who have implemented AI report increased efficiency and decision-making throughout their teams. Yet only 12% of companies are actually benefiting from this technology. 58% of those companies
AI is rapidly reshaping the business landscape and is now poised to revolutionize decision-making processes. 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 challenges which organizations face in implementing AI-assisted decisions
Though AI-assisted decision making is a quickly growing field, many data and analytics executives struggle to successfully implement and scale these solutions within their organization. Here are the top challenges you’ll need to tackle:
- 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 inefficiencies.
Best practices for implementing 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 a few 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.
There are many other challenges which organizations face and best practices which will help you realize the full potential of AI-assisted decision-making. Read our ebook to learn more tips.
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