Why the first strategic stage in any AI implementation should be SaaS.
There have always been divided opinions when it comes to AI. Amidst the tropes and the fear, businesses often hype the technology as a magic wand with the power to grant rapid transformation. However, in manufacturing specifically, although 60% of companies are aware of the benefits that AI could bring to their business, only 23% are currently using it.
Makes you wonder, if most of us know what AI could achieve and we’re all hugely excited about it, why aren’t all businesses using iThere area’s a few reasons. . AI is a capability not a quick-fix, paper over the cracks product. Adopting AI and Machine Learning applications takes a whole shift your people’s behaviour and a new administration style.
It is a process, and there are a few stages in between. Stages which must be deployed absolutely correctly, otherwise the machines are rising up and judgement day is on the cards (joking).
SaaS = Software-as-a-Service = Not as confusing as it sounds
I’m going to explain SaaS in a very simple way because I don’t think many people do. SaaS is your gmail account, it’s Microsoft Teams, it’s Slack. You can access any one of those platforms on any computer or device, at any time of day as long as you have a good internet connection.
Let’s take gmail. Not only will you be able to connect with it, it’ll be the same wherever you look at it. Same contacts and the same neverending inbox. Google also makes sure it's always working (most of the time). You don’t need to maintain it, you don’t need to update it - apart from a few very annoying design features (am I right?) gmail is perfectly set up for personal and professional emailing.
Now think about how gmail has changed recently - and for the better? Have you noticed that you can hit the tab key and it will instantly type up your usual email sign off - a simple kind regards or a ‘KR’ if you go in for that. Have you noticed it sometimes tells you not to trust emails from an unknown or new sender?
This isn’t a coincidence. Google has recently rolled out a roster of smart tools thanks to AI, designed to make composing email comparatively faster and more secure than ever before.
This graduation of tools and capabilities is a classic example of SaaS paving the way for AI - specifically a Machine Learning application of AI. And how has Google achieved this? By learning about you and your data on a scale never seen before.
The all important data
The very first version of what would become known as email was invented in 1965 at MIT. The system allowed select users to share files and messages on a central disk, logging in from remote terminals. This central disk would have been the size of a grain of sand compared to the cloud we have to play with now. All the system could do was log the messages, it couldn’t provide or store any data which could give you insights into how the email was being used, when it was being used.
But it’s modern cousin has lots of opportunities and an endless memory to gather data on an extraordinary scale.
This is what SaaS does best, it enables you to use this information to provide insight to your customers and about your customers. Within your business, SaaS CRMs like Salesforce and accounting packages like Xero could already be doing this and are often in use within our workplaces. They are harvesting data and intelligence which is enabling your people to make smarter decisions and be more efficient.
If you’re not doing this and there are lots of outdated and unpopular manual processes still in place within your business, this is the first step. If AI is your next big dream, gathering and understanding that data or what that data could do is the foundation.
Making the leap into AI
You may have multiple SaaS packages doing different things for your business or even better, they may be fully integrated. You’re leveraging that aggregated data from different customers and becoming more efficient and maybe more profitable.
But how could your business make the jump like gmail did and roll out a smart tool which could supercharge processes? Think of your CRM, we’ll use that as an example. Imagine if you could harness this platform to provide a predictive tool for your salespeople to calculate a quote based on your customer data and relationship?
This is possible. Through working with data engineers who understand the true meaning of big data, machine learning models can be created with that information. The key is that it’s not just a static tool, this tool will learn, based on how the customer relationship progresses amongst rafts of other considerations.
The possibilities are endless and transformation could be on the horizon if businesses are prepared to make that leap into SaaS or see the power in the SaaS they’re already using on a daily basis. It’s time to catch up.
And a word to the wise, data is a key concern but your people should be your next consideration. None of these tools work unless the humans trust the robots.