Considering your use cases, it’s time to explore the AI technology landscape. Discover the various AI tools and how to match them with your selected use cases. We’ll revisit the three types of cognitive technologies—process automation, cognitive insight, and cognitive engagement—to help you make informed decisions. Additionally, determine how third-party organisations can assist you during the ideation phase.
Before diving into the selection process, let’s take a moment to survey the vast AI technology landscape. The world of AI is evolving rapidly, with new tools and solutions emerging regularly. Understanding the tools at your disposal is essential to make informed decisions.
Three Key Types of Cognitive Technologies
In our eBook How to Leverage AI into Your Existing Investments, we introduced three fundamental categories of cognitive technologies: process automation, cognitive insight, and cognitive engagement. Each type serves different purposes and can be matched with various use cases. Let’s look at these categories to help you align your AI strategy with the right technology:
What is Process Automation?
Process automation is all about automating repetitive, rule-based tasks traditionally performed by humans. This technology is particularly useful in industries with high volumes of routine tasks, such as finance, manufacturing, and customer service. Robotic Process Automation (RPA) is a prime example of process automation technology, allowing software robots to mimic human actions and interactions with various systems.
Benefits of Process Automation
Substantial cost savings: RPA has consistently demonstrated its ability to drive rapid and significant improvements in key business metrics across various industries globally.
Increased accuracy: 57% of users attest to RPA’s ability to reduce manual errors, leading to more precise and error-free operations. Source
Improved compliance: 92% of users confirm that RPA meets or exceeds expectations to ensure better compliance with established regulations and standards. Source
Enhanced productivity: 68% of the global workforce believes that automation, enabled by RPA, will significantly enhance their productivity levels. Source
Takeaway questions for you and your team:
Determining whether your process is a suitable match for Robotic Process Automation (RPA) involves carefully evaluating its characteristics. RPA is best suited to tasks that are highly manual and repetitive. To assess if your process aligns with RPA capabilities, gather your team and brainstorm the following questions together:
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Which specific processes within our operations are highly manual and repetitive?
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What are the most time-consuming and error-prone tasks currently performed manually?
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Are there clear, rule-based decisions involved in any of our processes?
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How might automating these processes using RPA benefit our business in terms of cost savings and efficiency?
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What are the potential risks or challenges we should consider when implementing process automation in our operations?
By assessing your process against these questions, you can determine whether it aligns with the strengths of Process Automation.
What is Cognitive Insight?
Cognitive insight refers to the capabilities of artificial intelligence systems to mimic and replicate human-like cognitive processes to gain a deeper and more nuanced understanding of data and information. These insights involve AI systems leveraging advanced techniques like natural language processing (NLP), machine learning, reasoning, and problem-solving to extract meaningful knowledge from complex datasets.
The goal is to enable the AI system to analyse data in a more human-like manner, understand context, and provide actionable information.
Benefits of Cognitive Insights:
Enhanced Decision-Making: Cognitive insight technologies empower organisations to make data-driven decisions. By uncovering hidden patterns and trends within large datasets, businesses can better understand their operations, customers, and market dynamics. This leads to more informed and strategic decision-making.
Efficiency and Cost Savings: Automating data analysis and insight generation reduces the time and effort required for these tasks. This results in improved operational efficiency and cost savings, as fewer resources are needed for manual data crunching.
Identifying Bottlenecks: Cognitive insights can also identify bottlenecks in business processes. When information flow is interrupted or not distributed optimally, it can lead to inefficiencies in operations. Cognitive insights can help pinpoint these bottlenecks and suggest solutions to optimise information flow.
Identifying Business Opportunities: Cognitive insights can be instrumental in helping businesses identify opportunities for growth and improvement. By analysing their existing data and text archives, companies can uncover potential areas where they can benefit and make informed decisions.
Takeaway questions for you and your team:
Determining whether cognitive insight suits your business involves carefully evaluating your data and analytical needs. Here’s a checklist to help you assess the alignment of your use case with cognitive insights:
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In which areas of our operations do we heavily rely on data for decision-making?
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Are there challenges or bottlenecks in data analysis hindering our ability to make informed decisions?
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Can we identify processes where complex data patterns or predictive modelling could provide significant advantages?
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How might predictive analytics help us anticipate market fluctuations, demand changes, or equipment maintenance needs?
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Do we have the necessary data sources, such as historical and sensor data, to implement cognitive insights effectively?
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What are the potential benefits and risks of adopting cognitive insights in our business, and how will they impact our decision-making processes?
What is Cognitive Engagement?
Cognitive engagement refers to the ability of artificial intelligence systems to interact with users in a way that simulates human-like cognitive processes, including understanding language, context, emotions, and learning from interactions. It involves making AI systems more aware, adaptable, and capable of engaging in meaningful and relevant conversations with users.
Generative AI platforms like ChatGPT and Gemini fit into this category.
Benefits of Cognitive Engagement
Enhanced Customer Service: AI-driven chatbots and virtual assistants with cognitive engagement can provide efficient and personalized customer support 24/7. They can understand customer inquiries, address issues, and offer solutions, improving customer satisfaction and reducing response times.
Improved User Engagement: Cognitive AI can create engaging and interactive user experiences, resulting in increased user engagement and retention. Gamification and conversational interfaces are examples of how cognitive engagement can make applications more captivating.
Data Insights and Analytics: AI with cognitive engagement capabilities can analyze and interpret data more effectively, providing businesses with valuable insights. This helps in data-driven decision-making, identifying trends, and optimizing operations.
Streamlined Business Processes: Cognitive AI can assist in automating and optimizing various business processes, such as data entry, document processing, and workflow management. This leads to cost savings and improved efficiency.
Employee Productivity: Businesses can use AI-powered virtual assistants to help employees access information, schedule meetings, and perform routine tasks, increasing overall productivity.
Takeaway questions for you and your team:
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How can cognitive engagement improve our existing processes and enhance human-machine interactions?
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How might cognitive engagement assist in addressing challenges related to complex manufacturing processes and the need for timely human intervention?
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Is there equipment in our operations where predictive maintenance can have the most significant impact on reducing downtimes and costly failures?
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Can cognitive engagement help us better monitor the health of our machinery and alert us to potential issues proactively?
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Are there specific inspection points or processes where cognitive engagement can be integrated to detect defects and ensure product consistency?
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Can cognitive engagement help us in real-time decision-making when quality issues arise during production?
Matching Use Cases with AI Technology
The key to selecting the right AI technology is matching your specific use cases with the appropriate cognitive technology category. Carefully evaluate your business objectives and requirements to make an informed choice. This step is critical in ensuring that your AI investments yield the desired results and drive business success.
Third-Party Organisations: Your Allies in Ideation
Navigating the AI technology landscape and matching it with your use cases can be daunting. Fortunately, you don’t have to go at it alone. Third-party organisations that specialise in AI, like Aicadium, can be valuable allies during the ideation phase. These organisations bring expertise, experience, and a fresh perspective. They can help you identify the most suitable AI technologies for your needs and assist in the development of a robust AI strategy.
Stay tuned for the final part of our series, where we’ll discuss the crucial phase of implementing AI solutions and managing the transformation within your organisation. In the meantime, take the time to explore the AI technology options available, and don’t hesitate to reach out to us to ensure your AI endeavours meet your company-wide objectives.
