KPIs of the Future of Machine Learning

McKinsey produced it's 2016 Analytics study, that explores the future of machine learning. Meanwhile, Google Brain, is evolving its deep learning systems.

Power of Data in Retail

U.S. retailer supply chain operations who have adopted data and analytics have seen up to a 19% increase in operating margins during the last five years, 2012-2017.

How do you Compete in a Data-Driven World

The transformative potential of Big Data has evolved into more powerful machine learning that is poised to evolve predictive analytics that changes how data is used in real-time. Read the PDF of the executive summary by MGI, here.

• Volume of data is growing exponentially
• More sophisticated algorithms are being developed
• Computation power and storage are improving
• Deep learning, cognitive computing and predictive analytics are evolving

What Industries Gain the Most?

1. Location-based services. Capturing up to 60% of data and analytics value.
2. US Retail. 60%+ increase in net margins.
3. Manufacturing. Lower product development and operating costs, with up to 30% gross margins increase.
4. US Healthcare. 0.7% annual productivity growth.

Data Richness Matures in Forecasting

During the period of 2017-2020, machine learning comes closer to reaching its potential in forecasting and predictive analytics.

1. Radical personalization
2. Real-time optimization
3. Forecasting and and half a dozen more KPIs.

What Industries Have the Most to Gain?

• Media
• Consumer
• Automotive
• Telecom
• Manufacturing
• Energy
• Finance
• Healthcare, etc...

KPIs by Industry

Machine learning's revolution of industries is just beginning in 2017, over the next three years it's expected to result in, for instance:

• Radical personalization in Finance
• Real-time optimization in Energy
• Forecasting in Telecom
• Process of Unstructured Data in Healthcare
• Real-time optimization in Transportation and Logistics
• Radical personalization in Healthcare, among many others.

The Hyper Connected Future

40% of all the potential value associated with the Internet of Things requires interoperability between IoT systems.

Read the full report. #FutureRunDown

As Big Data doubles in size every two years, the most actionable machine learning benefits in the short run are autonomous vehicles and personalized advertising. With searching changing from web to mobile, and now to voice, Google & Facebook themselves stand to be disrupted as the "jackpot lottery" of winning the search and mobile advertising eras respectively will one day be over.

But what will replace it?

The Rise of Executive AI

Predictive analytics can shorten the sales process by up to 30 percent and increase conversion rates by up to 10 percent, according to a recent  McKinsey study

Forget consultant firms, and venture capital startup-advisers, soon machine learning will power better decision making and be implicated in leadership itself.

Indeed Executive AI (eAI), will also be able to automate sales and marketing to a degree. The digital revolution is nothing compared to the AI revolution that is coming.

Machine learning is going to slowly become more implicated in 2017 in the nitty gritty of our professional existence:

• Leadership & management: business intelligence and forecasting
• Human resources: identifying the right people for the right teams
• Voice-first and VR executive systems: uniting collaboration hubs and making metadata even more accessible

Leaving humans to do strategy, innovation and non-routine work to move their business forwards.

Data, Analytics & Prediction - The Holy Trinity of Machine Learning (DAP)

As DAP changes the basis of competition of corporate entities moving forwards, leading companies will utilize machine-learning not only in their core operations, but in how to remain agile, innovate and launch new business models, pivot and leverage value.

• Winner-takes all dynamic continues
• Data as the critical corporate asset
• IoT proliferates how DAP scales with machine-learning
• DAP underpins several disruptive models that will manifest themselves
• Hyperscale platforms to match buyers and sellers in real-time will continue to mature
• DAP translates into faster evidence-based decision making in organizations, institutions and how the future works.

Why is Machine Learning Itself Disruptive?

45 percent of work activities could potentially be automated by currently demonstrated technologies; machine learning can be an enabling technology for the automation of 80 percent of those activities

Machine learning systems can be used to provide any type of repetitive work at levels above human performance.

1. Customer service
2. Logistics management
3. Analysis of medical records
4. Writing news stories, just to name a few.

The disruptive models of DAP emerge in a self-evolving data-ecosystem. We are at the infancy of machine-learning, over the course of the new few decades what machine-learning can do, and the data and computational power at its disposal will increase exponentially, as will its impact on us, the Planet and disruption itself.

MGI (McKinsey Global Institute) acknowledges while progress is accelerating in 2017, we haven't even captured close to the full data of their predictions on Big Data made back in 2011.

• Data scientists are devising more sophisticated algorithms
• Data-driven business models are becoming the norm
• DAP natives are post digital natives that shift the basis of competition
• Legacy organizations that don't embrace the new mindset of DAP, are not longer simply at risk, they are near failure.

The Automation Economy is Real

As we enter into a world of self-driving cars, personalized medicine, intelligent robots and so many other trends I've mentioned before, we begin to realize how the data-driven world changes our cities and our lives permanently.

We have to envision, prepare and discuss about new kinds of jobs and business models that will exist thanks to machine learning, and not simply wait for companies and our organizations to capture more of the value of DAP.

We have to all become "amateur futurists" (like I'm trying to be) and understand the major social trends that we are on the cusp of and learn to think of humanity as one collective being. With truck drivers one of the most common jobs in every state in the U.S., things are about to change with self-autonomous vehicles.

As Cole Feldman says, The "end of work" is our opportunity to be human. We have to create new jobs to fill the new gaps that will form in society. Working with machine learning, analytics, Big Data and AI, to build a better world together.

The future is closer now than ever, will you join it or resist change?

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How has machine learning and algorithms changed your life in 2016? What's your relationship to AI going forwards?