A geeky blog about digital thinking, digital doing and technology

I’ve being asked for my view on AI a lot this year, more so than last. It would appear the industry has caught up with the ‘hype’ being a reality.

I’m not getting the slightly twitchy ’Is it going to steal my job?’ anymore though, now the questions are; Is this something I need to be bothered about?, Can I afford it?, Where do I start?

All good questions. If you’re a brand or business looking to get ahead, simply keep up, or maybe even catch up, you can’t afford not to be thinking about this and getting a beta into place. If you don’t your competition will, and then you will be sat watching them eat your share of market or voice, or both. Either way, I can’t imagine that’s something you’re keen to see happen.

Roughly a third of the brands I work across either have a version of an AI (artificial Intelligence) ready IA (Intelligent Assistant) or have jumped straight into an AI trial or beta. Every single one of them has seen positive results. Every single one of them is now developing a roadmap with us to put in place milestones to be better, faster and more informed on a real time basis.

The shape of these solutions vary from bettering service response levels to informing fashion design and everything in-between. But the steps to get there are the same, and here they are;

Work out what the problem is you want to solve

Ok, obvious right? But actually I highlight this because I recommend you don’t ‘do a chatbot’ because your competitor did.

Is there a challenge that advertising or marketing isn’t fixing for you right now? Do you have a human centered design idea that you can’t quite get to grips with? Do you have micro communities you don’t understand or can’t reach in meaningful ways?

All of these are problems AI can help you with, quickly and effectively. So consider where you might want to turbo charge a solution and put a brief together around that.  Be clear about your brief as well, if you’re vague about what you want to achieve it’s tricky to train an AI to think comprehensively, it in turn will be vague.

Review and understand ALL of your relevant data

AI is only ever as good as the data you feed it; the more data you have, the more connections can be compiled and the faster it will evaluate and learn. It’s not magic, it’s algorithm on speed. 

Define the goals you want to achieve in order to reach the objective in your brief, or work with an AI data partner to do this (most good agencies should have someone who can help you get started and then find the right partner for you, the answer isn’t always ‘Watson’ btw). You will likely have a mass of data you understand and a bank of data you’ve never really thought about, once you have it all in one place you need to work out where the gaps are and fill them in.

This up front bit seems tedious, that’s because it is. But don’t cut corners as you’ll only pay for it further down the line. The better the data set, the more robust your AI solution will be and the quicker you will see results.

Choose your AI partner

What you want your AI to do will depend on what supplier or partner you choose. There are many solutions already available at both scale up and enterprise level to choose from. They offer everything from; language skills, analytics, tech stacks that speed up services, listening, finding ‘moments of serendipity’ through to predictive analytics and forecasting.

A read of IBM Watson and AWS are good places to start if you want to dig more into what’s on offer, but also check out the likes of DigitalGenius and DeepMind for something smaller or a bit more creative.

Of course you may be looking to create something truly bespoke in which case you may have to hire a bunch of experts to create your algorithm from scratch, or seek a start up willing to work with you and co-create. There are an abundance of really cool start ups just about to break on to the scene so this is a truly valid and cost effective approach, don’t rule it out.

That’s it. From here, you should be in safe hands. You know what you want, you have the data in play to get it and a partner who knows what to do with the data to get what you want.

My parting piece of advice is to remember that AI / ML (Machine Learning) are solutions that learn and develop, think of it as a child going from kindergarten to PHD level but in weeks rather than years. There may be a few mistakes along the way but be patient and think big, because with direction and correction the results are nothing short of impressive.

And it’s not just customer service stuff either…

Marchesa and Watson’s cognitive dress – read more here

P.S. Here’s a mini wiki;

IA; Generally speaking an Intelligent Assistant is a pre-structured agent used to deliver automated responses but does not include self correction or ‘learning’, therefore is not always classed as AI. It’s often the step before AI and used to validate the quality of data. That said some do include NLP (Natural Language Processing) and are connected to the IoT (Internet of Things) so the line is often blurred.

NLP; Natural language Processing is a computer science that uses AI and handles human speech between computers and humans.

AI; Artificial Intelligence is an intelligent or cognitive behavior exhibited by machines, sometimes also referred to as problem solving or learning.

ML; Machine Learning is a sub-field of AI that includes programming computers to deepen the learning process.

P.P.S. If you find AI interesting generally you might want to check out my other blog, co-written with @kayperbeats – it’s a bit more off the wall but it’s insightful none-the-less.