AI - Love it or hate it? The reality is it is all around us.
In this article we will explore how it can be used to enhance value in your business exit and I will share some secrets about how I use it to enhance (not write) this article.
Have you ever heard the saying,
"What's worse than finding a worm in your apple?
The answer:
Finding half a worm!"
It's a vivid reminder of how an unpleasant surprise can ruin an otherwise perfect experience.
This metaphor aptly captures the potential pitfalls of planning a business exit without integrating AI.
As business owners, we often focus on the big picture, but sometimes it's the hidden details that make all the difference. Artificial intelligence (AI) is one of those game-changing details.
There are many opinions out there about where it will take us. For me, the better question is:
How fast will I be left behind if I don’t embrace AI?
AI doesn’t have to be complicated, and you are probably already using it without realising it.
For example, if you use Xero as your accounting software, it uses AI for transaction matching amongst other things. This is a time saving operation for your finance staff which frees them up to do higher-value tasks.
Another tool you may use is Grammarly to check your documents and emails. Grammarly also uses AI.
How Technology Has Changed Over Time
I started my career at IBM in the mid-80s, at a time when personal computers were just starting to become more common.
Back then the titles of the people I met, in my role as a Systems Engineer and then a Marketing Representative, were Data Processing Managers. Over time as the technology evolved so did their titles into Information Technology Managers and then, we had Chief Information Officers and Chief Technology Officers. *
Since the 1980s computers and technology have developed to the point where we can barely imagine running a business without them. And can you name a business that runs today and does use a computer, technology or smart phone?
And today, this is where we find ourselves with AI. We are just at the beginning like we were in the 1980s with computers.
Today, those who get left behind are going to be the ones who don’t embrace AI in some form in their business. And one of the changes we are seeing is having dedicated AI focused teams or staff to keep on top of the ways we can use these tools to make our lives and businesses better.
What is the point of harnessing AI at this point in my business career?
You may well be wondering where AI fits in within the context of a business exit?
The bottom line is the bottom line!
Businesses who embrace AI in a measured and ethical way, are those with a vision, eye on the future and are committed to creating growth going forward. And those are the businesses that command premium pricing in the marketplace.
There are two types of buyers for businesses – those who are looking to get a bargain and those who are looking for a strong, growing business.
Which kind of buyer do you want to attract?
If you want to attract a buyer who will pay for a good solid business then I want to introduce you considering embracing AI in your business, whether you plan to exit or continue in your business.
Which begs the next question:
How can I use AI in my business?
I'd like to demonstrate by way of this article one way you can use AI very simply - not to create massive amounts of meaningless content - instead to use it with intelligence to enhance the experience for everyone.
I am going for total transparency here as I share how I created this article with the assistance of AI.
Example of Using AI in Business
Using AI to Enhance Content - How I created this article with the assistance of Artificial Intelligence.
Firstly, I asked ChatGPT for an outline for a series of posts on AI for business exits.
We brainstormed back and forth and during that brainstorm I remembered the saying about What's worse than finding a worm in your apple? Answer: Finding half a worm! Something I remember from a real-life encounter with a worm in an apple from our garden! I then introduced this into our brainstorming, and we fleshed out the idea.
ChatGPT writes somewhat how I write because I have taught it to - I will share how I do that in another article. Training ChatGPT takes a little time, patience and lots of iterations.
I take the rough outline created through my dialogue with the ChatGPT and I amend, adjust and add in my nuances.
I also asked ChatGPT to create me an image to go with this article – the one that you see is the first version. I asked for other versions and in the end, this was the version I liked the most.
And then when I finish writing this (because all of the last 10 plus paragraphs have been 100% written by me with zero AI input – I will ask ChatGPT to proof read, check grammar, spelling (in Australian English) and then I will read again to ensure nothing has been lost.
There’s a paragraph I wrote from my memory that I put an * at the end so you can go back and have a look at how it reads. I have not altered it deliberately even though I now see perhaps I could have made it better! In this next section I will show the power of this tool that in seconds created the information you see below.
Using an AI Like ChatGPT to Verify Information
I want to illustrate some of the other ways I use ChatGPT for example verifying information in a format that I want that may take me a long time to track down and collate.
To verify my memory of the titles of computer professionals I asked ChatGPT:
ChatGPT 4o Prompt
“Can you give me a rundown of the types of titles of business managers whose job was to look after computers starting in the 1980s and their evolution over time to today.”
[Notice how my English doesn’t need to be perfectly structured and my grammar correct – it gets it!]
Click below to reveal the response
“The titles for business managers responsible for computers and IT have evolved significantly since the 1980s, reflecting changes in technology, business needs, and organisational structures. Here’s a rundown of their evolution over time:
1980s:
- Data Processing Manager: Focused on managing mainframe computers and large data processing operations.
- MIS Manager (Management Information Systems Manager): Responsible for overseeing the management information systems within an organisation.
- Computer Operations Manager: Managed computer operations, including hardware maintenance and data processing.
1990s:
- IT Manager (Information Technology Manager): Emerged as businesses adopted personal computers and networked environments.
- Network Manager: Focused on managing network infrastructure and ensuring connectivity.
- Systems Manager: Responsible for overseeing systems operations and maintenance.
- Database Manager: Managed databases, focusing on data storage, retrieval, and management.
2000s:
- Chief Information Officer (CIO): Senior executive role responsible for the overall technology strategy and direction.
- IT Director: Oversaw the IT department, reporting to the CIO or other senior management.
- IT Project Manager: Managed specific IT projects, including implementation and upgrades.
- Network Administrator: Focused on maintaining and supporting network infrastructure.
- Systems Administrator: Responsible for managing and configuring computer systems and servers.
2010s:
- Chief Technology Officer (CTO): Focused on the technological innovation and development aspect of the business.
- IT Operations Manager: Managed daily IT operations, ensuring systems and networks ran smoothly.
- Information Security Manager: Responsible for protecting the organisation’s data and IT assets.
- IT Service Manager: Managed IT service delivery, often using frameworks like ITIL (Information Technology Infrastructure Library).
- Cloud Services Manager: Managed cloud computing resources and services as businesses began migrating to cloud platforms.
2020s:
- Chief Digital Officer (CDO): Focused on driving digital transformation and integrating digital technologies into business operations.
- Data Officer/Data Manager: Managed data governance, data quality, and data analytics initiatives.
- Cybersecurity Manager: Focused on protecting against cyber threats and ensuring cybersecurity resilience.
- DevOps Manager: Managed the integration of development and operations to improve collaboration and productivity in IT and software development.
- AI and Machine Learning Manager: Oversaw the development and deployment of AI and machine learning projects.
- IT Strategy Manager: Developed and implemented IT strategies aligned with business goals.
- Cloud Infrastructure Manager: Focused on the management and optimisation of cloud infrastructure and services.
- IT Governance Manager: Ensured that IT practices aligned with regulatory requirements and corporate policies.
This progression reflects the growing complexity and strategic importance of IT in business operations, moving from basic data processing roles to highly specialised and strategic leadership positions.”
And as a comparison here’s the answer for AI related positions in businesses.
ChatGPT 4o Prompt
“What are some of the evolution of titles for people in charge of AI in businesses?”
Click below to reveal the response
“The titles for professionals in charge of AI within businesses have evolved as the technology has advanced and become more integral to business operations. Here’s a rundown of how these titles have evolved over time:
Early 2000s:
- Data Scientist: One of the earliest roles focused on extracting insights from data using statistical methods and machine learning.
- Machine Learning Engineer: Specialized in developing algorithms and models that enable machines to learn and make predictions.
2010s:
- Chief Data Scientist: A senior role focused on leading data science initiatives and aligning them with business goals.
- AI Research Scientist: Conducted advanced research in AI and machine learning to develop new algorithms and technologies.
- Data Engineer: Focused on building and maintaining the infrastructure required for data generation, storage, and processing.
- AI Engineer: Similar to a machine learning engineer but with a broader focus on implementing AI solutions.
Mid to Late 2010s:
- Chief AI Officer (CAIO): A senior executive role responsible for overseeing AI strategy and implementation across the organisation.
- Head of AI: Led AI initiatives within a company, often reporting to the CTO or CIO.
- AI Product Manager: Managed AI-driven products, focusing on the development and integration of AI technologies into business solutions.
- AI Solutions Architect: Designed and implemented AI solutions tailored to meet specific business needs.
- Deep Learning Engineer: Specialized in developing deep learning models and architectures for complex tasks such as image and speech recognition.
2020s:
- Chief Analytics Officer (CAO): A broader role encompassing AI, data analytics, and data science, focusing on leveraging data for strategic decisions.
- AI Strategist: Developed and executed AI strategies to drive business transformation and innovation.
- AI Operations Manager: Focused on the operational aspects of AI projects, ensuring smooth deployment and maintenance.
- AI Ethics Officer: Ensured that AI applications and developments adhered to ethical guidelines and regulatory standards.
- Director of AI: Led the AI department, overseeing the development and implementation of AI technologies within the business.
- AI Policy Manager: Focused on developing policies and frameworks to guide the ethical and effective use of AI in the organisation.
- Conversational AI Designer: Specialized in designing and developing AI-driven conversational interfaces like chatbots and virtual assistants.
- AI Innovation Manager: Led innovation initiatives involving AI, exploring new ways to apply AI technologies to business problems.
- Machine Learning Operations (MLOps) Engineer: Combined machine learning and DevOps skills to streamline the deployment and management of machine learning models.
- Natural Language Processing (NLP) Engineer: Focused on developing AI systems that understand and process human language.
These titles reflect the increasing specialisation and importance of AI in business strategy, moving from general data science roles to highly specialized positions focused on various aspects of AI technology and its ethical implications.”
Wrapping it Up
AI isn't just for tech giants – it’s for all businesses regardless of industry and size. Embracing AI means staying ahead of the curve in your industry – it could mean the difference between growing and dying in your market.
I wanted to use this article as a way of demonstrating what tools like ChatGPT can do and how they can impact your business now to enhance your business exit.
With AI
Just imagine: with AI, you can automate mundane tasks, freeing up your time to focus on high-level strategic decisions. You can gain deeper insights into your business performance, identify growth opportunities, and improve your market positioning.
Without AI
On the flip side, ignoring AI could mean missing out on these advantages, leaving your business vulnerable to competitors who are eager to capitalise on your oversight.
This article was not written by ChatGPT and I want to make that distinction very strongly. Instead, it was enhanced by using ChatGPT 4o. The image was creating using DALL-E through ChatGPT 4o based on a prompt I wrote - so it was co-created.
Questions are one of my favourite things, and so I challenge you with these two:
How are you using/going to use AI in your business?
And whether you do or you don't use AI now - what's your next move?
Look out for more articles about specific uses of AI – so stay tuned and subscribe to our fortnightly newsletter to keep up to date and so you don't miss the things we share there, that we don’t always share publicly.