December 2020

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 Bringing AI Innovation to your enterprise

With all the buzz around AI, where do you start to bring AI to make your company
to power innovation by the prescriptive power of AI

Sudha Jamthe is
a Technology Futurist and CEO of IoTDisruptions
who mentors business leaders with Capstone projects to solve industry AI problems at Stanford Continuing Studies and online a

Contributing Editor

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I have thinking about the increased demand for innovation amidst the covid quarantines to offer the technologies needed to thrive in our remote access and social distanced lives. 2020 has been a testimonial to human resilience and adaptability. The isolation that comes work social isolation has led to leadership at an individual level though many might not see it that way when living alone separated from family and friends and working and doing all that we define as life, albeit remotely using a 2D screen.

Today reminds me of the early days of the Internet (link to Dec issue anchor article about RFP) when suddenly our works showed promise of expanding in fun and exciting ways and we did not know about much of the technologies that was being built. Then came a period of consolidation and several platforms and layers of technology set in.

I believe that we are at that stage of promise of opportunities to innovate in our businesses and create operational efficiencies and new revenues. This time we know what comes next and can shape the landscape of technologies to work for us, on our terms, as a business and as individuals.

What does it take to bring AI innovation to your business? 

With all the buzz around AI, where do you start to bring AI to make your company to power innovation by the prescriptive power of AI. Yes buildings need to be connected for remote access and building managers will be better off knowing how to manage the reduced personnel that come into buildings proactively so they can manage the operations and energy efficiency of buildings.(link to Edge AI article from Nov issue)

AI and data innovations have been promised to offer the predictive power of predictive maintenance of assets, predictive behaviour of people, and in the predictability of the personalization of this relation of people to places, things and actions. But AI pilots fail faster than IoT pilots at 85% of AI proof of concepts stuck without scaling. Similar to typical enterprise pilots the challenge is in being able to show ROI. With AI howecer, somehow, business people involved are optimistic about the AI technology even for failed pilots  They believe that AI should be able to solve their problems and instead they blame themselves for the performance of the algorithm, lack of clean data to train the models and not having enough budget to hire more datascientists.

The Growing rift between Business users and data scientists

I believe that the biggest hurdle to get started with AI Innovation in a company or business unit does not lie in the technology or data or budgets. The real challenge lies in the growing rift between the data scientist and business leaders.  ( I want to highlight this sentence as call-out for the article)

1  Datascientisr speak the language of AI and define problems as inputs and outputs to an AI model. For example, a building owner might want to reduce energy cost by 20%  They might do this by optimizing which machines to use by reducing usage of  less sustainable ones. Another approach could be to see which machines are used less by people and adapt the machine operations to reduce the overall cost. Each of these might drive the same business outcome of reduced costs but it translates to different data science problems. You ask the AI to find the machine that consumes less energy in the former and you ask the AI for the machine that is used less by the user in the former. Both arrive at the same business ourcome but the underlying AI is a totally different model.

2. Data is the language of training the AI. Business users own customer data, production data or operational data. Data scientists train the initial model using public datasets based on their assumption of business needs. They will need business users to bring their business acumen to translate the context of the company data to improve the AI models.

3. AI algorithms are a black box today. How an AI model makes a prediction is not transparent to the business user who might be concerned about trusting a model that challenges many years of their business experience in their industry. So when an AI recommends to shut down a turbine how can a business owner with decades of experience listen to it? The realiry translates to trust between people and the AI is not questioned but it comes down to the trust on the Datascientist, trust on themselves to understand the complexity of AI predictions and the trust that they had contributed to capturing years of business acumen into an AI model.

These reasons cause a disconnect between the business users ask and data scientist needs to talk the language of data. Then in lies the failure of 85% of AI pilots not scaling past a proof of concept while the exuberance on AI solving business problems remains intact.

How do you bring AI Innovation to the enterprise ?

1. Start with the customer.

Focus on solving customer problems and arrive at if and how AI can solve the problem. Business users need to stay focused on business problems but need to learn to translate their ask as AI problem statements.

2. Quantify business outcomes.

If you want a 50% reduction in churn, you could achieve it even if the AI algorithm is not fully optimised and performs at a 20% statistical confidence.  If you have a clear quantifiable expectations of business results, then the business user can improve the AI model in partnership with the data scientist to exceed expectations.

3. Focus on the data

Data  prep takes 70% time in AI problems. So look at what data is needed to solve your customer problem and where are the gaps in the data and clean your data to get good veracity and all desired features before you start the AI modeling cycle.

The way forward to get started with AI innovation is to get the business user on the same page as the data scientist to become AI Translators. This is done by a new set of evolving tools and platforms called NoCodeAI.

What is NoCodeAI?

NoCodeAI started as a way for the datascientist to automate the AI model selection process to find the right AI algorithm for a specific problem, known as AutoML. AutoML stands for automating Machine Learning.  Recently it is evolving to a new field with a platform for the business user to use drag and drop interfaces to build AI models. This helps the business user focus on their data and convert their problem to a data science problem and understand the complexity of building the model. The business user does not build the model. The data scientists still builds the models .This frees up the Datascientist to focus on solving more complicated data science problems and offer simpler models that the business user can apply on their data to optimize their data to find solutions for desired business outcome.

We are in early stages of NoCodeAI but it is time for the business user to understand their role in powering AI Predictions to achieve the promise of innovation that is inviting us as we manage the covid reality and get ready to request proposals for the next round of innovations.

Sudha Jamthe is a Technology Futurist with a learning community with online courses and a live learning lab for product and business users to bring innovation to their business as they pivot their careers with NoCodeAI for business managers.


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