Headshot of Prateek Joshi, CEO and Founder of Plutoshift AI Industrial Technology
Prateek Joshi
CEO and Founder

Large companies have been tackling the issue of AI for the last few years. Business leaders are often faced with the problem of figuring out how to use this technology in a practical way. Any new technology needs to be packaged into bite-sized pieces to show that it works. These "success templates'' can then be used to drive enterprise-wide adoption. But should they do it all at once? How do you ensure that you're not boiling the ocean? How can a company package AI into bite-sized pieces so that their teams can consume it? From what we've worked on with our customers and seen in the market, there are 5 steps to do it:

1. Start with the use case
It always starts with a use case. Before launching any AI initiative, the question you should ask is whether or not there's a burning need today. A need qualifies as "burning" if it has a large impact on your business. If solved, it can directly increase revenue and/or margins for the company. We need to describe this burning need in the form of a use case. These use cases are actually very simple to describe as shown below:
- "We're using too much electricity to make our beverage product"
- "We're taking too long to fix our pumps when someone files a support ticket"
- "We're spending a large amount of money on chemicals to clean our water"

2. Pick a data workflow that's specific to an operation
Once you figure out the use case, the next step is to figure out the data workflow. A data workflow is a series of steps that a human would take to transform raw data into useful information. Instead of figuring out a way to automate all the workflows across the entire company, you should pick a workflow that's very specific to an operation. This allows you to understand what it takes to get something working. We conducted a survey of 500 professionals to get their take on this and we found 78% felt supported by their team leaders when they embarked on this approach. Here's the full report: Instruments of Change: Professionals Achieving Success Through Operation-Specific Digital Transformation

3. Be selective with data
Once you pick a workflow, you need to understand what specific data is going support this particular workflow. If you try to digest all available data, it leads to chaos and suboptimal outcomes. If you're disciplined around what data you need, it will drive focus on the outcomes and ensure that the project is manageable.

4. Create a benefits scorecard collaboratively
The main reason you're deploying AI is to drive a specific outcome. This outcome should be measurable and should have a direct impact on the business. You should include all stakeholders in creating a benefits scorecard. The people implementing the AI solution should hold themselves accountable with respect to this benefits scorecard. The time to realize those benefits should be short e.g. 90 days.

5. Have the nuts-and-bolts in place that enable you to scale
Let's say you successfully execute on this PoC. What's next? You should be able to replicate it with more use cases across the company. There's no point in doing this if the approach is not scalable. Make sure you have a data platform that supports deploying a wide range of use cases. The nuts-and-bolts of the platform should enable you to compose many workflows with ease. What does "nuts-and-bolts" include? It includes automating all the work related to data -- checking data quality, processing data, transforming data, storing data, retrieving data, visualizing data, keeping it API-ready, and validating data integrity.

Most Recent News & Articles

Plutoshift logo with Accelerator 100 logo

Plutoshift Selected to Join Exclusive Sustainability Innovation Program

Plutoshift was born out of the desire to unlock the potential of AI to address energy and water challenges. And since our founding in 2017, sustainability and resource conservation...
Towards3Z Podcast

Towards 3Z Podcast: Zero Emissions, Zero Downtime, Zero Waste and Digital Transformation

I was honored to join Albert Vazquez-Agusti on Towards 3Z’s first podcast to talk about zero emissions, zero downtime and zero waste in a world where industrial transformation and...
Machine learning

6 key ingredients of successful Machine Learning deployments

Machine Learning (ML) is a vehicle to achieve Artificial Intelligence (AI). ML provides a framework to create intelligent systems that can process new data and produce useful output that...

The Water Values Podcast: Digital Transformation with Prateek Joshi

CEO Prateek Joshi talks about digital transformation in the water sector. Prateek hits on a number of important and practical points in a wide-ranging discussion on data, AI, and...

100th Episode Of The Dan Smolen Podcast

Prateek Joshi, Founder and CEO of Plutoshift, discusses how A.I. makes the world a better place on the 100th episode of The Dan Smolen Podcast. The Dan Smolen Podcast...
Data Security image

8 Security Standards For Safeguarding Customer Data

As a company, Plutoshift has many responsibilities towards our customers, teammates, vendors, and the environment. We manage critical data across many facets of our business. Being accountable for data...