The Future of Business Decisions: Leveraging AI for Better Outcomes

Frontier technologies can analyze vast amounts of data in real-time, providing valuable insights that can aid human decision-makers. However, skeptics argue that too much reliance on this approach poses challenges, including ensuring access to accurate and unbiased data, the need for skilled professionals to develop and maintain AI systems, and ethical concerns surrounding privacy and bias.

Notwithstanding these concerns, there is no doubt that by augmenting the human decision-making process with AI-driven analytics and recommendations, organizations can make more informed choices, mitigate risks, and seize opportunities. This allows workers to focus on resolving more complex and strategic issues, while routine and repetitive decisions can be automated.

Different degrees of AI application in decision-making

Decision automation, decision augmentation, and decision support represent the degrees to which AI and analytics can be deployed by organizations to pursue faster, more consistent, and higher-quality decisions at scale. With decision automation, for example, the system makes the decision using predictive analytics, while with augmentation, the system recommends a decision, or multiple decision alternatives, to human actors. The obvious benefit of using this approach is the synergy between human knowledge and the capability of AI to analyze high volumes of data rapidly and deal with complexity. Lastly, with decision support, workers make the decision, supported by descriptive, diagnostic, or predictive analytics.

A crucial aspect of decision-making lies in the democratization of data across the organization. In the past, business intelligence tools and data analytics dashboards effectively served this purpose by enabling workers to view a static perspective of the business, showcasing essential descriptive data and key performance indicators (KPIs).

Today, technology is enhancing standard organizational decision-making processes through the availability of machine learning for decision support, data-driven insights, predictive analytics, natural language processing, and real-time monitoring and alerts. Coupled with adaptive learning systems that continuously improve based on feedback and outcomes, decision support systems can refine their recommendations over time, enhancing their effectiveness in assisting human decision-makers.

For example, by leveraging historical customer data and other statistical information, contact center managers can gain actionable intelligence to improve service quality, reduce costs, and maximize the efficiency of their teams, while also using predictive analytics to guide staffing decisions. While data analysis has always been an integral part of contact center work, augmentation leverages AI to analyze large datasets quickly and instantly extract meaningful insights.

Machine learning algorithms can also provide decision support by identifying patterns, correlations, and anomalies in data. This assists decision-makers in evaluating options and making decisions based on a more comprehensive understanding of the relevant factors. It’s also crucial that decision-makers are equipped with tools and guidelines to ensure that any AI-derived decisions align with accepted standards and corporate values. The tools and methods that can assist users in explaining outcomes must then become part of the decision-making process. This capability is the focal point for what is being called ‘Explainable AI’ (XAI).

The emergence of GenAI has significantly transformed the landscape of business analytics dashboards. These have evolved to become dynamic, intelligent platforms complete with prescriptive capabilities. Large language models leverage machine learning insights to create a narrative for the worker, together with adaptive visuals. Dashboards today possess the capability to provide information through what could be termed a ‘conversation’ with the existing data. This elevates worker insights and encourages a more proactive approach to data analysis.

While new technologies can significantly enhance decision-making processes, it's important to consider ethical implications, transparency, and the collaborative role of human decision-makers. Combining AI's analytical capabilities with human judgment and values can lead to more informed and responsible decisions.

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Logical Design Solutions (LDS) is a digital strategy and design consultancy for global enterprises. We create experiences that transform business and help people work successfully in the new digital organization. Clients come to LDS because of our reputation for intellectual rigor, our foundation in visionary experience strategy, and our commitment to enabling digital transformation inside the enterprise. Learn More about how LDS has dramatically improved the way that some of the largest corporations in the world do business.