A deeper understanding of AI/ML methodologies can help you realize greater business value. An end-to-end understanding of how your business dynamics interact with these emerging technologies can achieve much more.
Copyright: cio.com – “How organizational learning can unlock more business value from machine learning”
We routinely underestimate the effects that new technology has in the long run while also overestimating its impact in the short term. What has become known as Amara’s Law, in honor of the late researcher and scientist Roy Amara, is playing out now in many organizations that adopt artificial intelligence and machine learning (AI/ML) technologies. AI/ML has capabilities that seemed science fiction not long ago, from autonomous driving to image recognition and advanced robotics, with surely many more on the way. At the same time, even advanced enterprises can struggle to achieve the anticipated return on their AI/ML investments. Although expectations might have been set high, following several guidelines has helped our customers maximize the business value delivered by their AI/ML initiatives. We’ll share them in this AI/ML blog post mini-series.
There Is No Magic: Acknowledge the Uncertainty
The pressure to deliver solutions faster and at lower cost while ensuring compliance, security, and reliability has caught many IT departments between a rock and a hard place. It’s no surprise, then, that they’re looking for new solutions to break through old barriers, and rightly so: modern technologies coupled with new ways of working can help transform IT from a cost center to an innovation driver. However, too often the latest technology is heralded as the panacea that’ll cure all the previous technology’s shortcomings. It’s therefore important to remind ourselves that while modern IT solutions can overcome past limitations, there is no “increase my customer satisfaction” algorithm, neither within AI/ML nor in any other technology.
AI/ML solutions model human reasoning by allowing the computer to make judgement calls (“inference”) based on past positive or negative outcomes. Just as for human reasoning, the result remains an educated guess. Building more sophisticated models can increase accuracy, but the business value of that accuracy needs to be weighed against the cost of building and training the model.
Upfront business cases that treat AI/ML initiatives as if they were deterministic software programs run the risk of overinflating expectations. You can’t expect an out-of-the-box AI/ML model to already be perfectly calibrated to your business problem and immediately make good decisions. The value the solution delivers depends largely on how well you’re able to balance investment and gain in the face of uncertainty. For example, if you’re looking to use a new data source for ML (or any analytical process for that matter), you’ll face a high degree of uncertainty, both for the required investment and the possible benefits. Let’s say you want your ML product to use daily stock levels from your warehouses around the globe to decide which items to advertise. You can’t be sure whether the data quality is sufficient for automatic processing, how much harmonizing data from the different warehouses will cost, whether daily stock levels are even relevant within your business processes, or how you can use that data to improve your top line or bottom line KPIs.[…]
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