Artificial intelligence in business. A start-smart guide to successful AI adoption

Introduction: Your AI-driven transformation

Where are you on the AI readiness spectrum?

What are the benefits of AI Sprint?

The biggest challenges of AI implementation projects

Start smart

Whether we want it or not (honestly, we probably do), we are surrounded by AI.

You wake up in the morning, get yourself a cup of perfectly brewed coffee, and
check your email. Spam-free email, of course, your Gmail (or another provider) has
mastered clearing your inbox of unwanted messages. Then you go to check your
newsfeed. Personalized, even if it looks like it’s just the way it is. Oops, you’ll be late
for your meeting! You quickly order an Uber and your driver arrives in 5. The app
selects the optimal route to get you to the office asap. On your way, you get an email
from your assistant that asks whether you can squeeze in another meeting at 3. You
can, so you click the “Sure” automatic reply button, and it’s done. Oh, and you just
remembered you’re out of toothpaste, so you order it online. Suggested for you is
also a shampoo, that you (coincidentally) also need but forgot about it. You get it
too. And while you’re shopping online, you’re listening to your personalized playlist
on Spotify.

All that looks so simple, and it’s just everyday life. But there’s AI in it: filtering spam,
automatic response, personalized content, Uber’s pricing and estimated arrival time,
calculating the quickest route to your destination, personalized articles, playlists,
and ads – all that’s achieved with AI. Even Googling something includes an element
of AI. It really is a tool that can be used to make our lives easier – and to tempt us to
use these services! Bbut is it also something you need (and want) in your business?

The journey towards AI adoption doesn’t start with AI at all. AI is a tool, and just like
any other technology should only be used where applicable – where it can bring value. Practical AI is there to support operations, help achieve business objectives.
So it doesn’t start with AI. It starts with your very own digital transformation.

See, the whole buzz that surrounds artificial intelligence has also caused it to be a
technology everyone wants a piece of. The save-all, magical fairy dust of AI can be
sprinkled on any part of your business, et voila, all’s well, and everyone’s rich. Sure, I
am exaggerating now – but it’s true that people tend to have insufficient knowledge
of AI’s potential and limitations, and they have unrealistic expectations.

When you’re planning to start with AI, getting to know your “why” is a good
place to start. Why did you come up with this idea? Why AI? Why is it good for your
business?

These are only seemingly simple questions – in reality, the hype surrounding artificial
intelligence blurs the lines between reality and fiction, preventing business leaders
from getting a clear picture of what AI can help them achieve and how exactly it can
be used in their organizations.

There’s nothing wrong with starting with an “I want AI” mindset, though. The process
will require you to figure out why and how you want to do it anyway. Since many of
our clients face a similar challenge – they’re not sure how and where to adopt AI to
make it successful – we’re sharing our approach towards identifying the room for AI
in your product – the exact place where adopting AI will bring the biggest tangible
results.

To start, we’ll need to focus on your business: discuss the existing processes, list
pain points, identify goals to be achieved. Let’s have a look at an example.

Meet Derek. Derek is an innovation manager at a big retail company that sells
high-end office furniture. He was given a task to optimize the sales process in the
company – to make it more efficient and turn more leads into deals. What a task.

Knowing what we want to achieve (there’s our “why”!), we can dig into the process
itself. It is clearly inefficient – even though the sales department reaches a lot of
prospects, there isn’t much interest in the products. The strategy includes cold calling
and mailing – and these work best (still, not well enough). The process starts with assistants shortlisting companies to contact – then these lists go to the call center where
representatives contact the prospects, and if a prospect shows interest, another list
is passed to the next team who divides the leads among the team members and
contact them to schedule a presentation. It’s a time-consuming process and one
that’s error-prone since the lists change and travel across different teams.

Derek thinks that at this point, after having contacted thousands of companies, they
should be able to score leads more effectively to shorten the path a customer has to
go from initial contact to deal made. Making this path shorter will not only save time
of the sales team, but will also give the customers a better experience – they don’t
have to be contacted on numerous occasions and by different people.

In this case, Derek needs accurate assessments of how “hot” a lead is to know who
to contact first.

Let’s have a look at one more example.

John owns a construction store that sells concrete, plaster finish, tools. The company’s
main focus, best-selling product, is concrete. John’s customers include construction
companies, contractors, and individuals, and he maintains positive relationships
with the companies that purchase his products. These companies generate about
80% of the store’s revenue, and they purchase significant amounts of concrete every
3 weeks or so.

Now, there’s a catch: John needs to send the products from his warehouses the next
day. Often, that poses no problems, but when a big order comes in, managing that
gets tough. There aren’t enough trucks and drivers to distribute the orders on time.
So next-day delivery is sometimes impossible. In such situations, John has to extend
the delivery time and deliver products within 2-3 working days. While it’s often not
a big deal for the first time, if the situation keeps happening, customers leave. John
has already lost 4 important customers, and his main customer base is now only 2
companies. Since these 2 companies generate 80% of the store’s revenue, if John had
kept the other 4 customers, the store would have generated 3 times more revenue.

Clearly, the delay in the delivery of products is an issue that’s critical to John’s
customers and leaves room for improvement. So what are the options to help solve
this problem?

John was considering growing his truck fleet – however, that solution will not be
efficient since higher demand is not regular. However, if John knew that a bigger
order would come in soon, he’d delay the delivery of a small order to serve his major
customers first. Or he could contract out the delivery if he were properly prepared.

In this case, John needs accurate predictions of when bigger deals are about to
happen. With this knowledge, he’d be able to manage his resources in a more
efficient way to prevent delays and serve all customers as soon as possible. John’s
company would need information about an upcoming bigger deal at least a day in
advance – and this would give him enough time to plan for his deliveries.

Makes sense, right? And, as you can see, there isn’t a lot of AI in this whole process
– not yet.

When you want to implement artificial intelligence, you have to do some work on
your own. It doesn’t start with hiring a top data scientist or getting an AI degree. You
first start with a vision.

Don’t be fooled, though, we’re still not doing any magic, and it’s not the Aladdin
wishful thinking kind of vision – it’s a vision backed by strategy and business objectives.

So, number 1 on your getting started with AI to-do list is: VISION. This means
that you should know what you want AI to do. It’s a very general concept at this
stage. You may know that you want AI to increase sales, but you don’t have to dive
into technical nuances just yet. You got it, let’s move on.

Step number 2: decision-making. It’s common for companies starting with AI to
scatter the decision-making responsibility across different individuals or even departments. That’s because they’re trying something new, they may not know what
to expect, and they want to be in this together. While a good team is of great value
in any project, the decision-making power has to be assigned to one person. Is it you
who calls the shots? Is it a manager, an expert from a given department? Make sure
there is a person who’s in charge of making decisions, and make them a part of the
process.

That’s step 3: identifying the use case. However, if you don’t want to have extensive
knowledge of artificial intelligence, there’s nothing wrong with that. When you’ve got
the vision – the idea of what you want AI in your business to be – then you know
what information to look for. If you want to recommend products, you don’t have
to look into chatbots. Clearly, you prioritize: you choose what matters and what
doesn’t and that gets you to step number 4 – the objective.

It’s time to do some reality checks – that’s your step 5. Ask yourself difficult
questions and look for answers. Don’t lie to yourself – if you don’t have data, you
won’t create it overnight, if you lack the know-how, you won’t wake up AI-smart the
next morning.

What do you have to know? You can find the questions you should ask yourself in
chapter 2 “AI readiness spectrum”.

To sum up, even before you start assembling your AI team, there are some general
steps you should follow:

• Have a general vision of AI in your company
• Know who’s the decision-maker
• List some use case(s)
• Define the objective along with quick wins, success and failure criteria, and
relevant metrics
• Do some reality checks of the current state of your AI readiness
• Make a plan for bridging the gaps found during the reality check