Scaling AI solutions to deal with real data, business users and customers is fraught with risks and difficulties. Even experienced AI-savvy organisations have fallen foul of growing AI solutions to production size.
This article highlights 5 areas where scaling AI for production can be problematic. If you want to introduce AI into your business, it’s critical to get this right. Understanding why it’s tricky is the first step towards that.
There’s no consensus yet on the “right” approach to AI projects. Nor will you find agreed, proven estimating models or benchmarks specifically for AI project costs and durations.
There’s plenty of guidance on how long regular IT development should take, but much of it tends to focus on activity to elicit requirements from users, or build technical designs and write code. There’s much less help for the more unstructured work at the heart of AI work — such as crunching data, evaluating algorithms and processing training data.
For now, estimating the non-traditional parts of AI work often relies more on…
With AI still in its infancy in the business world, there are plenty of ways for AI to go wrong. And there’s still limited widespread knowledge on how to avoid AI pitfalls.
Many are technical, but some aren’t. Businesspeople can play a valuable role in flagging risks, especially those with significant business impact.
This article lists some important AI pitfalls relevant to AI business stakeholders, with tips to steer around them. They’re a result of the anecdotal, personal and published experience of AI projects that could have gone better.
Starbucks doesn’t simply sell huge numbers of hot and cold drinks around the world — it also gathers huge amounts of data from over 100 million transactions a week. How does it use this data? And what role do A.I. and the internet of things (IoT) play in this?
The way Starbucks uses data and modern technology for competitive advantage is instructive for all businesses, regardless of size. For example, it’s a pioneer in combining loyalty systems, payment cards, and mobile apps. But that just scratches the surface.
This article highlights five of the most interesting examples of how Starbucks…
Netflix’s best-known use of AI technology is recommending what to watch next. But that’s only a small piece of how it uses AI. Like others, it uses AI & sophisticated analytics across the organisation.
It’s been at the leading edge of AI for many years. But in 2009 it inadvertently scored a high profile AI own-goal. This was the Netflix Competition, something they’d run since 2006.
The episode has become part of AI folklore, but the lessons are still relevant today. They’re especially pertinent if you’re new to AI, or complacent about the risks and pitfalls of AI in business.
If you solve business problems with data, there’s a good chance you already use data visualisation tools. Their ability to present data analysis result can be critical in extracting insight from information in many aspects of the business.
But some, probably many business people are unaware of such tools, either doing without, or restricted to whatever their IT department gives them.
There is already a gap between those equipped to make their data work for them, and those who aren’t. The prevalence of AI across organisations and industries will only exacerbate this.
But using data visualisation tools for AI work…
Every second, 10,000 drinks from the Coca Cola Company are consumed, making up nearly 2bn sales transactions a day over 200 countries. Because it has so much data, you’d probably assume Coca Cola uses AI extensively to improve its business.
What you may not expect is that Coca Cola uses AI to create intelligent vending machines. An increasing number of these are equipped with touchscreens, wireless connectivity, and computing power.
Or is there a rational business…
A few years ago, it wasn’t unreasonable to build bespoke systems to computerize most business needs. Then, as the IT industry matured, pre-built software became more effective, especially for commodity functions like accounting.
AI is in the early days of such an evolution, and most AI work today seems to be bespoke development. “Off-the-shelf” solutions are appearing, but are mostly “platforms” to adapt and configure, not products to unwrap and use.
But in due course, that will change, and the arguments for building your own AI will become more tenuous. …
What’s the difference between good and great AI business projects? Most advice on building effective AI focuses on the data science and technical aspects of work.
These are of course crucial, but there’s now enough industry experience to start speculating on other characteristics of effective AI work for business.
Here are 7 traits I believe contribute to the business success of great AI work. Hopefully it’s food for thought on your aspirations for your own AI business projects.
Most people working in AI would probably acknowledge that highly effective AI business projects still aren’t the norm. …
All businesses will be affected by AI in the coming years, and the impact for most will be significant. As a business executive, are you prepared for what this will mean for you and your organisation?
This introduction to artificial intelligence may help. It’s aimed primarily at business leaders new to AI, and its focus is on understanding what AI is and means in a business context.
It’s a self-contained extract from a longer piece first published on AIPrescience.com. The full article additionally discusses how organisations might build, apply and benefit from AI.
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CEO of AI research & consulting firm AI Prescience. Author of “AI & Machine Learning” (SAGE). 30+ years experience using data & technology to improve business.