How to get more business value from artificial intelligence.
Since the Turing Test was developed in 1950 to ask the question, “Can machines think?”, the concept of artificial intelligence (AI) has expanded dramatically.
Today, AI can mean machine learning, computer vision, natural language processing, robotics, and more.
While countless films, from Blade Runner to The Terminator, have questioned the long-term implications of computers having human-like intelligence (think robots taking over the world), AI now focuses more on gathering and delivering insights to humans so that we can make better decisions—especially in the enterprise.
How can we ensure that we not only gather accurate, meaningful insights that drive business value, but also distribute them to the right people, at the time and on the device of their choosing?
Understand the value of machine learning.
Machine learning, the field of computational science centered on pattern recognition, provides the needed insights that bring greater understanding, predictive accuracy, and prescriptive intelligence to enterprises’ data sets, as well as contribute to diverse strategic outcomes.
Without looking too hard, you can already find machine learning all around you—when you ask Siri or Cortana a question; when you watch a movie Netflix suggested for you; or when you buy from Amazon based on product recommendations.
In enterprise businesses, machine learning proves effective at handling predictive and prescriptive tasks, allowing these companies to define which behaviors have the highest propensity to drive desired outcomes.
Enterprises eager to compete and win more customers are applying machine learning to both sales and marketing challenges.
A study by the Accenture Institute for High Performance found that, of the companies they surveyed:
- 40% are already using machine learning to improve sales and marketing performance.
- 38% credited machine learning for improvements in sales performance metrics.
- 76% are targeting higher sales growth with machine learning.
For example—sales, marketing, and channel management teams use machine learning to optimize promotions, while compensation and rebates drive the desired behavior across selling channels.
Beyond sales and marketing, citizen and professional developers can be guided through the software development process by machine learning. For example, the machine learning algorithm can suggest placement of buttons, tables, components, and whole page layouts—intelligently guiding the developer to create an app without writing code. We call this data-driven design. Gartner calls it augmented software design.
You need good data to make good predictions.
All of the data organizations amassed can be a goldmine—incredibly valuable now, and a predictor of value in the future.
But to unearth those data nuggets and make accurate predictions, the data itself must be accessible and properly prepared. In many enterprises, that’s simply not the case.
For many businesses, data is siloed in multiple different systems that don’t talk to each other, making it difficult to get a complete picture of what’s happening. That is why earlier this year Salesforce spent $6.5B to purchase Mulesoft, an integration platform for data and applications.
Low adoption of applications that require employees to input data, like CRM, can also damage data integrity. If employees don’t use the tools accurately (or if they don’t use them at all), data could be inaccurate, incomplete, and even change unpredictably.
To make AI accurate and effective, organizations need to ensure that applications are intuitive and easy-to-use, so that employees will adopt them. Additionally, organizations need to be able to easily access and capitalize upon data wherever it rests—be it in a data lake, or in multiple disparate locations like SAP, Salesforce, Oracle, Microsoft, AWS, Google, and more.
What do you do with all of those insights?
So, say you’re able to determine that your data is reliable, and then integrate and consolidate it so that it’s easy for machine learning to access.
How do you deliver those insights at the right digital moment, to actually get value from your AI investment? How do you go the last mile?
With Skuid, you can connect data from multiple sources—no matter where that data lives. You can use it to build beautiful, highly adopted business applications for any device. And you can use those applications to surface machine learning insights to the people that need them at the time and on the device of their choosing.
With Skuid, you can take your AI insights the last mile—where the real business value lies. And we’d love to show you how.