ML & AI Strategy

Artificial Intelligence & Machine Learning

Easing businesses into the world of Artificial Intelligence and Machine Learning to amplify their net impact to customer and employee

The possibilities of Artificial Intelligence are endless. But the practicalities aren't.

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Icreon works with senior business leaders to identify holistic Artificial Intelligence strategies. Today, one of the challenges facing organizations is knowing where and how to invest into AI, Machine Learning & Deep Learning. And because the possibilities are endless, a number of capital expenditures go by the wayside and don't make the impact they're supposed to within the business.

Our Artificial Intelligence strategists work on identifying high-impact areas within a particular industry and pair that with proven-successes within the broad-spectrum of AI research. Our technique emphasizes maximizing AI & machine learning impact to create immediate revenue generating opportunities within a business.

This typically means infusing AI within the sales side of the business (such as POS and product recommendation capabilities) or the operational profitability of a business (such as labor reduction or supply chain optimization opportunities).

Knowing the future of Artificial Intelligence

The challenge with knowing the future of artificial intelligence is that the current boundaries feel completely amorphous. The true reality is that artificial intelligence will and to some extent has already transformed every industry. However, in more practical terms, the future of artificial intelligence is in knowing how best to utilize it in conjunction with a physical workforce, and knowing how best to re-activate your employees and stakeholders given that virtually all administrative activities over the next decade will become the responsibilities of adaptive and intelligent machines.

A Novel Approach to ML & AI

Because Icreon's teams have firsthand, real-world experience building out applications, platforms & ecosystems that are driven by machine learning and artificial intelligence systems, we know how to get ROI out of them immediately. And this real-world implementation knowledge sets us apart when it comes to helping business plan for their AI roadmaps. Today, having an AI Plan is just as important as having a Disaster Recovery plan or a Security Breach Plan or a Product Roadmap. Why? Because practically applying artificial intelligence with your business is one of the most effective ways to drastically reduce cost overheads or exponentially increase sales objectives.

And the reason why this is so imminent today is because AI has increased the value of having first-mover advantage within an industry. Whether it's using pattern analysis to dynamically predict retail sales, computer vision to automatically process inventory data, or natural language processing to more quickly understand customer satisfaction, properly leveraging AI, machine learning & deep learning is the difference between first place and fourth place.

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thoughts

Principles we employ when evaluating how to utilize Artificial Intelligence technology within a business

Thoughts and Considerations

Empathetic

Designing experiences that feel relateable to customers by understanding their intent and behaviors

Honest

Creating trust by having authentic interactions that are organic and customer centric

Facilitating

Harnessing datasets to figure out the next best product and service that can be of use to our customer-base

Evolving

Learning customer and stakeholder tendencies over time to better predict and anticipate user needs

Decision-Driving

Building confidence in decisions by backing it with past performance

Purposeful

Making conscious choices about where and how to utilize AI to tailor experiences without being intrusive

AWS
Azure
Caffe
Clarifai
Google Cloud
Keras
Sagemaker
Tensor Flow
Torch
Watson

Tools & Frameworks we leverage in order to build out scalable AI within businesses

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deeper

The Difference of Predictive vs Reactionary AI, and Why it Matters

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Why MLaaS will be the driver of AI digital transformation

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What is Machine Learning as a Service?

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Data is Still the Driver in Machine Learning

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The Difference of Predictive vs Reactionary AI, and Why it Matters

Something important to recognize is that each of these business processes optimized and streamlined by digital transformation take either a predictive or reactionary approach to embedded AI. While predictive is more beneficial to a company, it is nearly impossible to always be one step ahead. The best example of this may be the use of embedded AI in cybersecurity trends.

Companies hope to be as predictive as possible when it comes to protecting their devices and data from malware and cyberattacks. The hope is that AI can predict cyber threats before they do any damage to a business; however, there are so many new forms of malicious cyberattacks that it is impossible to predict them all. Therefore, security applications need to be reactionary as well. If a piece of malware does get through a threat intelligence solution, the embedded AI needs to be able to immediately take the appropriate steps to mitigate any damage or potential loss of data.

It would be an ideal world if we had the solutions to all of our business problems before we knew what they were, but that is just not a realistic expectation, so reactionary AI is still necessary. Spaces such as ERP, where the embedded AI provides insights based on historical data, are providing a predictive service based off of the reaction of prior performance, inexact human projections, and unknown or outside catalysts. Same goes for the people analytics provided by AI in HR solutions, along with a plethora of other business software.

Why MLaaS will be the driver of AI digital transformation

Any time there are a few big companies in a space it appears ripe for disruption, but enterprises are poised to dominate the MLaaS space for a variety of reasons, the first being big data. Simply put, they have it. It is getting easier to access open data sets (which are often open-sourced by the enterprise companies), but these corporations have access to exponentially more data than small or mid-sized businesses. Because they have data they have been able to build machine learning algorithms and train them with said data. This is an advantage that is almost insurmountable for small competitors and startups.
Another major advantage is competitive salaries the likes of Amazon, Microsoft, Google and IBM can offer AI developers, of which there are not many. Lean startups cannot afford to pay even close to a similar salary, and offering a stake in ownership is no longer enough. According to a New York Times piece from October 2017, large tech companies have put such an emphasis on AI that they are willing to pay beyond top dollar. “Typical A.I. specialists, including both Ph.D.s fresh out of school and people with less education and just a few years of experience, can be paid from $300,000 to $500,000 a year or more in salary and company stock, according to nine people who work for major tech companies or have entertained job offers from them,” the article read.
Now some might pass up those salaries to build something on their own or join a startup where they can have more control, but those unguaranteed common stock shares are difficult to compare to those shares of the tech giants. “Well-known names in the A.I. field have received compensation in salary and shares in a company’s stock that total single- or double-digit millions over a four- or five-year period,” read the Times’ piece. “And at some point they renew or negotiate a new contract, much like a professional athlete.”

The ability to grab talent is so important because there isn’t much of it. There is a major lack of people who are knowledgeable and skilled enough to build AI applications. Per the New York Times article, “In the entire world, fewer than 10,000 people have the skills necessary to tackle serious artificial intelligence research,” according to Element AI, an independent lab in Montreal, per the New York Times article.
These jobs are relatively new, so while there are many data science and machine learning courses being offered to students, it still takes years to receive the education needed to develop AI, so the talent gap will continue for some time. Since enterprise companies have the resources to attract the talent, they are able to build out their machine learning services, which will only increase the need for other businesses who can’t afford those employees to utilize MLaaS.
Also, many businesses already take advantage of public cloud providers, so adding one more microservice from the catalog is not too much of a hassle. If a business is already storing its data in an AWS or Azure public cloud, it is easy to adopt an MLaaS solution from those vendors. They can work with a business’ data, which is stored on their infrastructure, and help train their machine learning service to benefit the business. Not only will it be a quick deployment, but most likely inexpensive, another draw of microservices in general. This MLaaS approach will save the business time, energy and resources (which they do not have enough of), to help modernize a business with AI.

What is Machine Learning as a Service?

Machine learning as a service (MLaaS) is an umbrella definition of various cloud-based platforms that cover most infrastructure issues such as data pre-processing, model training, and model evaluation, with further prediction. Prediction results can be bridged with your internal IT infrastructure through REST APIs.
Amazon Machine Learning services, Azure Machine Learning, Google Cloud AI, and IBM Watson are four leading cloud MLaaS services that allow for fast model training and deployment. These should be considered first if you assemble a homegrown data science team out of available software engineers. Have a look at our data science team structuresstory to have a better idea of roles distribution.
Within this article, we’ll first give an overview of the main machine-learning-as-a-service platforms by Amazon, Google, Microsoft, and IBM, and will follow it by comparing machine learning APIs that these vendors support. Please note that this overview isn’t intended to provide exhaustive instructions on when and how to use these platforms, but rather what to look for before you start reading through their documentation

Data is Still the Driver in Machine Learning

By adopting artificial intelligence software and services, businesses can enhance product capabilities, better interact with customers, streamline business operations, and create predictive and precise business strategies. Developers can build quickly and efficiently with MLaaS offerings, because they have access to pre-built algorithms and models that would take them extensive resources to build otherwise. The developers who have the knowledge and skills to build machine learning models are few and far between, and are very expensive, so the ease and speed of setup coupled with the monetary benefits will be a major draw for business implementing AI in 2018.
Data is the driver behind machine learning, and because these huge companies produce and have access to so much data, they are able to build and train their own machine learning models in house. This allows them to offer it to outside companies as MLaaS, the same way that since they have more datacenter space than smaller companies they can provide IaaS. Generally, smaller companies do not have access to as much data to create powerful AI models; however, they do have valuable data that can be fed to pre-trained machine learning algorithms to create business-critical outcomes or actionable insights.
There are a number of MLaaS offerings for businesses to choose from, including natural language processing (NLP), computer vision, AI platforms and other machine learning APIs. Amazon, Google, Microsoft and IBM all offer different services for these machine learning functionalities. Additionally, these different types of AI can have unique impacts on many aspects of digital transformation.

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