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AI doesn’t just change an organization’s technology. AI creates disruption at the cultural level: it requires people to collaborate in new ways, gain new skills, and adopt new processes. If this transformation isn’t handled with care, even the strongest AI strategy will flounder.​

​The next question is: within your organization, who is responsible for what tasks when it comes to AI? We’ll dive deeper into how you can assign AI-related responsibilities in your organization.


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The capacity to pivot as a business and foster innovation is crucial to long-term success. Innovative companies generally lead their industry’s market share, attract top talent, and create cutting-edge products and services. So what processes do we need in order to start fueling innovation?

First, foster a culture of meaningful innovation throughout the organization. This may involve setting up a group or committee for that purpose. Then, involve all employees in strategically solving business problems by applying innovation. Disruptive solutions should be encouraged and success is measured by impact. …


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The societal implications of AI and the responsibility of organizations to identify and reduce unintended consequences of AI technology are significant. Considering this responsibility, organizations are finding the need to create internal policies and practices to guide their AI efforts, whether they are deploying third-party AI solutions or developing their own.

AI is the defining technology of our time. It is already enabling more profound progress in nearly every field of human endeavor and helping to address some of society’s most pressing challenges. …


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A successful AI strategy must consider cultural issues as well as business issues. Becoming an AI-ready organization requires a fundamental transformation in how you do things, how employees relate to each other, what skills they have, and what processes and principles guide your behaviors. This transformation goes to the core of an organization’s culture, and it’s vital for organizations to tackle such transformation with a holistic approach.

There are some requirements to foster an AI ready culture. The first is to be a data-driven organization. Next, an AI-ready culture empowers people to participate in the transformation and creates an all-inclusive…


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When business leaders have a better understanding of the why (Business of AI: An Overview) and the what (Business of AI: Tech Overview) of AI implementation, next comes the how. Implementation starts with strategy. This article will cover the framework of AI strategy. The following framework was developed by INSEAD and is covered in Microsoft’s AI Business School.

Framework Overview


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Smart cities. Unlocking phones with your face. Search engines that complete your queries for you. Responsive chat bots. All of these innovations that allow us as humans to substantially become more effective and efficient stem from AI and machine learning. Organizations that find where the human element can be enhanced utilizing AI and machine learning are gaining competitive advantage. As data and processing power have become available at the small business level, AI and machine learning are becoming viable solutions for businesses of all sizes. Before considering AI and machine learning solutions, it can be helpful to understand what these…


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Courtesy of denimlabs.com

AI is changing the way the world does business. The vast amounts of data and the speed at which this data is processed has made artificial intelligence more adept and usable at an everyday level. As a business decision maker, implementing AI may at first seem like navigating through uncharted territory. From an employee perspective, AI may look like their replacement. This series of articles will cover the business of AI: from building a culture to making it responsible.

Why to Consider AI

Gartner predicts that in 2021, artificial intelligence (AI) augmentation will create $2.9 trillion of business value and…


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One of the current trends of AI is empowering all employees to become citizen data scientists. Embedding intuitive chat bots and image classification into a website no longer takes exhaustive coding to implement. Software as a Service (SaaS) can now allow just about anyone to create and implement common machine learning practices into their apps.

Power BI is a Microsoft product that serves as a reporting tool for business intelligence (BI), which is a way of creating visual stories that allow key stakeholders to make business decisions. …


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Data Science collaboration is changing. Gone are the days of sharing files over email. No longer is it necessary to have new team members download dependencies in order to work on a project. This article is going to cover some options that are out there now that will improve the efficiency of your Data Science team and the quality of your workflow.

Databricks

Databricks is an amazing Apache Spark product that has evolved into and end-to-end workflow that incorporates parallel processing into a machine learning workflow. Environments can be loaded pre-configured and customized for a particular project. Utilizing notebooks, data science…


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Photo by Will Francis on Unsplash

Whew…it’s been a few weeks, but glad to be caught up! The goal for these posts is to hopefully find something that was new to me so I can ‘pay it forward’. Many of you have probably heard of GridSearchCV and perhaps even RandomSearchCV, but what about informed searching?

The advantage of using an informed searching technique is that hyperparameters are tuned through sequential learning. GridSearch and RandomSearch are great, but they only go one layer deep. Informed searching learns from prior hyperparameter tuning to optimize the tuning process. …

The Data Detective

Solving Next Level Business Mysteries

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