Artificial intelligence (AI) – using computers to create systems capable of performing tasks typically requiring human intelligence such as recognising images, generating text or holding a conversation –  is on the cusp of becoming part of mainstream business. Larger firms in particular are already testing the technology en masse and deploying AI to improve decision-making and productivity across many business functions.

In a survey of over 500 business leaders Peak Indicators found one in eight UK businesses are now widely using AI insights or automation, while more than a quarter are investigating how it could support their activities in the future.

So how do you move from AI talk to implementing AI within your business?

Identify ‘quick wins’

There are a huge number of core business processes, even within small enterprises, that could be improved using AI, for example in sales forecasting or online customer support.

A good approach is to identify small, targeted AI pilot projects that solve clearly defined business issues or needs, that can quickly realise their business benefit and (if successful) could lead to further investment.

A key part of this is engaging with users from around the business to understand where and how AI could provide a ‘quick win’. Often, they’ll point to rudimentary and repetitive tasks that can be quick and easy to automate while boosting your business capability.

Utilise cloud technology



Implementing AI technology doesn’t need to be a multi-million pound project or require an extensive, expensive IT team. Cloud computing and services has democratised AI and high performance computing, making it accessible to the smallest of businesses.

Cloud computing also provides more cost effective use of resources, ending the need to invest in expensive tools that aren’t fully utilised across the entire enterprise.

Resource and upskill

While it’s true that larger businesses have the capability to employ large teams of data specialists and equip them with the best tools, recent developments have levelled the playing field somewhat. You might consider upskilling existing staff using online learning resources (many of them free) and low- or no-code AI development products and services.

Many smaller firms are using off-the-shelf AI and software packages with embedded AI to automate low-value tasks without the large price-tag.

For larger projects, you might want to collaborate with vendors who can provide solutions with short-term return on investment. A dedicated vendor can build solutions that are flexible and can advise on effective use, and end-user training.

Good data in, good data out

Perhaps the most important step towards successful AI implementation, is developing good data governance procedures within your business. Data quality is key here. The focus must be on investing in making key data sets as complete, coherent and unbiased as possible so that decisions made on the outputs of AI tools are as accurate as possible.  As you look to become more data-driven, effective governance of data will help to develop trust within your business and help you make the most of the data you have.

Targets and signposts
Even if you do aim to make small, focused developments, AI deployment is not an overnight one-off process. Hurdles will appear in the form of technological, equipment, and cultural challenges and a lack of clearly defined end-goals can be fatal – derailing projects that showed early promise.

This makes signposting your progress critical to success. A vague timeline combined with challenges can breed uncertainty. By understanding what a successful project looks like before you begin and coming up with suitable success metrics your business can avoid this pitfall and progress smoothly towards its goal.


By Paul Clough


Paul Clough is Head of Data Science and AI at data consultancy Peak Indicators and a Professor of Search and Analytics at the University of Sheffield’s Information School.

He has also recently been appointed as an Ethics Board Member and consultant for the Machine Intelligence Garage, which offers promising early stage machine intelligence companies access to expertise and support on applied AI ethics.



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