Artificial Intelligence Transformation At Lithe

[Written by a human being, co-founder of Lithe Abraham Schoots]

Every week new AI tools pop up. It’s hard to keep track of all of these innovations and so hard to keep up. I’ve just heard a song which was a remake of a Rihanna song, but then in the style of a 70s soul train song! Yes I can still hear that it’s not professionally produced, but this will soon change.
And I won’t be able to make sure what I’m listening to, who I’m chatting with, who’s picture I’m looking at. I think we can all imagine the confusion. 

Wasted human effort due to repetitive tasks
At Lithe, all our team members are very excited about these tools, as early adopters and hobby programmers we’ve been experimenting with tying multiple tools together to automate most of our processes in internal, and client operations, sales and marketing. Yet at the same time, we all enjoy the human contact that we have. The inefficiencies around us are more and more because of our short term memories not working, or a glitch in technology (e.g. new connections with Zapier failing). 

We’re in the midst of an AI revolution
Sometimes things don’t move forward yet at other times there’s a snowball of movement. Artificial intelligence, and then specifically the more recently visible Generative AI, is very likely currently undergoing a revolution. Things are moving forward so quickly that it has become impossible to follow everything.

So, Innovation often comes in waves
Spaceflight in the 1960s, the Dot-Com era, smartphones, or crypto going mainstream, hybrid and later electric cars going mainstream. All came in waves, an early, understream of innovators and weirdos, and early majority, then crossing the chasm, the tipping point whatever and the early majority (you're still the cool people!), late majority, Uhoh, and then the laggards. 

Everett Rogers, 1962 Diffusion of Innovations Model

The innovators are always trying and testing the latest, newest tools, they sign up for everything, leave more digital trash than anyone else, sometimes they sign up but they aren’t loyal to SaaS products nor banks, or insurance companies, nor energy companies.

So many people are - all of a sudden – a specialist

And making more noise makes you more right when it comes to innovation it seems, but really is this right? I don’t believe so. What makes one a specialist? Well that takes a lot of reading and work and more even practical experimenting, being open to being wrong most of the time and then trying to be less wrong every day.

Artificial Intelligence Transformation At Lithe

Our people have been innovating with all of these innovations in the past decades and we have learned how to automate, robotise and now how to use the right tools to increase efficiency and with that, effectiveness, make work transparent, understand why a piece of work is needed. Some of us where ordering pizza via with bitcoin in 2014, those were some expensive pizzas, and others where coding in C# in 1994.

That’s why we’ve launched our new service offering: We’ll review the way your teams work with their tools, and then help you to use the latest AI tools to increase productivity. We then also train your people on using it.

Whether it’s Jira plugins for software teams that help predict the future stream of work, digital designs, UX design improvement iterations, language creation through Generative Artificial Intelligence (Gen AI) and so much more. When human input is more than 30%, I consider this a waste. 

70% Gen-AI with 30% Human interaction

Low-code, Large-Language Models, API-integration for email?
You shouldn't write a full email. Saying Dear and Regards, choosing white space and wishing each other a nice weekend is a wasteful time consuming routine that will be automated, based on your earlier interactions. One can be automated, but what you want to do, your creativity is still yours, yet augmented. 

Wastes refined: The problem
Most of the day we type things on keyboards, we read information, interpret and answer, we open windows, click on browsers, we fill in webforms and we try to find our card details, and copy and paste a line into our calendars. Most of this is wasted time. When we started working on Scrum and Kanban for software teams, we learned how much waste most teams have. People wait for each other during meetings, there’s downtime and under load, overload, imbalance and more. Norwegians already get this, they don’t say please often, they say thanks (“takk”) quite a lot though.

Digital Artificial Intelligence Transformation done right

With our new service offering we uncover manual wastes in your teams, Whether it's improving the way that software is being tested and developed with low-code and test automation, towards fully integrated delivery cycles. Or whether it’s creating a new content strategy, or new processes, streamlining websites, contracts, tools and operations. Maximising towards your outcomes whilst making work more fun because you’re more effective now while typing less! 

Jira Burn Up Prediction Improvements:

• Machine Learning-based Burn down/Burn Up Charts: These tools use historical data and project trends to predict future completion dates with greater accuracy, allowing for better resource allocation and project management. 

• Risk Analysis and Mitigation Recommendations: Some AI-powered tools can analyse project data to identify potential risks and suggest mitigation strategies, helping teams stay on track and avoid delays. 

Building Backlogs:

• AI-powered Prioritization Tools: These tools analyse user feedback, feature usage data, and business goals to prioritise backlog items based on potential impact. 

• Topic Modelling: This technique can automatically identify recurring themes and topics in user feedback, helping product managers understand user needs and prioritise backlog items accordingly. 

Gathering Feedback:

• Sentiment Analysis Tools: Analyse user reviews, social media comments, and survey responses to gauge user sentiment and identify areas for improvement. 

• Chatbots and Virtual Assistants: These AI-powered assistants can gather valuable user feedback through chat conversations, allowing for real-time insights and quicker iteration cycles. 

Coding Copilots:

• Context-Aware Autocompletion: These AI-powered tools suggest code completions based on the current context, reducing typos and speeding up development..

• Bug Detection and Code-smell Recommendations: AI can analyse code to identify potential bugs and suggest improvements in code structure and maintainability. 

Low-Code Development Platforms:

• Drag-and-Drop Interfaces: These platforms allow developers with less coding experience to build basic functionalities and prototypes quickly, freeing up time for software engineers to focus on complex features. 

• AI-powered Code Generation: Some platforms use AI to suggest snippets of code or even generate entire functionalities based on user-defined parameters. This can significantly reduce development time. 

The future of work is likely to become more and more focused on creativity and cognitively focused, more than  typing on a keyboard and clicking on windows and buttons. 

Thanks, and yes I’m not a robot.

Abraham Schoots