AI News, Why manufacturers need to invest in digital transformation

News & Views

Intelligent Applications have been and continue to be a focus of our investing.  These apps sit on top of the infrastructure a company chooses, the data they collect and curate, the machine learning they apply to the data and the continuous learning system they build.  In this deep dive we talk about why intelligent applications are a central component to our investing themes and where we see the opportunities for company creation and building.

These intelligent apps range from “net-new” apps like those powering autonomous vehicles and automated retail stores to existing apps that are enhanced with intelligence, such as lead scoring in a CRM app or content recommendations in a media app.

Automation using AI is at the cornerstone of what every enterprise is going through in terms of digital transformation.  For example, UiPath’s RPA platform allows companies to drastically reduce costs by automating a wide variety of software based tasks using UiPath’s “robots.” While the UiPath platform is early in its journey to becoming an intelligent app, it is already helping its customer drive 10x process improvements.

While Suplari’s customers may have individual processes to reduce software costs through deduplication or to identify opportunities for savings in contract renewals, using AI to proactively identify the best opportunities allows their customers to realize large efficiencies in their procurement processes.

One of our portfolio companies, Amperity, literally combines different silos of customer data.  Companies that have customer data stored in disparate systems and tools can’t easily leverage this data to get a full picture of their customer base.

Now is an exciting time for investors and entrepreneurs to be focusing on intelligent applications because of the momentum and growth of several important technology trends: As these trends make it easier for entrepreneurs to build intelligent applications, we have been developing our own frameworks to understand how all of these pieces fit together to create value for customers.

precursor to using AI effectively and building intelligent applications is having a “data” strategy.  Having a unique data strategy that could be a combination of public data sets and proprietary data sets enables companies to provide unique and differentiated value.  This is a necessary first step, before you can use the data to train models and build a continuous learning system that is a core part of building an intelligent application.

What we’ve seen at this layer of the stack is that getting data into the right place, in the right format, in order to be used for machine learning continues to be very difficult, and simplifying this process is extremely valuable to customers.

Ultimately, we are looking for companies that can benefit from the virtuous data cycle – where more data creates better user experiences, leading to better user engagement, leading to more data, and ultimately better user experiences again.

Technology and regulatory trends have driven the healthcare field to rapidly digitize many different types of records – from basic medical histories, to insurance claims, to x-rays, MRI scans, and ‘omics’ data (e.g., genomics, proteomics, biomics).

However, we believe that as we move forward, the ability to build new applications and continuously improve systems and processes using machine learning will be a core part of any app, and machine learning will be immensely impactful in every fabric of the society that we work and live in.

Survey: 2019 a critical year for digital transformation

Digital transformation will reach a turning point in 2019, as companies across the globe turn up investments in digital and decide what to do with those investments, according to a recent survey conducted by The Economist Intelligence Unit (EIU).

Of more than 600 senior executives at large global companies who participated in the survey, 83 percent say they expect to increase investment in digital technologies in the next 12 months.

Most respondents directly link digital strategy to profits, as 68 percent say their digital strategy has helped them increase profitability over the past 3 years, and 74 percent expect their digital strategy will improve profits during the next 3 years.

“We were a bit surprised, given how positive the results were, by how little progress companies have made in digitizing different parts of their organizations, but they seem to  want to accomplish a lot in the next few years,” says Gilda Stahl, managing editor of thought leadership at the EIU.

4 Ways Artificial Intelligence Will Drive Digital Transformation In Agriculture

The United Nations reports that about 1/3 of the food produced globally each year is lost or wasted, and I’d reckon that number is not too surprising.

With exploding populations, global warming, and less land available for cultivation, we’re actually facing a global food shortage of epidemic proportions.

With the help of AgTech, connected farmers are beginning to share data, and make improvements in input, efficiencies, and operations processes, largely due to AI-driven sensors.

For instance, high-speed planting equipment can provide “as planted” estimates on crop yield and harvest output, allowing farmers to plan for sales forecasting, overflow and shortage.

Blue River Technologies, for instance, shows that they can reduce the use of herbicides by 90 percent by moving from broadcast spraying to targeted spraying using data pulled from AI sensors.

The AI teams at Monsanto, for instance, found that algorithms could help them more quickly determine which hybrid plants would grow best in real-life planting conditions, saving massive amounts of product development time.

For instance, in the past, Monsanto would evaluate corn hybrids for years in the field before bringing them to market—a process that could take eight years from discovery to commercialization.

Using an algorithm that used the past 15 years of molecular marker and field trial information, they’ve shaved an entire year off the breeding process.

What’s even better: connected farmers globally will theoretically be able to share this type of information, allowing for greater and faster product development not just on Monsanto farms, but around the world.

Google is working to train AI to recognize 5,000 species of plants and animals, which would improve drone ability to detect pest disease and crop damage.

Data: The Fuel Powering AI Digital Transformation

The currency of tomorrow isn’twhat you think:It’s not cold hard cash,precious metals,land or evencryptocurrency– it’sdata.Inthe very near future, every company in the worldwilleitherbuy or selldataas this corporate asset continues to gain value.

AI systems can access and analyzelargedatasetsso if businesses are to take advantage of the explosion ofdataas thefuel powering digital transformation,they’re going to needtoartificial intelligenceand machine learningtohelptransform dataeffectively,so they can deliverexperiencespeoplehave never seen before or imagined.

A few examples include thehyper-personalization ofa retailexperience, location sensorsthathelp companies route shipments for greater efficiencies,more accurate and effective fraud detection,and evenwearabletechnologies that provide detailed information about how workersaremoving, lifting or theirlocation to reduce injuries and increase safety.

Cleaning data–As Imentioned last time, a lot of the time spent by datascientisttoday is cleaning datato make itaccessible, but thisis a role that couldbeturnedover to AI-powered machines.This data cleaning process is critical because analyzing raw data will leave you withinsights that are, quite simply, wrong.

Transforming data to business valueis harder than many companies thought it would be,requiringdeeper resources, more expertise and harder work than expected, but unless you’re planningto buya one-way ticket toadeserted island,investing the time is essential to future survival.

believe thatreachingthe transformational level will require data literacy skills permeating an organization from top to bottom, as well as AI and machine learning to make sense ofourgrowing datasets.And, just like we need upgraded skills for the mechanics of todaythemanage the highly digital cars of today, corporations need to begin buildingdata capabilities to ignite their data fuel and accelerate transformation.

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