Top 5 AI implementation challenges and how your company could overcome them

Although corporate spending on artificial intelligence topped $50 billion last year, just 11% of companies that enhanced their workflows with AI have already seen a significant return on their investments. In this article, we’ll investigate business, technological, and ethical issues haunting AI projects — and provide several tips to seamlessly integrate Artificial Intelligence into your company’s Digital Transformation strategy.

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My Encounters with limitations of AI & ML in the Food & Beverage Industry: Lessons in expectation…

As a co-founder at AI Palette, I spend a lot of my time with brand managers and consumer insights teams to discuss the many capabilities and applications of Artificial Intelligence (AI) and Natural Language Processing (NLP) in the Food and Beverage (F&B) Industry.

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4 business strategies for implementing Artificial Intelligence

AI Strategies

Machine learning technologies are more accessible than ever, but finding the business case for AI isn’t always straightforward. In this piece of writing, I would like to make AI business strategies more concrete by walking you through four AI strategies that you can use to improve any activity you could imagine. After walking through the four strategies, I’ll help you figure out which strategy to use for any given activity.

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Gen Z is defining digital banking – FinTech is listening

Gen Zers are the true digital natives. From streaming to sharing, those between the ages 18 to 24 expect seamless connectivity — and their finances are no exception. The banking industry has shifted servicing dramatically from its beginnings as a brick-and-mortar stalwart to become far more digitally flexible. As one of many pandemic impacts, having a choice for complete online banking is an ageless expectation, however for Gen Z it has become a table stake.

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AI is transforming the Logistics Landscape: Here’s a glimpse at how, why and by when

As the COVID-19 pandemic continues to put pressure on supply chains worldwide, the prospect of leveraging AI logistics tech has become more attractive than ever. Data-driven solutions are giving companies the power to increase profitability, resiliency and meet the new demands of commerce in times of unpredictability. AI and machine learning (ML) technologies are now being widely applied across distribution networks. In fact, 61% of executives report decreased costs and 53% report increased revenues as a direct benefit of introducing AI into their supply chains.

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Which Data-Science skills are the most vital in 2021?

There are a lot of industry standard tools that any aspiring data scientist will certainly want to be familiar with. Experience with these tools is almost always presented as a requirement on job listings because they are likely the tools you will be working with in-house. At the very least, familiarity with the concepts presented by the tools will make them easier to utilize before you have gotten the chance to get experienced with them.

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Computer Science Degrees: Are they Worth Getting in 2021?

Is a computer science degree worth it? For me, partially. For you? You tell me. In August 2018, I enrolled in college to get my computer science degree. However, I wasn’t your typical CS student. First off, I was almost 30 and already had a college degree from nine years earlier. Secondly, I was already a professional software developer with a full-time job.

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76% of enterprises prioritize AI & Machine Learning in 2021 IT budgets

Enterprises

Enterprises accelerated their adoption of AI and machine learning in 2020, concentrating on those initiatives that deliver revenue growth and cost reduction. Consistent with many other surveys of enterprises’ AI and machine learning accelerating projects last year, Algorithmia’s third annual survey, 2021 Enterprise Trends in Machine Learning finds enterprises expanding into a wider range of applications starting with process automation and customer experience.

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How AI transforms Copywriting

What if marketers could leverage artificial intelligence for copywriting to deliver content that resonates with specific audiences? What if, instead of relying on gut instinct alone, creative teams could be mathematically certain about the words and phrases to use in marketing campaigns? It is now possible to apply science to the art of copywriting, and many brands have already started bringing together man and machine to produce compelling copy and achieve better results.

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Marketers who deploy AI can have an upper hand in lead generation

Artificial intelligence is now a part of our day-to-day lives and it’s an ideal opportunity to utilize the numerous AI lead generation tools in your business. Artificial intelligence dominates at extricating insights about leads from your marketing and sales data. Some AI-powered tools utilize that information to disclose to you more about your prospects and customers.

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The AI arms race comes to enterprise content management

Enterprise content management (ECM) platforms that have historically been employed to manage files are, thanks to the rise of AI, about to evolve into central repositories for keeping track of relationships between a much wider range of types of data.

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Deploying Machine Learning into production: Don’t do Labs.

In my experience building analytics products at Best Buy Canada, applied data science projects rarely fail because of the science. They fail because the model couldn’t be integrated into existing systems and business operations. We have had sound models showing good accuracy, even with proofs of concept demonstrating value, yet still fail to get deployed into production. The challenge isn’t POCs, its scaling.

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How to retain your institutional knowledge when employees retire (& how can AI simplify this)

Since skills, knowledge and experience are vital to a successful business and the pace in which it innovates, retaining existing institutional knowledge is an increasing priority. How can you guarantee that your company’s know-how won’t just walk out the door and jeopardise your brand and positioning? The short answer is: You can’t. But there are ways that utilising a combination of analytics, IoT, and AI techniques, along with corporate training and knowledge replacement strategies, can help.

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How the Dunning-Kruger effect can explain why your data science proposals don’t get buy-in

Consider the Dunning-Kruger effect to get your proposals taken seriously.
Many brilliant data science proposals never make it beyond the paper they’re written on. I’d like to start off by painting you a picture. Imagine you’re an experienced data scientist. You work for a small company and report into a team of directors who lead the company and are responsible for all the decisions made. Only proposals that get their buy-in can be implemented.

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The future of commercial Deep Learning

How do we balance its benefits and integrity going forward? Underlying modern AI is deep learning, algorithms through which computers learn to perform intelligent tasks without being explicitly programmed. These algorithms train artificial neural networks, which iteratively learn relationships between inputs and outputs through copious examples. My solution to preserving the benefits of commercial deep learning while prioritizing its integrity is three-fold:

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Modelling Risk: the absolute and the relative

What is the risk of a new creditor to default on their loan? what is the “risk” of watching a certain movie on Netflix given a certain viewing history? What is my risk of dying given a certain diagnosis and how is this affected by a certain treatment? Risks are all around us, and quantifying these risks is becoming increasingly popular. Providing the right kind of analysis to these key questions is crucial to making the right decisions.

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