Don’t Just Mine the Past – Mine the Future: Productivity Intelligence combined with Generative AI for Business Process Optimization 

Welcome to the intersection of innovation and efficiency, where productivity intelligence meets generative AI, to unlock potential for optimising business processes and rigorously testing enhancements before they are implemented.  

It’s a given that enterprises strive to improve efficiency and effectiveness. When it comes to their processes, the trick is to not only eliminate inefficiencies today but anticipate future needs. This is where productivity intelligence in combination with generative AI, offers new capabilities; firstly to analyze current workflows, and secondly, to project what optimised processes might look like, including any flaws, and consequently, improve those before implementing them. When done on a continuous improvement basis, where the outcome of process improvements are measured and analysed, organisations can fine tune their improvements becoming more agile, efficient and effective at the same time.  

Understanding Productivity Intelligence 

Productivity intelligence is garnered from people’s interactions with processes and the technologies that underpin them. It offers a detailed view of how work flows across various process steps and enterprise applications, highlighting inefficiencies and bottlenecks that might otherwise go unnoticed. Think of it as having a 24/7 lens on your operational processes and supporting systems, providing granular insights into every step taken to complete a task. 

The Power of Generative AI 

Multimodal generative AI, powered primarily by Large Language Models (LLMs), leverages a combination of AI capabilities and machine learning algorithms to create new content and scenarios from existing data. In the context of business processes, it can analyze productivity intelligence to recommend improvements and optimizations. It can even predict potential issues or risks associated with new versions of processes and suggest steps to mitigate them. 

For high-volume, complex processes, you might want to train the AI to produce synthetic transaction data to test the proposed new process version. Testing with synthetic transaction data enables the organization to evaluate process performance under exceptional scenarios as well as everyday operations, thereby improving the resilience of new critical business processes. 

A Symbiotic Relationship 

When productivity intelligence is fed into generative AI it leads to a powerful tool for both understanding the present and planning for the future. Here’s how this combination works in practice: 

1. Comprehensive analysis: Productivity intelligence provides detailed data on current processes. This data serves as the knowledge base for generative AI, which utilizes the insights gained to describe the inefficiencies and recommend improvements, all done using natural language that is easy for humans to follow. 

2. Predictive modelling: AI models can be used to predict the issues and risks that might come about as a result of the improvements made to the process. Given throughput test data, they can be used to simulate the performance of the new process to show the impact of different changes.  

3. Continuous improvement: With continuous collection of data for process intelligence and ongoing analysis and suggestions from AI, businesses can establish a cycle of continuous improvement. The use of the technologies together ensures that processes are not just optimized once but are continually refined to meet evolving needs. 

4. Scenario planning: Powerful generative AI can help businesses prepare for various future scenarios by modelling different outcomes based on current data. This capability is particularly useful in strategic planning and risk management, where understanding potential future states can inform better decision-making today. 

Real-world Applications 

There are many practical applications of the combo of the technologies. KYP.ai’s Concierge based on our LLMs can analyse the process intelligence that our platform provides to answer users’  questions on a variety of topics including work patterns. For example it can analyse work patterns and offer practical advice and guidance on how to improve them; perhaps there are too many Teams meetings that eat into productive time, or some problems with long working hours affecting employees’ work/life balance. The effects of changes can be equally assessed and reported on in plain natural language that is the beauty of LLMs.   

Other use cases including:  

• Customer service: process data can be analyzed to show how customer service representatives handle calls, identifying common issues and inefficiencies. Using generative AI, new workflows can be tested to improve efficiency of the process and customer satisfaction. 

• Supply Chain Management (SCM): By tracking workflows through supply chain processes, the causes of delays and inefficiencies can be identified. Generative AI can then be used to analyse the productivity intelligence to optimize processes such as routing and inventory management, reducing costs and improving delivery times. 

• Healthcare: In a medical setting, process intelligence can identify time-consuming or redundant process steps in care administration. Generative AI can explain the findings and test any changes to the process by feeding synthetic data to the proposed new workflow.  

The Future of Process Optimization 

The combination of productivity intelligence and generative AI represents a significant advance in the field of process optimization. By providing detailed insights into current workflows and projecting future needs, when the two technologies are combined they enable businesses to stay competitive in a changing market. 

Watch Miroslaw Bartecki, our CTO and co-founder, explain KYP.ai Concierge that combines the power of the two technologies, to Sarah Burnett, our Chief Technology Evangelist.