather focus on use cases that have practical utility for their organization, adopt a iterative approach to AI-driven transformation and implement measurement systems to understand the true potential of AI. impact of these technologies.
Sifting through the hype to discover real value
There is a key distinction to be made between AI applications that provide real utility and those that are gimmicks. Although AI has vast potential to revolutionize industries, not all applications are equally valuable. For years, utility-focused AI solutions, such as machine learning and predictive analytics, have contributed to industries such as healthcare, finance and environmental sciences, providing benefits tangible and solving complex problems. Consider predictive maintenance programs in hospitals that alert property managers to potential malfunctions in critical infrastructure or AI-based fraud detection systems to protect customer finances.
In contrast, some AI innovations, while flashy and technologically impressive, may lack substantial impact or practical commercial application – more novelties than tools for real change. Given the general public’s fascination with LLMs and applications such as ChatGPT and DALL-E, the natural impulse is to jump to the conclusion that critical business functions are a well-designed distance from disruption. In almost all cases, the reality is much more complex.
We can see how AI is already embedded in the business world thanks to a recent study led by IDC and sponsored by Microsoft.
• Seventy-one percent of companies surveyed are currently using AI and 22% are planning to implement AI in the next 12 months.
• AI deployments would take 12 months or less in 92% of cases. In 40% of organizations surveyed, the time to implement AI was less than six months.
• Over 14 months, organizations see an average return of $3.50 for every dollar invested in AI.
With returns like these, it’s no surprise that companies have been tempted to invest everything they have in AI solutions. But for an AI integration to be successful, it must be approached with a few fundamental principles in mind.
To maximize value, focus on practical business utility
Like any business investment, to achieve value levels like those reported in the IDC/Microsoft survey, AI must deliver measurable benefits and a clear path to positive ROI. For business leaders, this means thinking deeply about their organization’s needs and identifying areas where AI can make a practical impact. While needs vary from industry to industry, the following utility-based AI use cases are some of the most common that my team and I provide to our clients across industries.
• Data anomaly detection and quality checks: AI’s ability to detect data anomalies finds real value in industries such as finance, research and development, and consumer research. For example, it can identify fraud in financial transactions and eliminate bots in human data collection, thereby directly avoiding losses and preserving data integrity.
• Targeted customer segmentation: AI can transform marketing processes and improve customer segmentation. By analyzing consumer data, AI allows businesses to create highly targeted campaigns, leading to increased engagement and better marketing ROI.
• Empower decision-makers: AI tools make complex data understandable to non-technical users, facilitating informed decision-making in various business areas. This democratization of data analysis can improve operational efficiency and strategy formulation.
• Improved predictive analysis: In predictive analytics, AI predicts market trends and customer behaviors, allowing businesses to stay ahead of the curve. This level of foresight can help enable the development of a proactive strategy to maintain a competitive advantage.
Introduce AI programs focused on measurement and iterative improvement
For companies embarking on AI modernization, another important factor to consider is the role of measurement and iterative improvement. Integrating AI into business processes is not only about implementation but also about continuous measurement and evaluation of performance. The success of AI systems in any industry depends largely on how they are monitored, evaluated and refined.
From an organizational perspective, I find it helpful to start with the highest executive goals and work upstream to align data and systems to ensure they are traceable to priority outcomes. Likewise, establishing performance measurement criteria is the cornerstone of successful AI integration. These can serve as a roadmap for assessing whether an AI system is achieving its intended goals or whether its practical impact is insufficient.
AI systems are not ordinary “set it and forget it” Ronco solutions; they require constant evaluation and refinement. This is a result of the dynamic nature of AI and machine learning models, which learn and evolve based on new Continuous monitoring ensures that systems will adapt to changes and maintain their effectiveness over time and can also contribute to identify biases or errors that could infiltrate the system.
The development of AI systems should be iterative, involving continuous testing, feedback, and improvement. An iterative approach allows for incremental improvements, ensuring the system becomes more efficient while allowing for the latest technology upgrades over time, all in the context of a company’s evolving needs and goals.
Conclusion
The AI revolution is here, but it’s up to business leaders to sift through the hype and identify the use cases that will drive their organization to greater efficiencies. When approached correctly, AI can deliver a tremendous level of ROI and productivity acceleration. So, instead of chasing the latest shiny innovations that make headlines, ask yourself: where can AI meet the practical needs of my business, and how can we design a roadmap for ensure that it provides long-term sustainable value?
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