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It was almost 10 years ago when brick and mortar retailers woke up and realized that on-line businesses where eating their market share during the make-or-break holiday season.  Online players were able to provide discounts and offers while consumers were in physical stores – diverting and capturing customer time, attention and money. Traditional retailers then needed to play catch up – and have been ever since. They would have been better served to start their digital transformations 2 to 5 years prior.

Today, organizations sit in an opportunity to dive into AI and beat the rush and avoid playing catch up. Since we are on the verge of artificial intelligence (AI) taking off, we need to prepare as only a few leading organizations will be able to harness the impact successfully. Those that do will realize real economic growth after leveraging AI in tools such as, training data, governance tools, consulting and integration services, and most critical, the creation of new sustainable revenue models. Over the next two-to-five years, we can expect a profound transformation for knowledge workers and professionals as their daily tasks are infused, thanks to AI and machine learning (ML). AI will reduce repetitive work for humans, and force (or assist) humans to make decisions faster, easier making the process simple.

Startups, incumbent tech companies, and corporate innovation centers have already started using AI and ML to solve real business problems across nearly every industry, including manufacturing, healthcare, transportation, and energy.

The first successes were no-nonsense: Take a process that you know well and move the heavy lifting to AI. This enables humans to do more creative thinking and less repetitive work. The approach was characterized by the short-term wins, intended to be cross-company scalable with a focus on immediate value creation.

2020: The Future of AI Looks Shiny

Gartner predicts that by 2020, artificial intelligence will create more jobs than it eliminates, noting that the number of jobs impacted by AI will vary by industry. Through 2019, healthcare, education, and the public sector will see continuously growing job demand, while manufacturing will demonstrate the greatest growth. Starting in 2020, AI-related job creation is predicted to cross into positive territory, using AI where it matters, reaching two million net-new jobs by 2025.

Roles such as Citizen Data Scientist, Best Practice Training Data Creator and AI trainer will be needed for a variety of industries and domains, particularly within regulatory, law or finance companies. It will also create executive jobs, such as the chief data officer, AI ethics & governance officer, or AI training-property protection. There will also be data monetization-related jobs, where companies will see both monetization of their AI-enriched data and AI-trained data services to their industry or value chain. Leveraging AI in processes and augmenting tasks will strengthen the economy as a whole, as it will increase the job market.

The pivotal moment of mainstream AI usage will begin in 2020. Many analysts have already projected that by 2020, around 70% of the data that a company uses will come from external data streams and IoT devices. Experts also predict that 50 billion “things” will be connected to the Internet. As billions of things, data, business processes, and people become connected, AI, powered by ML, will need to do the filtering, inference, and prediction to make this all work. The current predictions are that $19 trillion of value will be created.

Data: The Glue vs The Asset

The most important question that companies need to ask relates to their core competence: “Is data the core asset that I monetize?” or “Is data the glue that connects the processes that have made my products or services successful?”

The companies that are purely data-centric, monetize their data for product selection and insight: For these companies, data is their asset. Data-centric organizations will need to create their own business model to get ahead. Like online businesses used data to better understand and predict digital selling 10 years ago, they were ahead of the curve by using data as their asset more than 15 years ago.

Data-centric companies create their own AI capabilities

Preparing the data used to train your own baseline AI models is incredibly time- and labor-intensive. For data-centric companies, the creation of data that trains their own artificial intelligence is what they need in order to sustain their company’s value. These companies create their own capability, their unique AI code, and their own platforms. Companies that have done this successfully are Airbnb, Facebook, and Alibaba—data is their asset.

The original miners of AI-driven insights had to create their own tools and workflows and data infrastructure, but as the community of data-economy prospectors grows, some question if they are better off focusing on accelerating innovation for the primary capabilities of their specific AI-infused processes instead of building their own underlying infrastructure. This forward-looking thought has provided technology companies the opportunity to swoop in and create AI tools, leveraging technology platforms and business solutions – all while alleviating the challenge for data-centric companies to build their own tools.

Most companies will not create AI from scratch. Instead, they will tailor AI off-the-shelf, so they can focus on what they do best and deliver their brand promise. This means using data as the glue, the trigger and connector to create intelligent processes.

Companies use AI to connect processes

What does it mean to make money by creating AI versus using AI? One example of firms that use AI data to connect processes are car companies that can use data to predict when a car will need maintenance, ultimately using IoT-connected vehicles to inform maintenance of a needed repair. In this case, data is being used to create new processes that digitally enable and even transform an organization while AI helps to answer data questions.

Of course, these companies can “create AI” and become prospectors as well. Most companies still spend too much time—and too many of their best resources—building up bespoke tools, ML code, and custom data frameworks. This slows time to market and consumes resources that should be focused on driving innovation and sustainable differentiation.

There will be rare success stories, such as Uber, Amazon and Netflix. Just like earlier rushes, like the dot-com bubble, a minority of players will make money from creating the AI & ML code, capability, and platform. But many more will become wealthy by inventing new products and creating a market.

The real question is this: Is your company ready and capable of capitalizing on AI ahead of 2020?


This is a contributed post. If you want to contribute columns & blogs to TFiR, please contact us.

Dr Marc Teerlink
Dr. Marc Teerlink is Global Vice President of Intelligent Enterprise Solutions at SAP. He drives the strategy, vision, and production of AI and machine learning technologies delivered through SAP Leonardo.