A 3 Phase Plan for Sustainable Digital Transformation

“The hardest part is starting. Once you get that out of the way, you’ll find the rest of the journey much easier.”

Simon Sinek

According to a McKinsey Global Survey, more than 60 percent of respondents with stalled digital transformations attribute the problem to factors that organizations can control. This notion goes against widespread assumptions that external pressures, such as market disruptions or regulatory changes, pose the most significant threats to digital initiatives. More commonly, sources of derailed progress included lack of clarity or alignment on a company’s digital strategy and poor quality of the digital strategy to begin with.

Cultivating A “Marathon, Not A Sprint” Mentality

It’s incumbent upon the digital transformation leaders to manage expectations and take the lead in defining realistic, data-driven ambitions for the enterprise. It’s also critical for all stakeholders to agree upon the necessary governance changes to achieve the new objectives.

Re-envisioning your digital transformation project requires crafting a bold, inspiring mission statement – one that is clear, concise, and consistent with established strategic pillars. This will help everyone focus their efforts on building a program that delivers immense value. Pick a leader who can cast a compelling vision, which acknowledges the past but establishes a new and exciting future.

It’s important to understand and accept that your other project and business leaders will shape the basic perceptions associated with your subsequent plans and actions.Therefore, proper preparation, assessment, planning, acting, measuring, and above all, communication can greatly enhance your chances of success.

Implementing a Proven Methodology and Framework

Following a proven approach to reinvigorate your data roadmap will likely solve many of the problems faced initially. There are a number of different frameworks that can help design your approach. For example, the Prosci change management framework is one approach to following a structured process for implementing lasting change within an organization. It’s initial strategic framework includes three phases: 

  • Phase 1 – Prepare Approach
  • Phase 2 – Manage Change
  • Phase 3 – Sustain Outcomes

Each phase is broken down into three stages, and each stage includes important activities to support the success of a change. Similar to other change management methodologies, the Prosci approach is structured, yet also adaptable and scalable to fit the needs of any organization or change initiative. For more information visit the Prosci 3-Phase Process full outline.

Source: © Prosci, Inc. – Managing Change: Take Action and Implement Plans Worksheet

Conclusion

Again, the Prosci methodology is just one of many available to successfully manage change to support digital transformation. See this list of 10 other models for a few examples. Regardless of the exact framework, what they all have in common is taking a methodical approach to ensure your digital transformation efforts are lasting – meaning you’ve identified the right goals and that you have stakeholders and an organization that’s fully invested in the change.

Don’t let fear of failure get in the way of trying to move your digital transformation efforts forward. With structure, clarity, and concrete objectives, leaders can forge a new path and create a new momentum that allows for a data-driven culture to emerge and thrive.

5 Digital Transformation Lessons from Dune

Last week, sci-fi fans finally got to see the latest film adaptation of Dune. When published in 1965, Frank Herbert’s novel was a groundbreaking, eco-conscious sci-fi epic. Set 20,000 years in the future with intergalactic dynasties and secret orders battling for control of the scarcest resource in the universe, Dune seems both completely alien but also very familiar.

Much has been written about Herbert’s inspiration for dune. But while the author had plenty of history and his own time to draw from, the story is even more relevant today, given how dire some of the same issues have become. So if Dune does such a great job of reflecting our current situation, what insights can it offer into how to address our challenges?

Here are 5 lessons from Dune on digital transformation

1. Bring back the thinking machines

In the Dune universe, a war against machines results in a prohibition against AI or “machines in the likeness of a human mind.” Subsequently, over thousands of years, humanity has filled the role of advanced computers with Mentats. After undergoing conditioning at specialized schools, these ‘human computers’ are able to process large amounts of data, identify patterns, apply logic, and then deduce probable future outcomes. The prescience and strategic abilities of Mentats make them valued advisors, with the great houses of the universe vying for their service.

Atreides Mentat Thufir Hawat

Sound familiar? With organizations across all industries racing to capitalize on AI, there’s been growing demand for data science related roles. Companies have to compete with big tech companies for talent, and there is simply not enough supply to meet the demand.

The solution? Automation. “Many machines on Ix. New machines,” notes a guild navigator (another class of humans that replace the work formerly handled by computers). Organizations can automate much of their data science work by partnering with vendors that have already made significant investments in R&D and data science talent. Leveraging outside expertise to focus on improving specific workflows is more cost-effective, provides flexibility, and can accelerate digital transformation efforts. 

It’s time to bring back the thinking machines (spoiler alert: the humans and AI eventually make peace in the Dune series).

2. Every drop counts

In contrast to the Harkonnen who seem to indulge in daily steam showers, the Fremen natives of Dune are relentless in their conservation of water. Donning water-preserving suits, the Fremen even reclaim water from corpses and avoid crying. Of course, personal survival demands it, but their hyper-vigilant water preservation also serves their long term vision – terraforming their desert planet into a green oasis. The Fremen use wind traps to collect moisture from the air and slowly amass giant caches of water across thousands of sites.

Fremen water catch basin

Organizations rightly prioritize opportunities that promise to have the biggest impact. But they also shouldn’t overlook less obvious opportunities to innovate (for instance, optimizing the various points at which water is used within food manufacturing processes). By applying the same rigor across other processes, the many small gains in aggregate can have an enormous impact on the efficiency and sustainability of the entire business.

3. “The slow blade penetrates the shield”

Combat in Dune highlights the value of adaptation and an incremental approach. With personal shielding technology having rendered conventional projectile weapons largely ineffective, military forces in Dune revive the use of hand-to-hand combat and traditional weapons. To win in battle, soldiers have to think steps ahead and employ techniques that allow them to overcome the shields, which only yield to slow attacks.

Likewise, with the conventional, top-down approach to digital transformation often failing to deliver, organizations must adapt more effective strategies. A survey of industrial professionals indicated that while 94% have taken an organization-wide approach to digital transformation, only 29% claimed success. Stymied by unanticipated complexity and plagued with delays and cost overruns, many organizations are turning to an operation-specific approach to digital transformation. By implementing digitization and automation techniques to specific workflows first, organizations are able to ensure incremental success and then scale their efforts to the rest of the org.

4. Enlist the frontline

Another benefit of the ops-specific approach is that it more effectively involves and considers those closest to the processes being targeted. In Dune, as the management of Arrakis and spice mining changes hands from the Harkonnen to the Atreides, there’s a clear distinction in the management style of the Atreides. The Harkonnen impose their rule and maximize spice production with violent oppression. By contrast, the Atreides begin their management by sending envoys to engage the locals. They rescue spice harvester workers at the expense of spice production, and then Paul embeds himself with the Fremen and gains their desert knowledge. The approach pays off, as Paul is able to mobilize the locals to overwhelming success.

Similarly, it behooves organizations looking to transform their operations to enlist stakeholders at all levels, especially those that can assess the situation on the ground and identify all opportunities to innovate. Getting their buy-in, tapping their experience and expertise, and ensuring the project delivers on their goals will increase chances for success.

Dune spice miner

5. Fear is the mindkiller

“Moods are a thing for cattle and love play” declares 1984 Dune’s Gurney Halleck while chiding Paul Atreides for not being more vigilant in preparing for their hostile destination. Once on Arrakis, Paul finds himself stripped of his resources and stranded in the desert. He’s forced to quickly hone his skills and adapt to the conditions of his new environment. 

The pace of innovation across all industries is increasing. To maintain their competitive advantage, organizations must create an environment to support innovation within. They can’t afford to wait for years long, enterprise-wide digital transformation projects to deliver uncertain results. Budgetary limits, legacy systems, lack of expertise, and other challenges can be overcome with the right approach. The op-specific approach can help organizations adapt faster, empower professionals across the organization, and realize ROI sooner. 

The sleeper must awaken!

Zillow & 2 Attributes of A Successful Data Culture

In our recent e-book, 3 Hacks for Onboarding AI Platforms, we outline a few key steps to building the right team and culture to support an AI deployment. And we did so for good reason. There is broad consensus that the success of digital transformation efforts hinge on having a data-driven culture behind it. A 2019 Deloitte study found that companies with strong data-driven cultures were twice as likely to exceed business goals. Another study by New Vantage Partners found that 95% of the challenge to adoption of big data and AI initiatives was cultural, organizational, or process-driven rather than technological.

Given this, organizations have prioritized fostering data-driven cultures within their organizations. Whether it’s hiring a digital-focused executive, establishing centers of excellence, or instituting organization-wide mandates, the focus is on moving away from decisions based on gut feeling to those based on data-derived facts. 

Organizations Must Look Beyond the Numbers

Sounds great, but an effective data driven organization must often look beyond the numbers and can face major consequences when they fail to do so. Take for example Zillow, a company that has used data to not only build more accurate real estate models but has also leveraged data into a powerful competitive advantage.

Zillow’s automated home-buying business recently made headlines for its decision to halt home purchases. The company, which has access to more than 17 years worth of data, is hearing backlash after the announcement. Some are calling into question the company’s ability to properly plan and take into account logistical constraints. Others are wondering if their brand has been irreparably damaged. How could these things happen in a data-centric company?

Attributes of a Data-Informed Culture: Intuition & Ownership

In our experience, organizations have proven tremendously successful when they connect big data analytics to the business strategy. This data-informed approach means they acknowledge the data-derived insights but are also aware of and account for the implications of other non-data factors that may impact the direction of the overall strategy.

It also means that when building this data-informed culture, in addition to data literacy, organizations must also look for and encourage two key attributes: 1) Intuition and 2) Ownership

Intuition is defined as the natural ability to know something without any proof or evidence.But it’s also another data point, based on unconscious knowledge, expertise, and experience to be combined with other data in decision making. Ownership is the state of being responsible and accountable. It’s critical that these two components are embedded into the company’s values so that data may be used in a way that properly guides and informs decisions. Otherwise, you may be sitting on actionable insights that no one has evaluated properly or acted on because it’s “not my place.” Someone must answer to the choices being made and how those decisions align to and support broader goals.

It’s easy to wonder if the culture at Zillow didn’t empower the decision makers to use their intuition in the process, but instead they had been accustomed to letting the data be their one and only guide. 

It also highlights a gap between the company’s actions and the real-world issues having to do with the on-the-ground workers and supply constraints. This could be the result of a lack of ownership over the decisions being made.

Being data-informed in addition to data-driven means using both intuition and ownership to constantly check your assumptions, methods and outcomes. The qualitative complements the quantitative, just as the human element complements the data analysis. 

If you want to take your data insights to the next level and avoid the unintended consequences associated with mismanaging the intangible side of your business, look for people that demonstrate high intuition and ownership traits. Your culture will thank you for it.

AI Automation in Manufacturing: 5 Key Business Functions Being Trusted to AI

Business Insider predicts that by next year, manufacturers will spend approximately $267 billion on IoT technology. One of AI’s key roles in that massive investment is its ability to automate and manage the millions of micro-decisions that manufacturers have to make. This enables them to deliver high-quality goods that customers have come to expect. While AI in manufacturing is not a catch-all for every business challenge, there are many specific business functions manufacturers can easily automate for greater cost-efficiency, safety, and accuracy across the manufacturing process.

Here are five examples that show how AI-based decision support systems are automating tasks and helping alleviate the labor shortage in the manufacturing industry:

1. Equipment Maintenance

For factories and industrial operations that are expected to run 24/7, equipment downtime can be a major operational challenge. Whether it’s planned maintenance on a membrane in an industrial beverage plant or an emergency repair on a pipe in an oil well, operators need to be able to manage and react to equipment issues as fast as possible.

IoT sensors can monitor factors that affect equipment conditions across industries such as oil temperature, salinity levels, and vibration levels. IoT sensors can give operators critical insights into wear-and-tear as well as emergency issues, allowing them to shut down equipment to prevent catastrophic failure or take other appropriate actions. The speed of response is an important factor in minimizing the potential losses: unplanned downtime costs manufacturers an estimated $50 billion annually.

2. Quality Control

In an increasingly competitive market, manufacturers cannot afford to waste resources on subpar products. AI algorithms can proactively identify mistakes and abnormalities that can occur at any time along the production process.

There are business tasks that human workers will always be better suited for, but machines can be more appropriate to perform quality control tasks than manual inspectors are.

According to recent Mckinsey statistics, deep-learning-based systems can provide defect detection improvements up to 90% compared to a human inspector.

3. Supply Chain Optimization

AI’s contributions to the manufacturing sector don’t stop at the production line. Algorithms can help companies improve how they deliver their products to their consumers via predictive analytics. Better informed firms are able to shift from a reactionary model to a more profitable and predictive one.

IoT sensors can collect a myriad of data along the industrial supply chain, from transportation and energy consumption to raw material cost fluctuations to weather patterns and other market conditions that can have an impact on a company’s bottom line.

4. Time-Consuming Parts of the Design Process

While automation’s benefits can be clearly observed on the factory floor, AI also is helping streamline and optimize the design process in manufacturing. For small and incremental improvements to a product’s design, AI algorithms are able to explore millions of different tweaks and adjustments to a design to optimize its performance. Factors such as material usage and efficiency, structural strength, and weight can all be assessed and improved upon with AI algorithms.

5. Dangerous Manual Tasks

Safety is an inherent problem when workers and traditional machines are sharing the same space. While rare, accidents are always a possibility when workers are operating near powerful machines with no cognitive awareness.

Cobots are AI-powered collaborative machines designed to safely work alongside skilled laborers in industrial environments. Cobots can assume physical tasks such as heavy lifting and repetitive tasks that require a degree of fine-motor control. At an estimated cost of about $24,000 each, the machines can prove to be a useful and affordable supplement to skilled laborers on a factory floor.

While there is enormous potential in automation for the manufacturing industry, it’s not always an easy task to implement an AI project. Be on the lookout for our upcoming survey report that takes a closer look at the challenges faced by manufacturers and other business leaders when implementing AI initiatives in their businesses.

Follow us on Twitter and LinkedIn for more company updates or drop us a line through our website.

AI In Manufacturing, 2020 And Beyond

As we continue to dive deeper into Industry 4.0, the state of manufacturing is largely the same as it was several years ago. Recently, I came across an article on Forbes titled: Top 5 Digital Transformation Trends In Manufacturing For 2020 written by Daniel Newman. In this piece, he talked about a significant difference in the perception vs. reality regarding the state of manufacturing.

The manufacturing sector still has the same goals but the approaches within the industry have changed. The number of firms using AI and IoT technologies in their processes continues to grow rapidly. Company goals are shifting in response to a new type of consumer that expects: (1) higher quality services and goods that can be produced, (2) delivered at a faster clip, and (3) manufactured with responsible practices. This puts pressure on companies to look for different approaches and solutions to fill customer needs. Additionally, companies are realizing that embracing AI and IoT technologies can be a prudent financial decision as well. This is especially true because they can now reduce equipment downtime, cut down on waste, and improve their ability to predict maintenance breaks. It’s estimated that just 60 minutes of downtime can be equal to a $100,000 loss in a manufacturing environment. 

The article brings up many relevant points in the manufacturing sector, but three distinct pieces of information stood out to me. These trends will significantly impact  the industry in 2020 and beyond: 

  1. Manufacturing Is Fully Embracing The Automation Of Routine Tasks

90% of manufacturing companies in the United States have fewer than 500 employees. To some observers, this might seem like a problem in the industry. But what you don’t see on the surface is that the reduction in workforce is a sign that companies leaving behind inefficient old ways. It’s impossible to do everything manually, which opens the door for technology to resolve these issues and become more efficient.

  1. Consumers Are Dictating The Actions of Manufacturers

The speed at which manufacturing moves is driven by a group of consumers who want higher quality goods, combined with responsible manufacturing practices. And this is not just coming from consumers! Corporate leaders are backing up this sentiment as well. Recently, the Business Roundtable issued a statement that companies should not just advance the interest of shareholders but “must also invest in their employees, protect the environment, and deal fairly and ethically with their suppliers.” Combining speed and responsibility requires not just a different philosophy, but intelligent action. Manual processes and limited operating information are being replaced by data intelligence platforms to keep up with this increasing demand. Companies need to be able to take immediate action on their collected data, which was echoed by 76% of manufacturing professional respondents that Plutoshift recently surveyed.

  1. These Innovations In Manufacturing Aren’t New

This massive shift in company practices could lead some to believe that this technology is a new phenomenon, but the number of companies who are using it has just increased. This means that more business leaders are seeing the benefits of taking manual tasks (especially ones that require a lot of labor hours) and automating these responsibilities.

It isn’t a secret that manufacturing is evolving at a rapid pace. Increased access to data combined with the widespread use of AI is eliminating the barriers that once held companies back from using IoT within their businesses.

With these barriers no longer an issue, companies are fully embracing these innovations and taking advantage of technologies that are already available. This shift provides a decrease in overall costs and an increase in efficiency, providing benefits to customers and companies at the same time.

Follow us on Twitter and LinkedIn for more company updates, and be on the lookout for our upcoming survey report about AI adoption in the enterprise world.

 

3 Ways AI Is Helping Manage Climate Change

Artificial Intelligence is an amazing tool available to researchers, industries, and scientists to help solve some of the world’s most complex challenges. While AI is not a panacea to the world’s most pressing issues, it is proving to be a critical tool in alleviating the strain on natural resources.

As AI applications become more sophisticated and widely available, people across industries and cultures are finding new ways to solve climate-related issues with smarter data usage. Below is a sample of some of the most promising and interesting AI applications in the field of climate change mitigation:  

  1. More Responsible Water Usage

Almost one-fifth of the world’s population, or 1.2 billion people, live in areas of physical water scarcity. Agriculture, industrial manufacturing, energy production, and mining command the lion’s share of water usage. Each one of these categories has significant room for improvement in how they use and treat the freshwater they consume every day. 

In the not-so-distant future, precipitation patterns will be less reliable and the demand on historical freshwater sources like aquifers will outpace natural replenishment rates. In such a situation, getting every ounce of value out of water resources will be critical to overall sustainability. Our company’s AI framework enables operators to automatically monitor critical water processes and have access to actionable information. One water treatment plant was able to reduce its overall water and energy usage/costs by 18% after implementing our AI solution. The U.S. manufacturing sector uses about 15.9 billion gallons of water per day, so every drop counts! 

  1. Better Climate/Weather Predictions

Agricultural activities worldwide rely heavily upon predictable rainfall and temperature patterns. This helps the farmers understand when to plant, harvest, and irrigate their crops for maximum efficiency. With historical climate patterns in flux, farmers will need better modeling technology to adapt to a less predictable climate. 

Microsoft’s AI for Earth initiative is combining cloud technology with AI-powered sensors to collect soil, tillage, and yield data for specific farms around the world. They are making this data available to other farmers via cloud-based applications. 

As other meteorology-focused technologies like sensors, satellites, and computer models continue to advance, AI will serve as a unifying service that will help make sense of the seemingly infinite data points they collect. 

  1. Better Emissions Tracking 

Every improvement AI brings to industrial processes will help us cut waste and conserve resources. But it’s the carbon emissions that ultimately need to be addressed to best mitigate the effects of climate change. A recent article in National Geographic outlines how Google is partnering with environmentally driven organizations to help better monitor carbon emissions from power plants with AI. 

“AI can automate the analysis of images of power plants to get regular updates on emissions. It also introduces new ways to measure a plant’s impact, by crunching numbers of nearby infrastructure and electricity use. That’s handy for gas-powered plants that don’t have the easy-to-measure plumes that coal-powered plants have,” the article explained. 

By better mapping and processing emissions data, governments and companies can better understand where problems are originating and how they can best address these problems going forward. 

AI For Business and Climate 

Just like digital transformation, targeted AI initiatives can improve top-line revenue as well as manage climate risks. The key aspect to note here is that AI initiatives require behavioral change across all lines of business. Addressing and managing climate change requires the same step-by-step commitment from companies that goes into any other business initiative — it’s the small steps that can add up to big returns. 

Follow us on Twitter and LinkedIn for more company updates, and be on the lookout for our upcoming survey report about AI adoption in the enterprise world. 

Plutoshift’s $8M Series A Funding — Fueling the growth of AI

Earlier in September, we had the pleasure of announcing an enormous milestone for Plutoshift in the form of our $8 million Series A funding round. I’d like to take a moment to express our gratitude to all of our investors, customers, team members, and partners that have played a role in helping us bring AI to the industrial world. 

Here are just a few of the investors who have committed their support to our mission to help change the industrial world’s relationship with its most critical and underutilized asset — DATA.

  • Fall Line Capital
  • Unshackled Ventures
  • Dave Gilboa, co-founder of Warby Parker
  • Joey Zwillinger, co-founder of Allbirds
  • Nat Turner, co-founder of Flatiron Health

We’re already putting this funding to good use by hiring new members to our technology team, including several new full-time data scientists at our Palo Alto HQ and other new team members in our Denver office. 

What is Business Water? 

We are using this as an opportunity to initially focus our platform on manufacturers with a critical reliance on water in their processes. We have termed it “Business Water”.

Business Water is the use of critical resources to manage water in industrial operations, and it’s quickly becoming one of the biggest challenges in the manufacturing industry. By applying the power of AI, we are doing our part to help alleviate the impending water crisis facing industries worldwide. 

By unlocking insights from existing data sources and predicting what’s coming up next, the Plutoshift platform provides a holistic view of key performance metrics and resources that impact the bottom line of industrial businesses.

From food and beverage manufacturers to energy and chemical producers, the potential opportunities for cost savings through AI are limitless. It extends to just about any company that relies on industrial processes to produce its final output.

While we’re taking a moment to celebrate this achievement in our company’s history, we’re getting right back to work to help companies realize the value in their data. Schedule a time to view our demo, and we’d be happy to show you the Plutoshift advantage in action. 

Mastering AI Investments, One Drop At A Time

Eighty percent of businesses are investing in some form of AI today. With such an overwhelming number of firms seeing the potential in AI solutions in addition to Industrial IoT technology, one would imagine that a large percentage of CIOs and  managers would have a solid grasp of what it takes to both measure and communicate the ROI of their initiatives to their boards.

But as recent research has demonstrated, that’s not the case for the majority of business leaders. Many of them are still unclear on the tools, time commitments, and expertise required to successfully implement AI initiatives and reap the benefits of their investments.  

At Plutoshift, we’re tackling this deficit through AI solutions that are straightforward and hyper-focused on specific, practical, and data-intensive tasks. This framework helps operators have access to actionable insights about their processes from the data they already collect. 

Over the past few months, I have been sharing this vision behind Plutoshift and my outlook on AI with a number of customers and partners across manufacturing industries. Below is a synopsis of that vision that may serve to ease some of the anxieties around AI initiatives; a CEO’s playbook of sorts. 

 Data → Insight → Alerting → Action

For AI to have a material impact on business, solutions must begin with data and lead to actionable response to a problem or opportunity. My background in this area is in leveraging Deep Learning frameworks to understand the data that’s generated in the physical world, but I recognize that the AI models must serve up the action and not just the intel for the user to interpret.  

The measurements collected by a data collection system are only as valuable as the recommendations it can display in the platform for users to see.  

Drive specific outcomes with AI 

Water is my passion, and AI is the engine we have designed at Plutoshift to ensure that AI actually works in the real world. There are many companies in this space that are pursuing an everything-and-anything model for AI. We don’t search for pie in the sky problems to solve, we go for practicality and that starts with real use cases tied to concrete ROI. 

In the case of Plutoshift, AI manifests itself in the form of Process Performance Monitoring. This is a specific application of AI that involves providing access to performance metrics related to resource consumption for various processes. It’s a unified view that shows the current reality as well as future outcomes to the operators. To make it even more practical, the platform is currently oriented towards manufacturers with a critical reliance on water in their processes.

Built for scale

In my view of the world, data intelligence solutions must be scalable on several fronts: 

  1. Aggregation of data across multiple sources to have  a single unified source of truth
  2. Ability to drive ROI for business units
  3. Consistent management of information across the team 

While we believe that the ROI for an initial pilot must be attractive, the real payoff is recognized when the proven solution is executed across the organization and supported by the wrap-around customer experience that we have developed. 

Don’t Try To Boil The Ocean 

AI solutions have the potential to save a lot of time and money in the manufacturing industry, but only if businesses are able to execute in an efficient way that doesn’t drag down the entire organization. 

In my experience, AI has the ability to make a very positive impact if it’s deployed in a practical way. If you’re interested in learning more about driving business outcomes with AI, take a moment to schedule a demo of our solution here.  

3 Ways A.I. Is Reducing Water Strain In The Permian Basin

By: Prateek Joshi, Founder of Plutoshift and Mike Hormell, Plutoshift Strategic Advisor and Oil & Gas Executive.

After a long history of relying on foreign oil suppliers, technological innovations and substantial investment in the area of unconventional oil and gas extraction have left the US as the top oil-producing nation in the world. Thanks to innovations over the last few decades like microseismic solutions, horizontal drilling and hydraulic fracturing, US producers have been able to utilize oil reserves that were historically too difficult to tap.

Looking forward, investments in artificial intelligence, cloud computing and IoT technology promise to drive efficiency and production even further in the coming years.

But what does this progress mean for water usage and conservation in a booming but arid location like the Permian Basin? By producing upwards of 4 million barrels of crude per day with dozens of companies operating more than 460 drilling rigs, stress on the region’s water is only going to grow as the boom continues.

At Plutoshift, we believe that water is worth its weight in gold, so we provide operators with the advanced insights into their water data they need to stay competitive and sustainable. Here are three ways A.I. solutions are saving water in the arid, but vital Permian Basin:

A.I. Is Reducing Water-Related Maintenance Costs

Due to the sheer volume of production and the water-intensive nature of hydraulic fracturing (195 million gallons of water is used per day in west Texas and southeastern New Mexico), injection well operators need to maximize how they use water on-site in order to meet daily disposal requirements and other contracted disposal volume commitments. By leveraging IoT technology at their wells, operators are able to improve their abilities to predict costly maintenance issues before they become big problems. Problems like pump failures and unexpected pressure issues can often result in downtime and reduced well utilization, impacting its bottom line.

A.I. can help cut costs by making the millions of micro-decisions and measurements required to keep operators one step ahead of equipment malfunctions. Plutoshift offers oil and gas operators solutions that extend from customized views on operational intelligence to fully automated alerts based on producer risk parameters.

A.I. Is Making Water Reuse Programs More Efficient 

In the dry and often remote Permian Basin, wastewater disposal costs can account for a third of total expenses for producers, making it vital they get every ounce of benefit from their supply. And with demand on wastewater disposal resources expected to double in the next two to three years, finding new and innovative ways to improve upon water reuse processes is an imperative task for A.I. applications.

A.I. sensors at the well site are able to ingest large quantities of unstructured data collected from frac water, and by utilizing historical data, make constant recommendations to more cost effectively achieve water quality criteria and meet reuse requirements. To unlock the true value of their water resources, operators can’t rely on data after the fact. In our own recent research, 76% of respondents from oil and gas and other industries said they need solutions that analyze data in real-time.

A.I. Is Helping Solve Oil And Gas’ Labor Crisis 

While the oil and gas industry has historically not been as quick to adopt new technologies as others, in part due to complications of operating in the field as opposed to in the plant, leading companies are now embracing digital transformation initiatives at high rates. A.I. is a huge sector of investment for firms that are in the process of recruiting new workers that bring different skills to the table. The estimated value of A.I. is expected to grow from $1.57 billion in 2017 to $2.85 billion by 2022, with North America serving as the dominant market.

Accenture Strategy released a report outlining the looming talent shortage in the industry, predicting that in the “… exploration and production side of the industry alone, they could face a shortage of as many as 40,000 engineers, geologists and other technical professionals by 2025.”

As A.I. and other technological initiatives draw in new generations of workers, water conservation efforts and profits have the potential to improve significantly.

Interested in learning more about how PlutoShift’s A.I. can help improve your oil and gas operations? Contact our team today, we’d be happy to show you more. Also, be sure to stay connected by following us on LinkedIn and Twitter.

3 Ways Big Data and A.I. Will Positively Impact Agriculture

The Agricultural Revolution was one of the most defining moments in human history, providing opportunities for billions of people to live richer, healthier and more fulfilling lives. We owe almost everything we have today to a few innovative pioneers from tens of thousands of years ago who recognized the value in farming. 

But we shouldn’t view the agricultural revolution as a singular, stagnant event. With global food demand expected to rise by 59–98% between 2005 and 2050, and the realities of a harsher, less predictable climate becoming more apparent every day, we owe it to our ourselves as well as our Neolithic ancestors to continue to find ways to innovate the way we create and use resources to keep the world fed. 

At Plutoshift, we are particularly interested in the vital role artificial intelligence and big data will play in keeping farmers efficient, sustainable and profitable.

Here are three ways modern-day pioneers are unlocking their data and leveraging A.I. to deliver meaningful outcomes.

A.I. will provide actionable insights faster than ever

One of the biggest challenges modern farmers face is getting the biggest yield from their crops in a sustainable way in the face of  an unpredictable environment. Farmers in Africa, for example, lose an estimated 49% of their expected total crop yield per year to factors like pests, disease and water measurement issues. 

AI-powered applications can both measure and act on factors that affect yields, such as soil acidity, moisture levels, fertilizer application, and a farm’s risk of pest infestation. Farmers in Texas have used the power of AI to compare historical satellite images of farmland to accurately predict when a swarm of potentially devastating grasshoppers was making its way to crops, providing warning much earlier than ever possible before. 

A.I. and smart data use can aid sustainability 

Research shows that agriculture is responsible for up to a quarter of all human-caused greenhouse gas emissions. Additionally, irrigation for agriculture is responsible for 70-90% of the world’s freshwater usage. When combined with land and fertilizer usage, these factors take a significant toll on the world’s resources and environment. 

How then can we use technology to mitigate the cost to the environment, while still feeding a hungry world? 

In the case of irrigation, advances in A.I. applications are helping both large and small-scale farms use water more responsibly and efficiently. One organization, DHI GRAS, developed a solution to help farmers reduce water consumption in their fields via A.I. algorithms that use thermal and optical satellite data, combined with continually updated meteorological data.

With data-driven insights provided, farmers can use exactly the right amount of water for maximum yield while limiting waste. 

A.I. can refresh a struggling labor market

While agriculture was once the most popular industry worldwide, employment numbers have dwindled during the past few centuries. In fact, in 1870, almost 50 percent of the U.S. population was employed in agriculture. As of 2008, that number has dwindled to less than 2 percent. Thanks to automation and other sources of innovation that have increased yields per acre, it simply takes fewer workers to successfully run a farm than in years past. 

Data-driven farming is set to provide massive economic opportunities. It has been estimated that smarter farming practices could generate $2.3 trillion overall worldwide annually, with $250 billion coming from AI and data analytics alone.

With a significantly increased demand for food and richer dietary expectations awaiting the industry in the next few decades, data-driven solutions (and the skills to provide them) will be in high demand.  

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