Ciphix makes use of a range of technologies: Robotic Process Automation, Conversational Automation, Artificial Intelligence. And with good reason. Choosing the right tech for the right task is a big part of what we do: what matters is a successful outcome for you.

But some technologies are harder to understand than others – which means they’re harder to sell to your team internally. (People need proof before they commit.) AI, a vast and fast-developing field, is one of the harder ones – which is why when we engage a new customer, we like to start with a small-scale first step: a PoC, or Proof of Concept.

Let’s see how a PoC for AI in your organization can grow from a single idea – and from there, scale up with your needs without the risks of going all-in on Day 1.

Defining the differences

Let’s start with a definition. Many automated processes happen “lights off”: the machine doing it (whether an app on the web or an industrial robot cutting metal) is simply following a set of rules. An AI software robot is cognitive, “sensing” the data around it and taking actions based on what it discovers. Think of an animal in the wild, eyes and ears seeking its next meal. 

Cognition carries a key advantage: it works with unstructured data. A blind-and-deaf hamster can survive in a cage, because the feeding bowl’s always in the same place: it’s structured. But put him in the forest, and he’ll have far more problems, because the food’s in odd places and some of it is moving around: it’s an unstructured environment.

The key point: there’s a lot more unstructured than structured data out there. From phone conversations to handwritten documents to images and video, most datasets can’t be slotted into set formats. The real world is messy. And since your business operates in the real world, so is most of your data.

The good news? That’s an opportunity. 

Pick a dataset, any dataset

Think of a dataset in your business that might be valuable, if only you had the time to sort through it properly. Perhaps that folder of feedback from customers. Perhaps a terabyte of recorded Customer Service calls. Or a scanned set of engineers’ reports on paper. There’s all sorts of insights in such datasets – if you know how to look.

An AI software robot can do that looking for you, seeing patterns and insights in the murky mess and surfacing them in understandable format. It’s not following a script – it’s making value judgements based on criteria you think are important.

Finding correlations, surfacing connections

If you want to know why you’re attracting so few qualified candidates for your jobs, could the answer lie in the design of your application form? If similar phrases keep appearing in customer feedback, does that mean there’s an unresolved process issue? If shopping carts keep being abandoned after an hour, are customers finding it difficult to bulk-buy from you? 

AI can shake out the problem – and how you can solve it. What’s more, that same software robot can learn as it roams your data. And from there flows business advantage. How one set of customers differs from another, and what motivates each. A feature of your corporate personality every customer likes you for, but you’d never realized was an advantage. A blip in the sales cycle that suggests market turbulence is on the way. AI can uncover such insights and inferences, and predict the landscape ahead, with all its potential, opportunities, and risks. 

Finding business value in unexpected places

That’s the difference AI brings: it adds a “digital workforce” not of dumb rules-followers, but of learners, decision-makers, action-takers. Entities with a broader range of freedoms and the ability to change as they discover information.

That’s the proof you need to demonstrate the value of AI. And here’s the kicker: unlike an idea sketched on paper, that PoC is already proven to work. Because it was developed in the real world, in the context of your actual business systems and information. Which gives you the justification for adopting AI, before rolling it out enterprise-wide.

CONCLUSION

Start small, then aim for scale

Best of all, there’s little risk in getting started. Ciphix can help define the scope for a small first project, with what we call an AI X-ray: searching your business for areas where AI could add value – often a small-scale project first. Then, once we’ve proven that first chunk of value, the real fun begins: scaling the wins across your organization, and looking for more.

Why not let us prove the concept to you? Contact us now!