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.
đȘïž With all this buzz around Artificial Intelligence, creating some clarity in the Midst of Chaos is important.
To do that, weâll take a deeper look into
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.
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,
The rest of the companies are at various planning stages. So if you donât have an AI-based solution yet, donât panic!
To prepare your business for the future of AI, consider these strategies:
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.
đ Define a clear business case for each area to understand if AI is worth further investigating.
For example:
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?
đ What is the problem you can solve with AI?
đ Which metric are you trying to improve?
Whatâs the estimated complexity?
đ Does the company have the necessary resources for the implementation?
đ Does the company need a temporary or permanent solution?
What are the potential benefits?
đ How big is the bottleneck youâre trying to solve?
đ 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.đ
The sky is the limit, right? In some cases, yes!
But before implementing any AI tool, letâs 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 customerâs 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!
OneTrack leverages AI to scan the factory floor for safety risks.
OneTrackâs 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.
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-4âs 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 â participants with lower skills saw a whopping 43% performance boost.
đ Read the full research here for a more in-depth understanding of their findings.
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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:
Accuracy: Accurate data is key when it comes to training AI models. If the data isnât on point, youâre looking at models that are all over the place and predictions that you canât rely on. Thatâs why it's so important to check and clean your data regularly â gotta keep that accuracy in check.
Completeness: Completeness is like the superhero cape for your dataset. You need all the necessary data points to ensure your AI training process isnât tripping over itself. Incomplete data? Thatâs a fast track to biased or half-baked model results. Be the hero â make sure your datasets are packing everything they need.
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 systemâs performance takes a hit. Keep that data in line, and your AI will thank you.
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.
Timeliness: Timeliness is like the freshness factor for your data. You want it to be like todayâs news, not last weekâs leftovers. Outdated info messes with your AI modelâs groove and can lead to less-than-stellar performance. Stay on top of updates and real-time integration â keep that dataset in the now.
Business leaders should prioritise the quality of their data as itâs a must for:
Accurate Insights: A solid data quality foundation ensures that AI algorithms generate trustworthy and accurate insights, providing a reliable basis for decision-making.
Reliable Results: When your data is of high quality, the results churned out by machine learning algorithms are dependable and can be counted on.
Improved Decision Making: Good data quality contributes to making decisions based on accurate and up-to-date information, enhancing the overall decision-making process.
Reduced Waste: With good data quality, resources arenât squandered on collecting, processing, and analysing inaccurate data, leading to a more efficient allocation of resources.
Increased Efficiency: By nipping errors in the bud, better data quality promotes the more efficient use of resources and time, streamlining operations.
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.
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 itâs 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 wonât 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 decisionsâno secrets.
Plus, bad data means no accountability. If decisions are based on garbage, whoâs to blame? So, keep your data game strong, check for biases, and fix errors. Itâs the only way to keep the AI on track.
And hereâs a kicker: bad data limits innovation.
If the information going in is rubbish, the results wonât be groundbreaking. So, clean data is not just about being good; itâs 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! đ
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?
đ 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, letâs continue with the human aspect of AI.
It's important to adjust strategies to different adoption segments throughout the implementation of AI systems.
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:
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:
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:
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:
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:
Youâre 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.
Itâs no longer a far cry into the future, itâs here, available, and ready to be implemented.
So how can you learn more about using AI in business?
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