How to Prepare Your Business for the future of AI

Artificial Intelligence (AI) has become ubiquitous in various industries, moving beyond science fiction and transforming the future of business. To prepare for the future of AI, business leaders should strategically identify areas for AI implementation, considering factors such as efficiency, accuracy, and productivity. Recognising the human aspect of change is crucial in successfully adopting AI within organizations.

 

In the past, only the big corporations could afford Artificial Intelligence systems to solve problems and boost productivity, but that has changed almost overnight. 

 

AI's rapid growth means startups and younger businesses might hop on the tech train before the big guys.

 

🌪️ With all this buzz around Artificial Intelligence, creating some clarity in the Midst of Chaos is important.

 

To do that, we’ll take a deeper look into

1. What is Artificial Intelligence?
2. How to implement AI in your organisation?
3. Identifying areas AI can help improve
4. Asking the right questions
5. Realistic expectations of implementing artificial intelligence
6. Investing in data quality and ethics
7. Consider the human aspect of change

 

 

What is Artificial Intelligence? 

Let's start by defining what Artificial Intelligence means. What is everyone talking about? 

 

Artificial intelligence leverages computers and machines to mimic the problem-solving and decision-making capabilities of the human mind.

 

Artificial intelligence (AI) refers to the simulation of human intelligence processes by computer systems. 

 

These processes include learning, reasoning, and self-correction.

 

With Artificial Intelligence, computers are programmed to learn from data inputs and make decisions based on that learning.

 

This is done by natural language processing, which enables machines to understand and interpret human language.

 

How to implement AI in your organisation?

With so many people talking about the power of Artificial Intelligence and promoting to step in right now, there is a real fear of missing out.

 

Both business leaders and individual employees experience this great fear, leading to the wrong way of implementing AI tools. 

 

And why not, right?

 

Getting rid of repetitive tasks, who doesn't want that? 

 

Although many of these speakers are right that you should jump on the AI bandwagon sooner rather than later, it is important to remember that it's only the beginning of the AI revolution.

 

According to the IBM Global AI Adoption Index 2022,

• 35% of companies reported using AI in their business.
• 42% reported they are exploring AI.

The rest of the companies are at various planning stages. So if you don’t have an AI-based solution yet, don’t panic!

 

 

 

To prepare your business for the future of AI, consider these strategies: 

 

 

 

1. Identifying areas AI can help improve

Before jumping into a full adaptation of AI tools, it is important to take a close look at your business operations and identify areas where AI can be implemented.

 

Think of improving efficiency, accuracy, and productivity. 

 

👆 Define a clear business case for each area to understand if AI is worth further investigating. 

 

For example:

• AI can play a significant role in your marketing team by automating tasks.
  
  
• AI can help marketers create highly personalised content and serve as a sparring partner for generating new campaigns and even creating the start for the messaging.
  
  
• AI can help improve the performance of your marketing campaigns by analysing data and suggesting optimisations based on that data.
• AI can even help redo the entire business model when it's in danger. It's like a super powerful T-shaped marketer at your disposal, 24/7!
  
  
• This process starts with asking the right questions. Let’s take a look at what questions you should ask yourself! 

 

2. Asking the right questions 

There are three categories you need to consider before you can start prioritising your efforts. 

 

This means providing these questions to anyone within your company who is using AI or will use AI at a moment in time. These questions need to be at the forefront of implementing any AI tool. 

 

Is it linked to a business problem?

👉 What is the problem you can solve with AI?

👉 Which metric are you trying to improve?

 

What’s the estimated complexity?

👉 Does the company have the necessary resources for the implementation?

👉 Does the company need a temporary or permanent solution?

 

What are the potential benefits?

👉 How big is the bottleneck you’re trying to solve?

👉 How likely is it that the solution will positively impact the metric?

 

You can plot the answers to these questions onto this matrix to understand where you should put your energy resources at.👇

 

3. Realistic expectations of implementing Artificial Intelligence

The sky is the limit, right? In some cases, yes!

 

But before implementing any AI tool, let’s take a look at some use cases for some realistic expectations. 

 

Vodafone has used AI software to analyse customer interactions in real-time, providing support agents with valuable insights into the customer’s emotions and needs.

 

This enabled the agents to tailor their responses and improve customer satisfaction.

 

Adopting AI in their customer support operations has enhanced personalised customer experience and allowed them to resolve customer issues faster. A huge competitive advantage!

 

• 20% increase in customer support agent productivity
• 15% improvement in customer satisfaction scores.

 

OneTrack leverages AI to scan the factory floor for safety risks.

 

OneTrack’s smart camera system scans the factory space, keeping a close eye on the mess, falling boxes, overfull shelves, or equipment being used incorrectly.

 

If the system detects any safety issue, it sends a direct, quick alert to all of the workers present to fix the issue at hand.

 

• This AI system has reduced labour costs by 15%
• Lowered OSHA incident rates by 75%
• Reduced the number of product damages by 56%!

 

 

Starbucks launched their newest AI feature in 2019 already. Let us introduce you to Deep Brew, the AI brain of Starbucks.

 

Behind the scenes, Deep Brew takes charge of daily stock orders, foresees gear fixes, and forecasts when extra hands are needed. In the shop, for their coffee lovers, it goes an extra mile.

 

Deep Brew can suggest coffees based on weather time and your personal coffee journey at Starbucks.

 

Thanks to Deep Brew, their same-store sales grew by 6%, with nearly 18 million new users worldwide. 

 

 

But ... we must not forget:

 

Between 60-80% of AI projects fail due to unclear objectives. 

 

Source: Forbes - The One Practice That Is Separating The AI Successes From The Failures

 

One of the biggest pitfalls is not having a clear strategy for implementing Artificial Intelligence. This can lead to a lack of direction and wasted resources on ineffective projects.

 

Artificial Intelligence can be expensive to implement, and organisations may not see a return on their investment if the project is not well-planned and executed.

 

Implementing AI requires specialised skills that may not be readily available in-house. So, organisations must invest in hiring or training staff with the necessary expertise. 

 

The Boston Consulting Group conducted a first-of-its-kind experiment on the impact on productivity using ChatGPT. Around 90% of the participants improved their productivity using ChatGPT for ideation and content creation tasks. 

 

However, when ChatGPT was used for business problem-solving, the participants performed 23% worse than those doing the task without GPT-4. 

 

Important note: Creative ideation sits firmly within GPT-4’s current frontier, and business problem-solving does not. So, be careful when using GPT-4 for such tasks. 

 

The baseline proficiency with GPT-4 seems to greatly impact the performance boost. Take the creative task, for example – participants with lower skills saw a whopping 43% performance boost. 

👉 Read the full research here for a more in-depth understanding of their findings. 

 

Want to get your team ready for your AI initiatives?

We’ve launched a brand new AI for business course with 6 modules and 21 hours of learning material for all of your team members.

 

👉 Get in touch with our Senior Learning Consultants for customised learning advice. 

 

4. Investing in Data Quality and Ethics

Data quality plays a crucial role in adopting and developing artificial intelligence systems. AI is trained on data. Poor data quality can ruin your whole project.

 

The quality of your data is determined by various metrics such as:

1. Accuracy: Accurate data is key when it comes to training AI models. If the data isn’t on point, you’re looking at models that are all over the place and predictions that you can’t rely on. That’s why it's so important to check and clean your data regularly – gotta keep that accuracy in check.

2. Completeness: Completeness is like the superhero cape for your dataset. You need all the necessary data points to ensure your AI training process isn’t tripping over itself. Incomplete data? That’s a fast track to biased or half-baked model results. Be the hero – make sure your datasets are packing everything they need.

3. Integrity: Integrity is the cool cat that keeps things consistent. Your data must play by the rules and stay true to its form. If it starts acting up and being inconsistent, your AI system’s performance takes a hit. Keep that data in line, and your AI will thank you.

4. Consistency: Consistency is the glue that holds your data representations together. No one likes a messy dataset. If your data is scattered across different sets, you ask for confusion and errors in your AI training and decision-making. Keep it consistent. Keep it sane.

5. Timeliness: Timeliness is like the freshness factor for your data. You want it to be like today’s news, not last week’s leftovers. Outdated info messes with your AI model’s groove and can lead to less-than-stellar performance. Stay on top of updates and real-time integration – keep that dataset in the now.

 

Business leaders should prioritise the quality of their data as it’s a must for: 

1. Accurate Insights: A solid data quality foundation ensures that AI algorithms generate trustworthy and accurate insights, providing a reliable basis for decision-making.

2. Reliable Results: When your data is of high quality, the results churned out by machine learning algorithms are dependable and can be counted on.

3. Improved Decision Making: Good data quality contributes to making decisions based on accurate and up-to-date information, enhancing the overall decision-making process.

4. Reduced Waste: With good data quality, resources aren’t squandered on collecting, processing, and analysing inaccurate data, leading to a more efficient allocation of resources.

5. Increased Efficiency: By nipping errors in the bud, better data quality promotes the more efficient use of resources and time, streamlining operations.

6. Cost Savings: Investing in improving data quality may require an initial investment, but the long-term payoff is significant cost savings, as accurate data reduces the need for costly corrections and rework.

7. Better Customer Experiences: Companies armed with good data quality gain a clearer understanding of their customers. This understanding, in turn, allows for delivering enhanced customer experiences and building stronger relationships and loyalty.

 

All of this has a direct correlation with the ethics around AI systems. 

 

Bad data messes up AI big time.

 

Imagine an AI diagnosing your health with outdated or messed up records. Not cool, right?

 

So, companies and their business leaders must step up, use reliable data, and keep it accurate and up-to-date.

 

And ethics matter!

 

No sneaky bias in the data and personal info!

 

Handle it with care. Get user consent, be transparent, and keep it private. 

 

This way, AI can help people instead of messing things up.

 

But it’s not just about accuracy.

 

Bad data can mess with predictions and sway public opinion. Scary stuff. So, developers, be upfront about where your data comes from and what you do with it.

 

Trust matters.

 

And if the data is junk, users won’t trust the AI. No one wants a system making important decisions with dodgy info. So, developers, clean up your data act and avoid biases. Make sure the AI is clear about its decisions—no secrets.

 

Plus, bad data means no accountability. If decisions are based on garbage, who’s to blame? So, keep your data game strong, check for biases, and fix errors. It’s the only way to keep the AI on track.

 

And here’s a kicker: bad data limits innovation.

 

If the information going in is rubbish, the results won’t be groundbreaking. So, clean data is not just about being good; it’s about pushing the limits of what AI can do.

 

Oh, and privacy matters too.

 

Mishandled data can lead to legal trouble. So, encrypt, follow privacy rules, and play it safe.

 

Bottom line: Good data is the secret sauce for AI.

 

Keep it clean and ethical. Win-win for everyone! 😀

• Develop ethical guidelines
• Establish an AI ethics committee
• Engrain guidelines in company values

 

 

5. Consider the Human Aspect of Change

Incorporating the human touch into the process of adopting artificial intelligence (AI) within an organisation is paramount for success and business growth. 

 

Sure, AI brings fancy changes, but we need to talk about the folks who'll be working alongside the bots. It's not just about the nuts and bolts; it's about keeping the team on board.

 

Engaging employees, addressing concerns, and fostering a culture of transparency and continuous learning are critical components. Both for the adoption as well as the employee productivity with AI tools. 

 

The introduction of AI often requires new skills and knowledge. Training programmes ensure employees are equipped to work with and alongside AI technologies.

 

Investing in employee development prepares them for the changes and demonstrates a commitment to their growth and future within the organisation.

 

Which core skills do you think are most important to drive AI adoption and success?

👆 We hosted a webinar on "How to prepare your business for the future of AI" and asked the attendees this question (158 responses).

 

Do you want to see the full webinar? Check it out here.

 

But first, let’s continue with the human aspect of AI. 

 

It's important to adjust strategies to different adoption segments throughout the implementation of AI systems.

 

Free up time for innovators to experiment

Innovators are the first group to adopt new technologies.

 

They are risk-takers and are always eager to try new things. Here are some tactics to increase AI adoption among innovators:

 

• Offer early access to new AI applications
• Host hackathons or innovation challenges
• Document case studies and success stories and provide a stage for sharing

 

 

Offering ways for early adopters to share

Early adopters are the second group to adopt new technologies.

 

They are more cautious than innovators but are still open to new ideas.

 

Here are some tactics to increase AI adoption among early adopters:

• Provide detailed technical documentation and tutorials
• Create user communities or forums
• Offer personalized training and support
• Use testimonials and endorsements from early adopters to build trust and credibility

 

Showcasing the value to the early majority

The early majority is the largest group of adopters.

 

They are more risk-averse than early adopters but are willing to try new technologies if they see a clear benefit.

 

Here are some tactics to increase AI adoption among the early majority:

• Offer clear use cases and practical examples of how AI tools can improve productivity or reduce costs
• Provide comprehensive training and support resources to help users overcome any learning curves
• Leverage social proof and endorsements from other companies or organizations that have successfully adopted AI tools
• Focus on user-friendly interfaces and features that are easy to use and intuitive

 

Providing support and help for the late majority

The late majority is more sceptical than the early majority and typically adopts new technologies only after they have been widely adopted.

 

Here are some tactics to increase AI adoption among the late majority: 

• Provide detailed case studies and testimonials that demonstrate the benefits of AI tools in a variety of industries and use cases
• Offer risk-free trials or pilots to help the late majority of users get comfortable with the technology
• Focus on providing outstanding customer service and support to address any concerns or questions

 

Pulling the laggards across the finish line

Laggards are the most sceptical and slowest to adopt new technologies.

 

They may require a lot of convincing and hand-holding to get them on board.

 

Here are some tactics to increase AI adoption among laggards:

• Offer personalised training and support to help laggards understand how to use the tool effectively
• Use case studies and success stories to demonstrate the value of AI tools in practical terms
• Highlight the potential consequences of not adopting AI tools to create a sense of urgency and necessity.

 

 

Want to Learn More About AI for Business?

You’re in the right place! AI is a fascinating field and one that is building tremendous traction across the business landscape.

 

As technology advances, artificial intelligence applications for business are becoming more plausible in everyday practice.

 

AI is being used to save time and increase productivity outputs over many different roles and sectors.

 

It’s no longer a far cry into the future, it’s here, available, and ready to be implemented.

 

So how can you learn more about using AI in business?

 

As a leading educational course provider, we created the most effective AI for business course you can find!

 

This course will help you and your team boost productivity with AI solutions and make data-driven decisions for the future.

 

6 Modules | 24 lessons | 24 videos | 6 tests | 6 exercises

• Module 1: AI and Machine Learning Fundamentals
• Module 2: AI business strategy
• Module 3: GenAI for text: ChatGPT and prompt engineering
• Module 4: GenAI for design: text prompts and visual communication
• Module 5: AI for productivity
• Module 6: AI-powered predictive insights 

👉 Check out our course here