5 Ways To Promote the Use of AI and Chatbots in Your Business

bot, chatbot, rpa, ai, promotion

The term artificial intelligence conjures up all sorts of images at different levels of a workforce and across industries. Preparing to deploy an AI chatbot or a smart service, iRPA or other effort requires education and careful planning and promotion to overcome any resistance and boost understanding and support.

Growth in the adoption of AI across every type of business is inevitable. However, acceptance will be uneven, even hostile, depending on the organisation and its traditions. Some companies are born with nothing but the latest technology, others are slowly transitioning from traditional working practices to that of a digital business.

Within each company, there are leaders that will pro- or anti-AI, depending on their previous experiences, or what they have read. While team managers and workers see AI through a variety of different lenses:

  • Their own experiences of poor/good AI services.
  • From tabloid headlines and sci-fi TV/movies.
  • Business reports focused solely on efficiency and rationality.
  • Previous technology “innovations” in the workplace.

This is nothing new, and most businesses will have been through these steps before. But with AI comes a barrage of new ideas and ways of working that can have a huge impact on an organisation. Getting it wrong with first efforts could have a serious impact on the business in future, and risk leaving it being left behind by rivals who are smarter at adopting AI and using it more efficiently.

Five quick ways to defuse an AI argument or opposition

There are plenty of ways to reduce the risk of AI technology-based tension in any business, especially as AI services arrive in growing numbers and forms.

Call it machine or deep learning instead

Artificial intelligence is a complex area and in many cases, vendors misuse the term to make their products sound sexier and more cutting-edge. In fact, most vendors should really be using the phrase machine learning rather than AI, as should businesses adopting the technology. This simplification would reduce the threatening overtones of AI and set a more grounded basis for the technology.

If you are going further with true AI, calling it deep learning or advanced analytics can remove the stigma over AI from the conversation and help focus on what the technology does.

Start with an internal pilot

Before unleashing AI on your customer base or the whole workforce, build a pilot project, clearly explaining to everyone involved what the aims are, what it does (and doesn’t do). From a simple robotic process automation task that replaces some unloved job to a chat for reception or HR that answers some basic questions staff are fed up of responding to. This demonstrates both the technology benefits and the ease or complexity of such projects across the business, reducing resistance to further projects.

Highlight successful examples

You should never launch an AI project just because a rival has one, but if your own business case justifies one, feel free to point out the many successful examples of chatbots and AI tools out there.

In the early days, hard data was tough to come by, due to the tentative nature of the projects and the need to maintain a competitive advantage. But now more businesses are happy to highlight the success they have had, and the efforts they made to make it a success. Showing these to business leaders, those with concerns and those who will work on projects should bring a can-do air to the teams, and demonstrate that AI has moved on from its test/beta status and there is nothing to fear for those stepping into uncharted territory with their technology adoption.

Find where to RPA chatbots

If you found success with a trial, then scour the business for processes and roles where RPA chatbot tools would benefit the worker and business. Build a list of suitable targets and establish what type (or smartness) of AI tool will be required to improve the efficiency of the target.

Build the list and order it by priority, value or return on investment and start adding AI tools or services that have the most impact first to demonstrate and reinforce their value. Whatever the market, there are vertical or industry specific tools already available for many roles and processes, or you can build your own speedily using code-free or cloud development tools.

Talk about AI in a non-technical way

In any discussions about AI, or when going about any of the previous suggestions, use basic or layman’s terminology rather than falling for the vendor or consultant jargon. Focus on the needs and benefits of the business, and the results that the AI will bring. Most businesses and workers are now far beyond being impressed by new technology and the barrage of jargon that comes with it.

Natural language processing (NLP), deep learning, neural networks and many other AI terms can and should all be described in simpler ways to demystify them and to help others understand why the company wants to use them.

During these discussions with the board, teams and individual workers, there are no dumb questions. People are discovering these technologies at varying paces. That’s as chatbots and AI themselves evolve from narrow services to broader tools that can perform many tasks and understand wider contexts.

Within a couple of years, every business will likely run multiple AI chatbots and services, or intelligent robotic process automations. Everyone will wonder what all the early fuss was, even as your business bots start talking to other machines in machine-to-machine (M2M) smart business systems using 5G networks and the Internet of Things.

But at their heart will be simple, repeatable processes that anyone in the business should be able to understand. Build these first and the rest will follow naturally, but only with the support of executives, end users and workers within the organisation and among partners or clients. 

, ,