Using AI for customer service in your company is a definite method to save time and money. If you’re like most business owners, you’re constantly searching for fresh, creative ways to improve your enterprise. We’re here to inform you that improving AI customer service is a simple and rapid win.
If you have a team of software engineers and want to move them to Mac, you will need to consider a number of things before you do so.
“By the time you find perfection, your standards for it will have changed.”
Anonymous
If you’re a business intelligence manager or even a software engineering manager, you probably love dashboards for providing a birds-eye view of your most important metrics.
But, are dashboards effective enough? Do you get the actionable insight you need? Or do you have the time to dig for the actionable insight you need? Ohm..right?
Granular details require some time to explore and relevant insights may not be readily apparent. We also feel sometimes that quick answers and actions are just an illusion. But then the emerging trend of digital assistants make us think future is right about to shine.
Improving profitability is of interest to both business intelligence managers and software engineering managers. Let’s take a look at a real use case of how performance analytics works in the engineering world:
So, finding ways to help developers to be the best coders they can be is time well-spent. It’s actually an investment.
It’s a small step, but it’s an example that you can consistently replicate. This is a better investment then the NYSE or BitCoin! Not to be cold about it, but developer time is a precious commodity. And no one has more of an influence on your team’s performance than BI managers, engineering managers, and analysts.
Data-driven analytics enables you to help the developers on your teams do that 10x thing we talked about earlier. Today’s dynamic dashboards make it exponentially easier to find the specific things that can help each developer code better and improve your entire team’s cycle time.
However, sifting through lots of metrics takes time and doesn’t guarantee actionable insights. And your time doesn’t scale. Or… does it? How many developers or teams are in your organization?
We’ll come back to this after we look at where we’ve been on our information superhighway. We didn’t always have dynamic data, and it can be easy to forget just how easy our jobs are today… compared to chiseling reports on clay tablets.
Clay tablets? Being facetious, sort of. A little bit of fun shouldn’t hurt. Figured a quick look back at the evolution of reports may add some context not just to data, but our exponentially expanding appetite for it. Before we had dashboards, all of the information shared, data recorded, and reports generated for the past 4,600 years were static. It was all… missing links.
Static reporting shows everyone the same data for a specific and non-adjustable period. They can’t be changed, without generating a new report – or configuring a new dashboard view. Each user, however, may want to see the information in a different way, or different information altogether. C-Levels and Business Intelligence Managers want to see organization-wide performance just as engineering managers are focused on their teams’ performance. Team leaders want to see how their individual contributors are doing.
Dynamic reporting accomplishes that, streaming or refreshing everyone’s most important metrics so that they’re conveniently available at a glance. Plus, while not a van Gogh, they’re easier on the eye than 1,048,576 rows of an Excel spreadsheet. But, is the data on your dashboard actionable enough? Even if it isn’t, dynamic dashboards provide users the ability to dig into the data to gain deeper insights into what’s going on.
If one hour of analysis can generate a 1000% ROI, how can we further improve the cycle time of the insights generated by our analysis?
Digital Assistants. Dashboards are evolving or perhaps virtually reincarnating as digital assistants. Better than dashboards competing with AI, the two are naturally capable of converging just like the computer converged with telephony and television.
Ray Kurzweil, a director of engineering for Google, cofounder of Singularity, and author of several books including the Spiritual Age of Machines, predicts computers will achieve human-level intelligence by 2029. Most of his predictions have been accurate.
We’re still a few years away, but already AI and machine learning algorithms are much better at pattern recognition than we are. Some smart chatbots can converse on a level that’s nearly indistinguishable from chatting with a human. Functionally, digital assistants are already at a point where you can practically click for insights.
| Static | Dynamic | Digital Assistants | |
|---|---|---|---|
| Data and Reports | Specific data for a fixed time. | Can change context and time frames. | Users ask questions and receive easy-to-understand answers with supporting visuals (charts/graphs). “Insights On-Demand” |
| Dashboards | Shows the same data to all users. | Each user can customize, interact with, and change their reports as needed. |
Data’s come a long, long way. By the time you find perfection, your standards for it will have changed. Dashboard capabilities are changing even as I write this, but it’s worth comparing the capabilities of what we’ve been using to what digital assistants add for us:
| The Typical Dynamic Dashboard | Digital Assistants | |
|---|---|---|
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Business and software development metrics are almost all interconnected in some way. In software development, defect rate is just one of the possible metrics to track for all developers. How many developers are in your team? Maybe you’re managing multiple teams? If so, then you’re also interested in training your team leads.
Remember at the start, we were looking at an engineering case for seeing over 1,000% ROI by spending an hour to help Samantha reduce her defect rate. Her defect rate could be due to her learning a new language, code complexity, maybe poor tests, there could be other reasons, too.
With a digital assistant, the hour an engineering manager spends finding insights for their developers might take 3-5 minutes. Digital assistants also set the basis for augmented teams – and no, we’re not talking about hiring or outsourcing more developers.
A few examples include:
The sum benefit exceeds the value of the individual parts, like compounding interest.
Ultimately, AI-Driven Dashboards – our New Digital Assistants, will provide BI Managers, Engineering Managers, and Analysts with faster insights to help 10x their business and development team’s 10x’ing.
