Plutoshift Secures Funding From Akron Fusion Ventures, Strengthening Presence In Northeast Ohio

Investor commits funding to Performance Monitoring Platform For Industrial Processes, Providing Manufacturers With A Unified View To Reduce Resource Consumption And Drive ROI.


PALO ALTO, Calif. — May 18, 2020 — Plutoshift, the leader in data intelligence for industrial processes announced it has secured funding from Akron Fusion Ventures, a venture capital fund in Akron, OH with an investment strategy to connect coastal ecosystems with Northeast Ohio.

Plutoshift has developed a specialized view of process performance for industrial companies, offering a unified window into manufacturing operations through highly sophisticated software that leverages Artificial Intelligence to drive proactive insights. 

By unlocking insights from existing data sources in real-time, Plutoshift enables industrial companies to have a holistic view of key cost drivers and resources that impact their business. This approach allows operators, managers and remote workers to stay better informed and take proactive action. The company works with a variety of industries including Food & Beverage, Manufacturing, Oil & Gas, Chemical, Power & Renewables, and related industrial verticals. 

The new investment and partnership will be used to help Plutoshift expand and strengthen its presence in Northeast Ohio where Akron Fusion Ventures has deep roots and a proven track record for bringing Silicon Valley startups to the region. 

“We are very excited to be partnering with Bill Manby and the entire team at Akron Fusion Ventures. The Northeast Ohio region represents a large opportunity for Plutoshift given the number of manufacturing and consumer goods businesses” said Prateek Joshi, Founder and CEO of Plutoshift.

“We work with companies out of Silicon Valley and bring their innovative solutions to the heartland of America. Northeast Ohio is home to hundreds of successful manufacturing enterprises and a growing startup environment. We are excited to partner with Plutoshift and bring their GROUNDED AITM solution to companies in our area” said Bill Manby, Founding Partner of Akron Fusion Ventures. 

Plutoshift was founded in 2017 by Joshi with the vision of connecting the constantly changing realities of the physical world with the monitoring power of intelligent software. This effort resulted in helping industrial operators and remote workers harness the power of existing plant data related to operations, finances, and maintenance spread across different systems. By providing a fundamentally different way of looking at processes that drive their businesses, Plutoshift aims to provide an unprecedented level of information access to operators and managers.

Joshi is an Artificial Intelligence researcher, an author of 13 books, and a TEDx speaker. He has been featured in Forbes 30 Under 30, CNBC, TechCrunch, Silicon Valley Business Journal, and many more publications. He graduated from the University of Southern California with a Master’s degree specializing in Artificial Intelligence. He has previously worked at NVIDIA and Microsoft Research.


About Plutoshift: 

Prateek Joshi launched Plutoshift in late 2017 with the vision of connecting the constantly changing realities of the physical world with the monitoring power of intelligent software. This effort resulted in helping industrial operators harness the power of existing plant data related to operations, finances, and maintenance spread across different systems. Plutoshift is the leader in data intelligence for industrial processes. 


Their cloud-based solution monitors the performance of industrial processes in an automated way for manufacturing businesses. Plutoshift’s GROUNDED AI™ technology transforms passive legacy monitoring systems to active performance monitoring in industries like water, food, beverage, brewing, chemicals, and energy. This enables operators to automatically monitor critical processes and have access to actionable information in real-time. Plutoshift has offices in Palo Alto and Denver.



Melissa Dunn

Director of Marketing, Plutoshift 


Business Water: The Value of Artificial Intelligence in Achieving Sustainable and Resilient Corporate Water Strategies


Will Sarni, Founder and CEO, Water Foundry and Prateek Joshi, Founder and CEO, Plutoshift


Access to water is one of the greatest challenges to business continuity and growth. A key component of this challenge is the effective operational management of water, which plays a critical role in how much of this resource is utilized. Businesses across a range of sectors such as agriculture, energy, food and beverage, and manufacturing are discovering the value of investing in access to water supplies, water use across their value chain (e.g., supply chain, operations and product use) and improvements in the efficiency and effectiveness of infrastructure and assets to manage water (e.g., treatment systems). Businesses also have a critical role to play in ensuring access to water for their customers, workforce, and communities. Increasingly, consumers, customers, investors, non-governmental organizations (NGOs), and civil society favor “brands with purpose” which includes consideration of how businesses are stewards of water.

Download the White Paper here:

Whitepaper_ Business Water_FINAL

Bridging The Gap Between AI Promise and Results: 3 Actionable Steps

By Prateek Joshi:

Analyst firms like Gartner and Forrester have been advising their clients and the industry at large for several years about the dramatic changes automation and AI will bring to the global economy. According to Gartner’s 2019 CIO survey, the number of enterprises implementing AI grew 270% in the past four years. Companies ranging from the manufacturing sector to finance and energy are feeling the rush to identify and implement AI applications that can help them become more data-savvy and profitable in the modern economy.

But in this modern-day data gold rush, few companies are experiencing as smooth or efficient an implementation process as they would like, and even fewer are seeing the focused results they were hoping for with their AI projects.

Challenge 1: Getting up and running

 In our recent survey report of 250 manufacturing professionals with insight into their companies’ AI projects, we found that 72% said they had taken far more time than anticipated to implement the necessary data collection processes for implementing AI. This lack of mature data-collection infrastructure, as well as other factors, has continued to stall companies’ efforts to fully digitize.

When you take into consideration the challenges of laying the groundwork for an efficient AI implementation, it’s not surprising that only 17% of respondents said they were actually at the full implementation stage of using AI at their company. On the other end of the scale, 20% said their company was still assessing the internal resources needed to implement an AI project. In the middle, about 24% said their company was still getting familiar with AI and assessing the potential business and financial value AI could bring.

Solution: Ditch the one-size-fits-all mindset

Companies should find tailor-made technologies that cater to their specific wants and needs. When partnering with an AI firm that can take all of your company’s unique data-collection and business circumstances into consideration, you can more clearly define and develop an implementation strategy that works for your specific business outcomes and can start to provide returns on the bottom-line sooner.

Challenge 2: Dealing with overwhelming scope

 As the analyst community rightfully points out, AI is capable of taking on many different business tasks: usually, ones that are repetitive, data-intensive or need to be performed around the clock. Because of its utility, many companies are finding it challenging to choose clear goals and business objectives for their AI projects. Only 57% said their company implemented AI projects with a clear goal while almost 20% implemented AI initiatives due to industry or peer pressure to utilize the technology.

Given the immense promise and wide range of applications of AI, respondents naturally had diverse goals for their projects:

  • Overall cost savings (54%)
  • Automating tasks (49%)
  • Achieving a more productive workforce (49%)
  • Improving efficiency in business processes (49%)
  • Improving the quality of their products or customer experience (49%)

Solution: Identify crystal-clear outcomes for your AI projects

A vital step in implementing successful AI projects is identifying specific business outcomes and goals. When companies are on the same page about how they define success with AI, they are better positioned to achieve their objectives.

The right AI partner can help your company select appropriate business goals and define a successful ROI. They can also keep the project transparent by measuring performance in real-time and make adjustments as needed.

Challenge 3: Lacking internal support

As with any period of transition, many companies are facing challenges aligning their internal resources in order to fully support an AI project. 34% said their company has struggled to keep its AI projects in scope because there was a lack of expert guidance at the planning phase of the project.

Additionally, 62% said their company took more time than anticipated to acquire internal buy-in and commitment in implementing AI projects; 60% also said their company struggled to come to a consensus on a focused, practical strategy for implementing AI.

When companies are not aligned behind the goals and the strategy of AI project, the entire process can suffer as a result: 34% said their company experienced an internal lack of engagement with AI projects due to a lack of confidence in the technology.

Solution: Create a data-driven culture

An AI initiative cannot be passed down from the board room without internal buy-in from the rest of the company. The right AI system can empower individual operators to take action on data and improve their overall job performance and experience. With an AI system that is accessible and centralized, no matter who the stakeholder is, they have the chance to see their own opportunities for ROI — whether it is financial or organizational.

Set your company up for success

By taking a thoughtful and intentional approach to implementation, companies of any size can quickly achieve positive business outcomes with AI through three steps: ensure your data collection infrastructure is adequate, define clear business objectives, and reinforce a company-wide commitment to AI.


Why Manufacturing Companies Continue to Struggle with AI Implementation Projects

By Prateek Joshi 

Manufacturing companies know that Artificial Intelligence (AI) can have multiple business advantages for the frontline team and on the overall bottom line. But despite companies’ best intentions and AI’s clear potential, many companies struggle to fully utilize AI. This is leading them to reevaluate their strategy on how to leverage AI for their business, according to our new report

The usage of AI  at the enterprise level is continuing to grow, but the  projects are often loosely defined and can take longer than anticipated to show returns. This has the potential to limit the progress that AI can provide. To further understand the hurdles standing in the way and opportunities AI can bring to manufacturing, we surveyed 250 manufacturing professionals who have visibility into their company’s AI strategy.

Continuing The March Toward AI Implementation

Companies have good intentions when they begin the AI implementation process, but the complexity of AI can pose problems and cause companies to reevaluate their strategy. 

  • 61% said their company has good intentions but needs to reevaluate the way it implements AI projects
  • Only 17% of respondents said their company was in full implementation stage of their AI projects
  • 34% said their company has struggled to keep its AI project(s) in scope because there was a lack of expert guidance at the planning phase of the project


What is Really Holding Companies Back

Having a mature data collection and storage system is essential for AI implementation projects. Without the ability to collect and store  data in a timely manner, manufacturing companies can’t get far with AI implementation. Manufacturers are realizing it takes more time than anticipated to get their data systems up and running. 72% of manufacturing companies said it took more time than anticipated for their company to implement the technical/data collection infrastructure needed to take advantage of the benefits of AI.

Internal buy-in is important for any company project, but it’s especially important for something as complex as AI. Just like marketing, business development, or any other business function, commitment from key stakeholders is essential for success.

Internal buy-in from employees needs to be taken into account when implementing AI projects. 62% said their company took more time than anticipated to acquire internal buy-in and commitment in implementing AI. This lack of internal buy-in has also caused 34% of employees to say that there is a lack of engagement toward these projects.

Important Factors For Successful AI Projects

We found that issues with internal buy-in, decisions regarding who should use the data and lack of budget consensus related to AI projects can slow down implementation in the manufacturing industry.

It’s important to have a mature data collection and analysis infrastructure and to agree on specific business outcomes before implementing and incorporating AI into an industrial workflow. Multiple reasons are given when companies make the decision to begin implementing AI. 54% said that cost savings were the top business problem that they were trying to solve, followed by 49% that said automating tasks was the top reason.

These kinds of projects can lose focus within the company and encounter multiple problems. Less than half (47%) said their company has kept AI projects in scope and focused on deliverables. Moving to a focused approach that can manage the complex process of AI can help to eliminate these issues and help companies stay focused on the long-term goal.

The use of AI technology in the manufacturing industry has the potential and opportunity for companies to empower the frontline team with automated performance monitoring for any industrial workflow. AI can help businesses drive ROI by reducing resource consumption, operating costs, and reliably predict the current state of their business predictions, whenever they need it. 

How do your company’s experiences with AI match up to the respondents? Read more about the report and the methodology here. 


A Day in the Life: Where an Industrial Operator’s Time Goes

Steve is a manager at an industrial beverage plant that produces bottled soft drinks. Accessing, analyzing, and sharing data about the daily performance is an integral part of his job and one that can often be tedious and time-consuming. 

Resources like energy, chemicals, and water all play a role in the quality of the end product the plant produces as well as the profit margins Steve and his team can achieve. Manual and legacy data management processes can eat up a serious portion of an operator’s day. 

The following illustrates the challenges that workers like Steve experience throughout the day in an attempt to manage and make sense of their data. 

Monday: 9 a.m.

Steve gets to his desk and opens an email from his colleague about the performance of a new piece of equipment the plant installed last week. He doesn’t quite remember what the data in the spreadsheet is measuring, but it doesn’t look good. He searches back through last week’s emails to jog his memory.

He clicks download on the Excel spreadsheet attachment in his colleague’s email, only to get a pop-up window that says he needs to update his Microsoft Office Suite in order to open the document. He asks himself, “Where is that activation code, again?”

He opens the spreadsheet and has to correct some of the data formulas that didn’t import the right way, he starts reading through the 13 tabs in the document. The numbers don’t look right for some reason. He swears it was performing perfectly when he read the initial read-outs from his technician last Friday. Steve rifles through the thick portfolio on his desk for the printout the technician gave him last week. He can’t find it. “I’ll have to give that tech a call,” he says.

Steve then gets a voicemail message saying that particular technician is out sick today. The report will have to wait. 

1 p.m.

After back-to-back meetings, Steve gets called down to the factory floor to inspect a piece of equipment that has automatically shut off due to a malfunction. Production is at a standstill as he and his team try to figure out what went wrong with the machine.

After sifting through dozens of printouts and warning screens on the equipment itself, he and the team discover the machine was overheating. Things get frantic as the plant sits idle, so Steve makes an executive decision to adjust the cooling system on the equipment to a temperature his gut tells him will work (he has over 25 years of experience, so his intuition is spot on, right?)

3:30 p.m.

Steve gets back to his desk and opens the spreadsheet from the morning. He realizes the report from the email was showing the coolant malfunction in the machine he just had to deal with on the factory floor. He has access to all this data, but it’s spread out across so many different sources that he can’t make the appropriate decisions that will lead to meaningful actions. He combs through the spreadsheet to see if the gut-based temperature adjustment he made earlier was the right one.

He’s way off…

Like thousands of other industrial operators, Steve can’t make real-time adjustments to his plant’s processes when his data is locked in legacy and manual systems. He would benefit from a centralized platform that can offer him real-time updates on his plant’s processes and assets, as well as automated recommendations and solutions on how to fix problems when they arise. 

After that nightmare of a day, Steve has to spend the next morning looking for ways to deal with equipment downtime and the issues that spreadsheets and other legacy methods have been causing him.

Increasing their industrial intelligence By installing advanced automated sensors powered by an AI system, Steve and his team can monitor critical assets and conditions around the clock in a clear and simple readout that is always up to date. And when emergencies arise, the right AI system can automatically make adjustments and recommendations before a time-wasting issue halts production.  

Does any of Steve’s day sound familiar to you or your team? Unplanned downtime can cost manufacturers an estimated $50 billion annually. It may be time to reevaluate your relationship with your data.