The Case For An AI Productivity Suite
An AI productivity suite combines all the elements required to move from data discovery, data design, to model selection, fine-tuning, building automated processes and more.
Introduction
I believe a key differentiator for putting AI to work is a complete and comprehensive AI productivity suite.
An AI productivity suite holds immense importance in today’s digital landscape, offering a comprehensive set of tools and services powered by artificial intelligence (AI) to:
Enhance efficiency,
Streamline workflows (Process Automation and intelligent APIs)
Optimise productivity across various domains.
By creating intelligent workflow-like APIs, AI-driven functionalities can be integrated into everyday tasks and processes, such a suite empowers individuals and organisations to accomplish more in less time, unlocking new levels of innovation and competitiveness.
Exposing AI To Business Processes
Within the GALE framework open-source models can be selected, and deployed into a private workspace. These models can be exposed via an API for use in applications etc.
There is also an option of no-code fine-tuning of models…however, as I stated in the introduction, exposing AI functionality and pieces of automation to business processes are key.
Considering the image below, an astute method to AI implementation is to have a measured approach of creating smaller pieces of automation. For instance, with GALE, workflows can be built via a no-code UI. These workflows can range from complex to simpler flows.
Business rules, guardrails and data scanners can all be part and parcel of a flow. Hence flows act as AI components which are exposed via an API call, or imbedded in Python or Node.js code; as shown below.
In a recent study Gartner took the approach of taking an area of business or a larger technology, and breaking it up into smaller use-cases. Subsequently each of these use-cases can be addressed in a piecemeal fashion.
Automation flows act as an enabler to expose and put fine-tuned models and RAG to work.
Process Automation
One of the key advantages of an AI productivity suite lies in its ability to automate repetitive tasks and decision-making processes, allowing users to focus their time and energy on high-value activities.
And due to its no-code nature, flows can be seen as disposable. Users might build flows for a single instance where a task needs to be achieved in fast and effective way.
Overall, the importance of an AI productivity suite lies in its ability to empower individuals and organisations to achieve their goals faster, smarter, and with greater precision.
Typical use-cases are:
Streamline Invoice Processing
Automating Customer Onboarding
Improving Patient Record Management
Enhancing Supply Chain Efficiency
Optimising Recruitment and Onboarding Processes
Automating Data Entry and Validation
Compliance, Ensuring Regulatory Adherence in Banking
AI-powered tools can significantly reduce manual effort and increase productivity.
Moreover, an AI productivity suite enables personalised and context-aware assistance, tailoring recommendations and suggestions to individual user preferences and specific task requirements. This personalised approach not only enhances user experience but also drives better outcomes by providing timely and relevant insights and recommendations.
Furthermore, the predictive capabilities of AI within a productivity suite can help users anticipate future trends, identify potential risks, and make informed decisions proactively. By analysing vast amounts of data and extracting actionable insights, AI can assist users in optimising strategies, mitigating risks, and capitalising on emerging opportunities.
In addition to improving individual productivity, an AI productivity suite also fosters collaboration and teamwork by facilitating seamless communication, knowledge sharing, and project management across distributed teams. By providing intelligent tools for collaboration and coordination, such a suite enables teams to work more efficiently and effectively towards common goals.
In Conclusion
Chatbots, Voicebots, Conversational User Interfaces…thrive on context, and on being able to execute customer requests in real-time and give the user instant feedback.
Chatbots have always been at the behest of dumb APIs with the absence of any intelligence or underlying processing of the request. This logic had to be built within the chatbot flow.
Not anymore with a AI productivity suite, purpose built smart APIs can be made available to the chatbot. Ensuring that each back-end interaction is customised specific to the use-case and relevant context.
I’m currently the Chief Evangelist @ Kore AI. I explore & write about all things at the intersection of AI & language; ranging from LLMs, Chatbots, Voicebots, Development Frameworks, Data-Centric latent spaces & more.
