What is the state of AI adoption?

In the long-term, AI-based algorithms will be serving as a new general-purpose “method of invention” that can reshape the innovation process and the organization of R&D. For now, AI has just started to deliver real-life business benefits, as confirmed in the latest McKinsey report.

Proactive AI-adopters can see a significantly higher profit margins in comparison to non-adopters. They are also positive about the future, predicting to grow and benefit even more, as AI applications mature.

McKinsey Report: “Artificial intelligence: The next digital frontier?”

Benefits of adopting Artificial Intelligence in your business

Do you think it’s a time for your business to get smarter with AI?

According to Statista, advertising, finance, healthcare, consumer, and aerospace are the sectors leading in AI adoption and the AI market itself is projected to grow to almost $60 billions in the next 7 years.

Adopting AI mechanisms into your technology stack can disrupt core business processes that are highly time-consuming, eg. scheduling, resource allocation, reporting.

On top of that, in the previously mentioned report, McKinsey determined four business areas where AI can create value:

enabling companies to better project and forecast to anticipate demand, optimize R&D, and improve sourcing; increasing companies’ ability to produce goods and services at lower cost and higher quality; helping promote offerings at the right price, with the right message, and to the right target customers; and allowing them to provide rich, personal, and convenient user experiences.

All these will lead to better products and services generating more profit, because simply put — consumers needs will be addressed smarter with better user experience and business processes will be handled more efficiently with better utilization of the relevant data.

First, get familiar with the basics of AI

The basic concepts of AI aren’t new at all. First IA was born back in 1950’s with the idea of autonomous computing.

This IBM 702 computer in a picture below was used by the first generation of AI researchers.

Image source: Wikipedia

What did change since then so we do not need a space of entire state to accommodate computer power necessary to handle intelligent computer processes?

The infrastructure speed, availability, and sheer scale has enabled algorithms to tackle more ambitious problems. Also computing power of graphic cards and their price dropped so we can now do things that previously were not cost-effective.

Not only hardware is better but also smart computers can now be deployed to the cloud at a fraction of the cost and much more easily.

Plus there are vast quantities of data being produced every day, which can be “fed” into AI mechanisms enabling them to learn and produce output.

And finally, the grundbreaking trigger for AI being reborn are the advances in the natural language processing toolbox made thanks to artificial neural networks and deep learning techniques.

Terms related to AI are often used interchangeably, but they are not the same thing. So let’s introduce some basics of AI terminology.

Artificial Intelligence

After Technopedia, AI is an area of computer science that emphasizes the creation of intelligent machines that work and react like humans. AI systems aren’t perfect by default. They have to learn and adapt, by taking in data, processing it, and storing it for future reference. This is where Machine Learning comes in.

Machine Learning

After Prof. Youshua Bengio, ML is a part of research on artificial intelligence, seeking to provide knowledge to computers through data, observations and interacting with the world. It is an application of AI and at its most basic level, it is the practice of using algorithms to parse data, learn from it, and then make a determination or prediction about something in the world — Nvidia.

Deep Learning

After Investopedia, it is the subset of machine learning composed of algorithms that permit software to train itself to perform tasks, like speech and image recognition, by exposing multilayered neural networks to vast amounts of data.

Neural Networks

Neural networks are a set of algorithms, modeled loosely after the human brain, that are designed to recognize patterns. They interpret sensory data through a kind of machine perception, labeling or clustering raw input [source].

How can computer learn new things?

In most simple case, instead of writing a predefined program from scratch, the system is exposed to lots of examples that specify the correct output for a given input. Then machine learning algorithm takes these examples and produces a program that does the job.

With deep networks a feature extraction and classification is done in one shot, which means programmers only have to design one model and neural networks will classify information in the same way a human brain does.

The difference between how Machine Learning and Deep Learning work is presented below:

Most common tasks that can be solved by learning are:

Recognizing patterns:

Objects detection and tracking

Facial identities or facial expressions

Speech recognition and words recognition

Recognizing anomalies:

Unusual sequences of credit card transactions

Unusual patterns of customer behaviour

Prediction:

Future stock prices or currency exchange rates

Cost forecasts, weather forecast

Future trends based on company’s KPIs

Customer preferences based on past purchase and web behaviour

Curious how this work in practice? Here you can find out how by using neural networks we counted the total time during which a specific company was advertised in various places at a football match: Neural Networks for Advertisers.

Data sets sample being prepared as input for neural networks

Second, seek out the IA-driven competitive advantages

First of all don’t get yourself down with the “AI bandwagon hype”. Ask the right questions:

How certain technology can help me solve my business problem completely and in a scalable way?

Why in a scalable way? Because things will change and your organisation must adapt and be ready to handle the constantly growing volumes of data.

If you do not measure something, you can not manage it! For every CEO, full control means certainty about the effectiveness (or lack thereof) of given activities and the determinants of them. However, the collection of data is not enough, you need to use it skillfully, which translates into real company value.

To make sure your AI implementation will become a successful project, start from an overview of your technology stack, challenges and problems. And:

Tie your initiatives directly to business value.

You will need an audit trail to make sure the solution fits seamlessly into your technological environment.

Adopting AI still seems like an uncharted territory for many. Gartner proposed a “framework” for CIOs when integrating AI into your business:

Sort out the type of AI applications to produce the kind of results your company is looking for. Think about how the technology will be used to integrate AI. Look into the common solutions used by various enterprise applications. Rank use cases in terms of risk, value, costs and scalability. Think iteratively to refine your use case within the new acquired context.

Similar approach is recommended by McKinsey:

McKinsey Report: “Artificial intelligence: The next digital frontier?”

Third, bring Machine Learning engineers on board

You have to estimate the internal capability gap to find out what your organisation can achieve in a given time frame. Here comes a question of partnering with a team of machine learning experts to help you with the project or hiring an internal team.

The costs of hiring AI-related engineers are high, similarly valuable is the time spent on the hiring process. If you are looking for a verified tech partner, we can take your mind off AI-software development.

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