While AI and machine learning has made rapid progress in consumer facing applications such as Amazon Alexa, playing Go, and making art, its deployment across the enterprise is still emerging and uneven. We discuss key facts and implications on enterprise AI.

Opinions differ on the scope and pace of A.I. to automate and enhance activities across the organization. On one end, an Oxford study suggested >50% of all jobs could be automated in the next two decades. On the other spectrum, many executives still remain doubtful to the practical impact of AI. In addition, the current hype cycle and misinformation in the media further cloud perspectives.

Based on client engagements and interviews across academia, startups, and enterprise users, we highlight 4 key facts and strategic implications on AI enterprise automation:

Fact 1: The current A.I. boom is sustainable and executives should not ignore it

For the first time in history, machine learning is beating human performance across tasks such as image recognition, translation and complex games. Three drivers are fueling the current machine learning boom. (1) continued breakthrough in Deep Learning algorithm sophistication. (2) rapid increase in big data (or structured data) to train such algorithms. (3) an exponential speedup in hardware such as the GPU chipset that cuts down training time from months to days and hours.

All three underlying drivers of today’s machine learning advances are expected to accelerate into the near-term future (see Figure below). By 2020, almost 70% of all enterprise data will be processed via cloud. This will provide unprecedented big data infrastructure as input for machine learning algorithm training. Similarly, hardware chips for speeding up machine learning algorithm training and processing is making rapid inroads. Google, NVidia, Intel, and others recently announced next-generation GPU chip hardware that will further accelerate training speed by 10-100X fold. Lastly, advances in the underlying algorithms continue to accelerate, as evidenced by an increase in patent filings.

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The above offer strong evidence that advances in A.I. will continue to improve in the near-term future. Ignoring A.I. is no longer an option for business leaders.

Fact 2: A.I. use-cases are pervasive across the organization – but limited in scope

Given that AI advances will continue to accelerate, what can A.I. do in the next 5 – 7 year near horizon? The consensus expert view is that most of the enterprise use cases will be driven by narrow A.I. (e.g. supervised learning) and that achieving broad, human like general intelligence is decades away.

In short, AI algorithms will have the capability to learn to automate a task from training data. Once the solution learns a task, it will be narrow in scope and cannot be generalized to perform other tasks. See figure below for a sample of use-cases that are possible today or emerging in the next 5 years:

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Consider the following computer vision use case. A company might want to train an AI algorithm to automate scanning of PDF and hand-written invoices, validating field formatting and triggering automatic accounts payable processes.

The resulting AI solution is fundamentally limited to automating text field recognition and formatting. If the company wanted to use the invoice processing bot to say detect fraud, they would need to design and train a completely new solution by focusing on other underlying features and patterns.

AI applications in the foreseeable future will be paradigm. This has two strategic implications:

– Acquiring labeled data for training becomes a strategic capability and source of differentiation

– AI solutions require deep functional and domain specific human co-creation and process redesign

Fact 3: Focus on prioritizing A->B activities 

The Japanese insurer Fukoko recently announced replacing human agents with AI for claims processing. Goldman Sachs transformed its 600 trader unit into a much leaner 200 person team augmented by machine learning.

However, not all organizational activities are suitable for AI automation under today’s narrow learning using data paradigm.

A helpful way to characterize machine learnable tasks is what Andrew Ng calls A->B activities; activities that take a set of clear, unambiguous inputs A and produces a response B.

For example, retail demand forecasting can be considered an A->B activity. By taking a diverse input such as seasonality, social media signals, and price sensitivity data, the algorithm can beat human level accuracy.

Financial trading is also an A->B activity. A trading algorithm would take a set of inputs such as historic prices, macro trend drivers, arbitrage rules complied from past traders, etc. and produce an output of either Buy or Sell. Making the right trading call may be a hard problem, but an AI solution is still attractive if it can outperform humans over large number of trades.

See figure below for some other A->B tasks suitable for AI as well as activities that are not suitable.

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Mapping enterprise processes and activities into a spectrum of A->B categories can help managers ensure a systematic opportunity scan.

Fact 4: AI Adoption requires more than technical feasibility

Some AI applications will achieve faster adoption rate than others, even though the underlying technical requirements are comparable. Companies need to consider adoption drivers to balance unlocking near impact value with longer term aspirations:

One-time cost: the initial capital outlay for developing the AI solution such as algorithm development and training data acquisition. Open source access to AI algorithms, and pay as you go, “AI as service” platforms can help reduce fix cost hurdles. Access to training data often becomes an expensive bottleneck.

Switching cost: the associated costs and hurdles in displacing the current solution with a new AI solution. This includes both technical hurdles such as the ability to open the AI algorithm black-box and human hurdles such as political, cultural and change resistance barriers.

Eco-system needs: the need for complementary technologies as part of an integrated solution. For example, an AI solution that requires integration with innovative IoT sensors and emerging robotics will experience higher adoption complexity.

System externality hurdles: the extent to which the value of solution adoption increases with more adoption.

See the figure below for three illustrative A.I. use cases with different adoption challenges and potential scaling timeline:

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A.I. automation use cases are rapidly becoming realities across organizations and value chains. Business leaders should adopt a disciplined and portfolio based approach to develop machine learning capabilities.

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Based on our whitepaper: “Unleashing the Power of AI for Enterprise Automation”  | Read Chinese version here

The author would like to thank Eric Gervet, Florian Dickgreber, Sean Monahan, Denis Bassler, Joseph Edwar, and Petr Materna for their feedback.

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