As technology is layered into every element of business, simplifying workflows and improving productivity, no one can deny that it is essential to operations. The exciting potential of innovation through Artificial Intelligence (AI) has indisputably accelerated the rate of industrial change. AI offers a seismic shift in enterprise decision-making processes by leveraging rules and data to initiate precise, efficient decisions for greater profitability.
When teams are supported with Decision Automation (DA), individuals can drive strategic planning, prioritize optimization, engage in meaningful tasks, and improve performance. For organizations to realize the benefits of automated decision-making, it’s important to consider where it will be most effective, how data analysis informs decision-making across departments, and the core technologies involved.
The Profound Potential of Automated Decision-Making
Automated decision-making is defined as making decisions by technological means without requiring human intervention. In practice, automated decision-making systems augment the reasoning process for human counterparts to take action. The type of decisions can vary in complexity, and many AI systems offer decision-making capabilities.
Empowered by algorithms to assess and interpret limitless data sets, the real potential of Decision Automation is the ability to distill information quickly and accurately for further interpretation. The value isn’t simply an equation of time or effort but the quality of outcomes DA facilitates.
Here are some examples of what automated decision-making can offer:
Enhanced Compliance Management
Following rules-based programming, DA will monitor and analyze compliance data according to an exact understanding of regulations. As it identifies potential noncompliance concerns, it will alert personnel for review and remediation. This offers a strong position to reduce the risk of penalties while ensuring decisions are supported by accurate reporting without misinterpretation or individual discretion.
Advanced Patient Care
Precision is critical in optimizing health outcomes as medical knowledge and care continue to advance. With the integration of AI in healthcare, patient data can be structured, indexed, and analyzed for more accurate diagnosis and improved error detection. This technology enables faster, more attentive patient care with automated decision support, allowing medical professionals to make informed decisions based on data-driven programming that conducts rapid analysis of structured and unstructured information such as test results in combination with patient history. By identifying patterns and making recommendations, the program keeps medical professionals better informed as they develop diagnoses and treatment plans.
Optimized Inventory Management
DA can be applied to rebalance stock levels and optimize pricing in real time. Automated monitoring of inventory helps minimize errors and costs while empowering employees to make responsive, strategic decisions for business growth informed by insights such as forecasted demand.
These examples demonstrate the potential for better automated decision-making processes, but they should always be established with oversight and care. Decision Automation relies on comprehensive and accurate datasets to prevent false insights or bias informed by the wrong patterns.
The ethical implications of manipulating automated individual decision-making have led to specific legislation governing the use of personal data for profiling. Regularly monitoring and maintaining DA processes is necessary to ensure appropriate data collection and application.
Understanding how the system makes its decisions—not just what it decided—will allow you to recalibrate it and improve decision-making with reliable, data-driven insights. With this approach, you’ll achieve better outcomes that align with business goals while minimizing risk.
The Importance of Insights
Data analysis is an essential driver of decision-making across departments. By using data to inform decisions, businesses can gain perspective to optimize processes. This leads to not only increased accuracy and efficiency but also creative problem-solving.
When data is easy to access and review, it creates a common language for teams across departments to communicate. They can come together for strategic planning without speculation, and insights motivate action and engage greater collaboration. Data analysis can spark curiosity and innovation.
Automated decision-making systems deliver high-quality, actionable insights when they’re built with:
- Centralized data collection and storage for consistent, reliable analysis.
- A Center of Excellence (CoE) to guide and scale automation models.
- Cross-departmental collaboration for data-driven decisions.
- Periodic training of the AI as data evolves.
The objective is not for Decision Automation to occur independently of oversight. Instead, the focus should be to integrate the systems into departmental workflows as the primary data processor.
Rather than directly engaging with the data, consider the appropriate conclusions to draw from the data analysis for informed decision-making. This will minimize manual effort and detect non-linear patterns, but it is crucial to review the analysis for accurate interpretation. Ultimately, there is additional information beyond the reach of AI—and that is where the synergy between automated decision-making and human knowledge occurs.
Automated Decision-Making Systems in Motion
As a tool, Artificial Intelligence excels at replicating and enhancing human decision-making processes with computer science, mathematics, and data. Through adeptly designed algorithms and comprehensive datasets, AI systems learn from patterns and attributes of the information it processes. With guidance, these systems can support Decision Automation processes of varied complexity and sophistication.
For processes governed by well-defined rules, a fundamental technology for automating decisions is Robotic Process Automation (RPA). This system uses digital robots to execute simple, routine determinations based on criteria set by your business to accelerate operational efficiency.
Intelligent Process Automation elevates the capabilities of Decision Automation by combining multiple technologies for data-driven decisions. It applies RPA with Artificial Intelligence, Machine Learning, and Optical Character Recognition to enhance business outcomes with dynamic optimization. These complementary tools, used together, compose an automated decision-making system capable of reading, extracting, and understanding unstructured data; streamlining end-to-end processes; and improving predictive outcomes with progressive accuracy.
Below are a few examples of automated decision-making systems:
Supply Chain Management
Real-time data analysis and automated decision-making with AI can be transformative for end-to-end automation of supply chain management. A good system can analyze demand forecasts, production schedules, and transportation logistics to optimize reorder points and distribution routes for efficient resource allocation and reduced transportation costs.
Customer Service
Interpreting interactions and historical data, AI can be deployed for enhanced engagement with customers. It leverages automated decision-making and analysis to recommend responses for customer self-service, personalize communication, and prioritize inquiries.
Fraud Detection
Through targeted pattern recognition and analysis of historic data, Machine Learning can detect suspicious transactions. Potentially fraudulent activity can be verified or dismissed by human review, and the program learns to better interpret patterns with greater accuracy. This makes financial oversight more efficient and reduces risk.
Decision Automation facilitates quick, strategic decision-making that can transform business operations. Leveraging rules and data, this technology offers increased accuracy, faster processes, consistency, and a competitive advantage that drives profitability.
Practical Considerations for Data-Driven Decisions
In enterprise organizations, it’s common to encounter challenges with legacy tools and differences in data collection and cleanliness across departments. For this automated decision-making process to offer accurate and effective insights, the data being used needs to be complete and clean. If there are any issues in data management across different teams, it’s important to address them before attempting to implement Decision Automation at scale.
The broad capability of AI resources to follow rules-based programming and process both structured and unstructured data makes it possible for you to create processes that bridge legacy systems with emerging technologies. This could potentially solve the challenges of disparate or dated workflows in the short term, enabling sustainable automation models you can adapt and build on over the long term.
The value of real-time data analysis to provide timely insights and enable agile decision-making cannot be overstated. The ability to act quickly and interpret data without delay provides organizations with the ability to respond to evolving industry landscapes with urgency.
As you prioritize business outcomes with automated decision-making, be mindful not to sacrifice responsible governance for speed. Take care to safeguard the integrity of data with reliable, meaningful information, and observe data security and privacy measures. Whichever AI resources you consider, maintain systems with continuous feedback loops to refine algorithmic programming and adjust implementation to meet changing objectives.
Ready to Start Making Decisions with Automation?
The next step in pursuing automation begins with knowing how to build momentum you can keep up with. Read our e-book How Scaling Fuels Decision Automation for Enterprise Organizations to learn more.
