AI-Assisted Decision-Making: Top 7 Challenges & Tips for Success

58% of those companies who have implemented AI report increased efficiency and decision-making throughout their teams. Yet only 12% of companies are actually benefiting from this technology.?

AI is rapidly reshaping the business landscape and is now poised to revolutionize decision-making processes. Though AI-assisted decisions are growing, many data and analytics executives struggle to successfully implement and scale these solutions within their organization.

Ronald van Loon is an Infocepts partner and is drawing on his deep experience as an industry analyst to explore the growing impact of AI-assisted decisions and the challenges organizations experience in using AI to drive their business forward.?

AI-supported decisions are changing how businesses operate and enhancing the effectiveness, speed and accuracy of decision-making processes. Many organizations have made great strides towards implementation but it’s a complicated, time consuming process that often leads to failure.

Organizations need to know what challenges they’re up against when attempting to leverage AI-assisted decision-making and the best practices that help them drive widespread value from their data.?

Top 7 Challenges in Implementing AI-Assisted Decision-Making

AI-assisted decision-making is increasingly important to modern businesses. It introduces more speed and efficiency because AI algorithms can process substantial amounts of data in real-time, enabling faster, objective, contextual decisions. It also improves accuracy because of the ability to identify patterns and relationships that would otherwise be unrecognizable by humans.

Additionally, because it automates repetitive and time-intensive tasks, it frees employees to focus on more strategic work and reduces the costs associated with manual processes, human error and inefficiencies.?

But implementation challenges persist:

Poor business case for AI?

Without a clear strategy and business case, organizations might not have a good understanding of what they hope to achieve with AI-assisted decision-making, making it difficult to determine if implementation is successful. Also, it can be difficult to integrate the solution into existing business processes, gain support of key stakeholders, measure ROI, and identify opportunities for ongoing improvement.

Data quality?

One of the biggest challenges in implementing AI-assisted decision-making is ensuring that there’s copious amounts of high quality data. Poor data quality can negatively impact the accuracy of AI algorithms and limit the ability to provide meaningful insights, leading to greater efficiencies.?

Integration with existing systems: Another challenge is integrating AI algorithms with existing systems. Existing systems are usually incompatible with AI systems because they simply weren’t designed to integrate with AI algorithms and feature data that’s stored in disparate locations. There may also be resistance to modify existing systems to accommodate AI algorithms.?

Bias and fairness?

AI algorithms can perpetuate existing biases and discrimination, leading to unethical and unfair outcomes. For example, data or algorithm bias that don’t accurately reflect the population they’re intended to serve will produce skewed results that can harm marginalized groups. Also, if the AI algorithms are black boxes, decision-making processes aren't easily interpretable by humans, making it difficult to detect and address biases.?

Trust and adoption?

Many executives struggle with building trust and encouraging adoption of AI-assisted decision-making among employees who may fear that AI will replace their jobs. This might be due to an organizational culture that’s slow to adopt new technologies, fostering reluctance to embrace new approaches to work or even mistrust amongst stakeholders.?

Regulation and compliance?

Companies must navigate an intricate, rapidly shifting regulatory landscape; regulations like the GDPR in the EU and California’s CCPA require organizations to protect personal data, which can lead to data privacy and security concerns when implementing AI solutions that use this data.?

Technical expertise?

AI algorithms require specialized technical expertise to design, implement, manage, and maintain, which can be an issue for businesses who lack sufficient internal resources. This can create risks of deploying AI algorithms that aren’t optimized for their specific needs, or increase the likelihood of technical issues and delays, negatively impacting business outcomes.

Top 7 Best Practices to Succeed with AI-Assisted Decision-Making?

Implementation of AI-assisted decision-making requires careful consideration of these challenges and a strategic approach to ensure efforts are delivering measurable business value and growth - and placing AI-powered insights into the hands of the organization.

Below are top best practices data and analytics executives should embrace:

Develop a comprehensive strategy?

A clear strategy helps align AI-assisted decision-making with overall business goals and ensure that resources are allocated effectively. High performing AI adopters tend to link their AI strategy to business outcomes. Executives need to define their business objectives and where AI can add the most value; assess their existing infrastructure to determine what must be in place to support the solution; conduct a feasibility study to understand the efficacy and cost of implementation; secure buy-in from key stakeholders; and develop a roadmap for implementation, including budget, milestones, and timeline.

Foster collaboration and communication

Data and analytics teams, technology partners, and stakeholders from all levels of the organization should be involved in the design, development and implementation process to ensure all needs and concerns are taken into account. Establish regular communication channels and encourage cross-functional collaboration to facilitate open discourse about the status and progress of AI projects to increase buy-in and ensure alignment on the goals of AI-assisted decisions.?

Focus on data management and data governance

Data management ensures that data used for training AI systems and making decisions is accurate, timely, consistent. and relevant. Proper data governance, data quality control and data security measures can help prevent issues like data breaches and bias, and improve the speed, efficiency and scalability of AI projects for faster time-to-value. A data governance framework can help establish policies for various data assets and help reduce potential legal risks by promoting policies for responsible data access.

Cater to the end-user experience

Organizations need to enable employees, customers and partners to effectively interact with the technology. Engage with end-users to understand their needs, preferences and pain points to ensure the AI system enhances their experience. Prioritize user-friendly interfaces that are intuitive to use, and identify KPIs that will be used to monitor performance and measure the solution’s success.?

Test and validate models?

Organizations should test and validate their AI models to ensure accuracy, reliability, and unbiasedness. This helps to mitigate any potential risks and ensures that AI algorithms make decisions that align with the organization's goals and values. A combination of statistical techniques, like cross-validation, and simulation studies, should be deployed. Also, assess model performance on diverse and representative data sets to ensure fairness. Models should be regularly fine-tuned to maintain accuracy and alignment with fair decision-making principles.?

Invest in the right expertise

With the right technical expertise, executives can ensure that AI models are trained on diverse and representative data, validated using rigorous testing methodologies, and deployed with robust safeguards to prevent bias and unintended consequences. Investing in the right technical expertise is essential to building trust with end-users and realizing the full potential of AI-assisted decision-making.

Keep up with AI advancements

Executives should keep up with advancements in the field of AI and machine learning to ensure that they are using the most current and effective technologies. This includes attending industry events, participating in training and development opportunities, and staying current with the latest research and trends.

Improve & Democratize Business Decision-Making

To realize the full potential of AI-assisted decision-making, executives must adopt a comprehensive and responsible approach to avoid potential pitfalls and ensure the solution is well-received and contributes to business success. Ultimately, it’s leadership that empowers the cultural changes and informs the investments that lead to transformative AI project results.

Check out Infocepts for more insights about the real-world potential of AI-assisted decision-making.