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Video: AI for Insurance

12 Reasons Why You’re Losing Data Scientists

Most Fortune 1000 companies are having hard time attracting and retaining data scientists. In this post I’ll describe many of the most common problems.

Talent

Don’t force your data scientists up the management ladder

As part of the release of our Winning the War for Data Science Talent report, we're digging into the tactics of building a world-class data science team. In this week's TechTalk, Kevin Dewalt and Alex Cunliffe discuss creating a non-management career path for your data scientists.

Strategy

Part 1: A Strong Opinion on AI Organizational Transformation

Within the next five years, every large enterprise will begin an AI transformation. After helping dozens of Fortune 1000 companies to modernize for AI, I have seen stark differences between those that succeed and those that fail. In this blog post I describe the three common AI transformation patter

Data Science

The critical flaw with AutoML: Big problems require human creativity

Like most engineers, I hate tedious work. After I solve a problem once, I want a computer to take care of it whenever it pops up again. I try to automate everything, including machine learning projects. That’s why I love the idea of automatic machine learning (AutoML). Any innovation that makes data

Data Science

Blindly following agile methodology painfully & slowly kills AI projects

You’re going to run into extreme opinions on this topic. Scrum, Kanban, and the many variants have become so standard that today’s developers and product teams can’t imagine working differently. The “agile is best” zealotry has become so ingrained that proponents will reject any contrary opinions.

Videos

Video: Natural-language Processing (NLP) for Insurance Companies

NLP technology is accelerating while costs are plummeting. As a result, most insurance companies are leveraging new language models like ULMFit, BERT, and GPT-2 for revenue-driving and cost-cutting initiatives.

Strategy

The debate is over: Centralize your data science team

Should you centralize your data science team and embed data scientists into product teams? Or should you hire data scientists directly into product teams?

Strategy

Demand simplicity

AI confusing and seemingly esoteric for a number of reasons. AI has its roots in academic research, and the terminology is based on communication between experts. AI is also new and few people have hands-on experience with it—just as nobody understood the Internet until they used it.

Talent

You need an AI transformation manager, NOT an AI product manager

As the business landscape slopes up toward AI Abundance, we need an additional specialized role to address the unique challenges of AI transformation. In this post we introduce you to the AI Transformation Manager (ATM).

Talent

How your data science career can survive the Covid 19 downturn

Data science has been one of the fastest growing fields over the past five years. Since starting Prolego I’ve reviewed more than 1,000 data scientists resumes, interviewed hundreds of candidates, and hired more than a dozen for my company or our clients. I’ve watched data science careers thrive …

Talent

You have the wrong technology workforce for the AI era

Like every successful large enterprise, your organization has likely made remarkable technological progress over the past two decades. The massive IT projects and the correspondingly massive failure rates are largely a thing of the past.

Data Science

A smarter approach to model governance

Entrusting your business decisions to AI does indeed create new risks, and you need new policies and procedures to mitigate them. But these sensational news headlines lead companies to take ineffective approaches that result in unnecessary delays and frustration. After helping some of the world’s la

Talent

The AI talent crises is real — and unlike the dot-com era

The AI talent shortage has evolved into a crisis, and every AI leader needs to begin resetting expectations with their leadership.

Strategy

Part 2: A Strong Opinion on AI Organizational Transformation

In the first part of this two-part series, I described three AI transformation patterns that lead to failure. In this post I describe the pattern that works.

Talent

Stop Looking for the Unicorn AI Product Manager

AI leaders soon realize after beginning an AI transofrmation efforts, that they need a quarterback for their AI initiatives. They need an AI product manager to lead the organization through all of the necessary changes. In this blog post, I’ll offer some suggestions to help you build a product-manag

Data Science

Amateurs talk about AI models. Pros talk about AI solutions.

Here is a hack I use to evaluate an engineering team’s AI experience: teams that are new to AI focus too much on the machine learning (ML) model. Because they usually come from a traditional IT or product background, they think of an ML model as an isolated library that performs a specific function.

Talent

So ... what is it really like to work in AI?

Let's face it. Most of what you hear about AI careers is b.s. from marketers, analysts, and recruiter firms. Of course there is a war for AI talent, but what do top candidates actually want from their careers?

MLOps

The most dangerous antipattern in MLOps: The model is “just a binary”

The most dangerous antipattern in MLOps: The model is “just a binary”For him, AI is the ultimate innovation challenge facing companies today. He co-founded Prolego because he believes AI innovation is different.

Strategy

My Secret Hack for Identifying AI Opportunities in the Enterprise

It can be difficult to discuss AI opportunities with business customers as they probably don’t understand AI. After years of helping our clients overcome the myriad of challenges to identifying AI opportunities, I’ve developed a few hacks to help overcome them. Here, I’ll share my favorite one. I ca

Strategy

The remarkable power of storytelling to spread AI in your company

A compelling story of AI can help others understand your vision and become evangelists for it. In this article, I explain why stories are a critical part of your AI strategy and share some case studies.

Strategy

The Coming NLP Revolution in Insurance

Companies only begin investing in AI when an “AI killer-app”—a must-have competitive asset for every company in the sector—arrives. Computer vision is an AI killer-app for automotive. Recommendation engines are an AI killer-app for ecommerce. Predictive analytics is an AI killer-app for retail. When

Talent

How We Interview Data Science Candidates

In this week's tech talk we share best practices developed over the last five years for interviewing data scienctists.

Data Science

Natural Language Generation - What Can You Develop in the Next 3 Years

In this video Justin Pounders, Director of Machine Learning and AI Research at Prolego breaks down natural language generation (NLG) into its most basic components and describes how you can begin building out these components in your business.

Strategy

NLP for Financial Services

Every large financial services company is pursuing NLP initiatives in 2022. In this blog post, I’ll put aside the technobabble and address the key considerations for applying NLP in financial services companies.

Data Science

A Crash Course on Model Compression for Data Scientists

Deep learning models are wildly effective at solving complex problems using unstructured data. Unfortunately, these models are also (usually) big, slow to run, and resource intensive. This blog post explores options to reduce the size, inference time, and computational footprint for trained deep lea

Strategy

The guaranteed path to AI failure: An unmotivated business partner

AI initiatives always start with the best of intentions. You create your AI strategy. You get the budget approved. You find some qualified data scientists to do the work. You identify the first AI project and have your kickoff meeting with the business partners.

Data Science

Fortunately, all coronavirus models are wrong

Working in AI has changed how I view the world. In traditional software projects I think about solutions in terms of time, scope and resources: What do we want to build? How much time do we have? How big is the budget?

Video: AI for Insurance

12 Reasons Why You’re Losing Data Scientists

Don’t force your data scientists up the management ladder

Part 1: A Strong Opinion on AI Organizational Transformation

The critical flaw with AutoML: Big problems require human creativity

Blindly following agile methodology painfully & slowly kills AI projects

Video: Natural-language Processing (NLP) for Insurance Companies

The debate is over: Centralize your data science team

Demand simplicity

You need an AI transformation manager, NOT an AI product manager

How your data science career can survive the Covid 19 downturn

You have the wrong technology workforce for the AI era

A smarter approach to model governance

The AI talent crises is real — and unlike the dot-com era

Part 2: A Strong Opinion on AI Organizational Transformation

Stop Looking for the Unicorn AI Product Manager

Amateurs talk about AI models. Pros talk about AI solutions.

So ... what is it really like to work in AI?

The most dangerous antipattern in MLOps: The model is “just a binary”

My Secret Hack for Identifying AI Opportunities in the Enterprise

The remarkable power of storytelling to spread AI in your company

The Coming NLP Revolution in Insurance

How We Interview Data Science Candidates

Natural Language Generation - What Can You Develop in the Next 3 Years

NLP for Financial Services

A Crash Course on Model Compression for Data Scientists

The guaranteed path to AI failure: An unmotivated business partner

Fortunately, all coronavirus models are wrong

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