Remove Experimentation Remove Marketing Remove Measurement Remove Risk
article thumbnail

Bringing an AI Product to Market

O'Reilly on Data

In this article, we turn our attention to the process itself: how do you bring a product to market? Without clarity in metrics, it’s impossible to do meaningful experimentation. Experimentation should show you how your customers use your site, and whether a recommendation engine would help the business. Identifying the problem.

Marketing 363
article thumbnail

How Svevia connects roads, risk, and refuse through the cloud

CIO Business Intelligence

Nearly 15 years ago, the then Vägverket Produktion was incorporated so road maintenance on Sweden’s national road network could be put on the competitive open market. Digital alerts Another project deals with slow-moving vehicles, something that increases the risk of accidents on the roads.

Risk 77
Insiders

Sign Up for our Newsletter

This site is protected by reCAPTCHA and the Google Privacy Policy and Terms of Service apply.

article thumbnail

Business Strategies for Deploying Disruptive Tech: Generative AI and ChatGPT

Rocket-Powered Data Science

3) How do we get started, when, who will be involved, and what are the targeted benefits, results, outcomes, and consequences (including risks)? encouraging and rewarding) a culture of experimentation across the organization. Encourage and reward a Culture of Experimentation that learns from failure, “ Test, or get fired!

Strategy 290
article thumbnail

Success Stories: Applications and Benefits of Knowledge Graphs in Financial Services

Ontotext

Let’s consider an example about risk and opportunity event detection. Case studies The risk and opportunity event detection use case discussed above combines all of Ontotext’s capabilities: storing and managing large amounts of data adding meaning to it (e.g.,, The business benefits here are also significant.

article thumbnail

What you need to know about product management for AI

O'Reilly on Data

You’re responsible for the design, the product-market fit, and ultimately for getting the product out the door. The need for an experimental culture implies that machine learning is currently better suited to the consumer space than it is to enterprise companies.

article thumbnail

8 Reasons Why Digital Transformations Fail (11 CIOs Weigh In)

Alation

Increasing revenue, going after a new market, etc., Failing to measure the impact of digital transformation against corporate strategies and OKRs. If you cannot measure the improvement, maybe don’t do it.” No place is the risk higher than data. Modernizing technology is not a business problem. are business problems.”.

article thumbnail

A New Era of Value-Driven AI

DataRobot Blog

Artificial intelligence is undoubtedly having a moment – and in response to market hype and increased AI investments, we’re even seeing tech companies reinvent themselves with AI identities. We are offering customers rapid experimentation and value identification, with both code-first and no-code approaches.