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AI might not be a new technology but whenever C-level management talks about it, it still appears like a wondrous formula that can solve every conceivable business problem. The issue: if you don't know why you need AI in the first place, you'll end up investing in the wrong things.
Tl; dr: To fully leverage artificial intelligence for your business, you need to understand how it works, what it actually can do (and can't do) and which use cases are relevant for your needs and goals.
Content:
As Kamales Lardi writes in her article (Forbes, 2021), many companies define their AI strategy too broadly, by having goals such as "implement AI-based solutions to realize xx% revenue gains". These goals create a sort of blackbox regarding where, what and how AI can actually help gain revenue.
what do you need to use AI for your business?
Know what AI can do (and can't do)
You don't have to be able to build your own quantum computer but it is absolutely necessary to have a grasp on the basics of intelligent technology, the different methodologies and terms and what the differences are between AI, machine learning and deep learning. Only then can you actually see what AI can do for your company, where you can make use of it and where it's not necessary. Additionally, you know which AI trends and technologies can help your business grow.
A basic knowledge gives you a realistic outlook, prevents you from investing in trends that have no actual business use for you and it sharpens your plans with real solutions instead of broad statements.
Implement the basic necessities for AI
One of the biggest hurdles of any AI strategy is lacking data management. For AI to work, you need your data structured and digitalized, otherwise you can have the best solutions you can buy but you still won't be able to use them because you lack the data. This means that you need a centralized, digital data bank that is compatible with your AI solutions.
AI can only ever be as smart as the available data. If your data is on localized, isolated servers, you won't be able to leverage them without additional costs and ressources.
Identify attractive use cases
I can guarantuee you that as you read this article, somehwere a manager will read about a new trend and immediately ask someone to invest in it without any consideration whether it aligns with the business strategy.
Especially with newer technology, businesses need to know how it can actually optimize processes and experiences, enable users or even change business models. Use cases help to identify not only the areas where AI can be useful but also give info on requirements, outcomes and KPI.
Start small and then scale up
Especially complex and/or new technology should be tested in a smaller environment. Pilot projects are ideal to implement AI where it can have the biggest outcomes but also start on a smaller level to reduce risks, train your employees (with or without the help of external service providers), stay agile and develop a framework for the next AI project.
This way, your employees can gain the necessary experience and make mistakes without hurting your business. Additionally, the advantages of AI are immediatelly visible and strengthen the general acceptance at your entire company.
Enable your employees
The industry currently has a high demand when it comes to IT personnel, especially in relation to AI development.
In fact, IT-jobs where among the very few that were continuously in high demand during the pandemic. Companies need experts, especially if they don't just plan singular AI solutions but want to strengthen their business with the power of AI across all units.
Businesses should hire more experts but also shouldn't ignore their existing employees. Invest in training, certifications and use the existing skills to build upon them. This also creates a positive signal to prospective employees, since lifelong company-supported learning is one of the staples of IT experts.
Identify what skills you really need and consider different methods to gain them:
- Internal training
- Nearshore/Offshore
- Consulting/Service providers
- Tech Labs
Read how the financial institute Stadtsparkasse München replaced a complex and time-consuming excel solution with an automated, BI-based and transparent digital solution.
The AI trends to look out for in 2022
So, what's on the horizon when it comes to AI technologies and how can you use them in your business? In the following I'll introduce the biggest trends that are currently shaping the AI market. However, it's important to look at every trend through your unique business perspective and consider whether these
- will have an effect on customer behavior and expectations
- can be used to optimize your existing processes, products and services
- can be used for new products, services and processes
- will have an effect on the market (e.g. competitors use them, they disrupt the market, etc.)
Hyper automation
'Normal' automation happens when you identify a process and automate it. Hyper automation combines different technologies, tools and platforms as well as robotic process automation (RPA), machine learning and artificial intelligence to not just automate single processes but both identify, test and automate all possible business and IT processes.
The AI is basically organizing the evaluation of the automation potential of a business and also implements the automation more or less independently.
AI & Cyber Security
With the rise of attacks on business IT infrastructures and data hacks, the need for Cyber Security is on the rise. Artificial intelligence can offer major opportunities to oversee systems and data flows and identify unusual activities for early preventive measures. Additionally, it can help detect security gaps.
Natural Language Processing (NLP) & conversational AI
How AI can understand (different) languages and respond in a way that it feels natural is one of the most exiciting aspects of AI research and it's also incredibly complex. AI systems have to recognize written and spoken language with numerous variations due to typos, abbreviations, accents, slang and more. Then, they have to identify the intent of the message and react accordingly. NLP is the basis for many AI technologies because if we can't communicate with our smart technology, we can't use it.
IoT and augmented reality
With better high-speed internet options on the horizon, the development of the Internet of Things can fully enter the mainstream. Especially in combination with augmented reality - the overlay of digital information over "the real world" - people can work better and safer with machinery but also interact transparently with the inner workings of products and objects.
A fridge could give information on food that's about to expire, offer up fast recipes for available ingredients or even order basic pantry products when they're running low. All on a screen that might even show its contents but with additional information.
Below: a neat example of the Microsoft HoloLens that visualizes how augmented reality can work.
Forecasting
Especially for businesses but also in science the forecast opportunities of smart systems are simply incredibly. With the right amount and structure, data can be used to give estimates of market changes, individual and general customer behaviors and other information that can help preparing for the future. The most important thing about forecasts, however, is that they need a lot of data and are the most accurate, the closer the event is to the present. Just like the weather, it's nearly impossible to predict the far future since the world is always changing in unexpected ways.
Ethics and white boxes
An ethical approach to developing and implementing AI is crucial to have a trusting relationship without exploiting users or creating discriminatory systems. Ethics need to be implemented by default and not as an afterthought. However, to incorporate ethics into AI, processes need to be more transparent.
Many algorithms function like a black box. This means that we can control the input and we see the output but we don't know what happens inbetween. Explained AI (XAI) are an important part of AI development, creating more transparency and information on decision processes within the AI to detect risks and prevent built-in discrimination, e.g. image recognition software not being able to differentiate between people of color because the AI was only trained with images of White people.
Automate your workflows, strengthen your reporting or optimize your services with AI. DIGITALL helps you to leverage the strengths of modern technology for your specific needs and goals.
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