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Business Strategies for Deploying Disruptive Tech: Generative AI and ChatGPT

Rocket-Powered Data Science

First, don’t do something just because everyone else is doing it – there needs to be a valid business reason for your organization to be doing it, at the very least because you will need to explain it objectively to your stakeholders (employees, investors, clients). Test early and often. Expect continuous improvement.

Strategy 290
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Bringing an AI Product to Market

O'Reilly on Data

Without clarity in metrics, it’s impossible to do meaningful experimentation. Experiments allow AI PMs not only to test assumptions about the relevance and functionality of AI Products, but also to understand the effect (if any) of AI products on the business. Don’t expect agreement to come simply.

Marketing 361
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Experimentation and Testing: A Primer

Occam's Razor

This post is a primer on the delightful world of testing and experimentation (A/B, Multivariate, and a new term from me: Experience Testing). Experimentation and testing help us figure out we are wrong, quickly and repeatedly and if you think about it that is a great thing for our customers, and for our employers. Counter claims?

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3 force multipliers for digital transformation

CIO Business Intelligence

While many organizations are successful with agile and Scrum, and I believe agile experimentation is the cornerstone of driving digital transformation, there isn’t a one-size-fits-all approach. Here are some force-multiplying differences achievable by agile data teams: Want that dashboard, then update the data catalog.

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10 digital transformation roadblocks — and 5 tips for overcoming them

CIO Business Intelligence

Failure to align technology capabilities with business goals can result in a wasted investment in technology that doesn’t support business objectives. Foster a culture of innovation: Digital transformation requires innovation and experimentation, and thus a culture for embracing new technologies and ideas.

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Achieving cloud excellence and efficiency with cloud maturity models

IBM Big Data Hub

Everything runs seamlessly and efficiently and all stakeholders are aware of the cloud’s potential to drive business objectives. Teams are comfortable with experimentation and skilled in using data to inform business decisions. Service ownership is established and distributed to self-sufficient teams.

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The pandemic pivot: 5 key leadership lessons that will last

CIO Business Intelligence

The early days of the pandemic taught organizations like Avery Dennison the power of agility and experimentation. Zoom calls became the norm, but he struggled to truly connect with employees globally and instill the right business objectives and purpose. Employee crowdsourcing can yield breakthrough ideas.