PwC echoes the sentiment, claiming that AI leaders take a holistic approach to AI development and implementation and tackle three business outcomes — i.e., business transformation, systems modernization, and enhanced decision making — all at once.
So, how to incorporate AI into your business processes and join the cohort of artificial intelligence leaders?
To answer this question, we conducted extensive research, talked to the ITRex experts, and examined the projects from our portfolio. Here’s what we learned.
Disclaimer: Innovation for its own sake won’t do your company any good.
Sometimes simpler technologies like robotic process automation (RPA) can handle tasks on par with AI algorithms, and there’s no need to overcomplicate things.
In other cases (think AI-based medical imaging solutions), there might not be enough data for machine learning models to identify malignant tumors in CT scans with great precision.
And occasionally, it takes multi-layer neural networks and months of unattended algorithm training to reduce data center cooling costs by 20%.
Artificial intelligence is not some kind of silver-bullet solution that will magically boost your employees’ productivity and improve your bottom line — not even if your company taps into generative AI development services. Yet, the technology has solid potential to transform your organization.
Companies eyeing AI implementation in business consider various use cases, from mining social data for better customer service to detecting inefficiencies in their supply chains.
On a broader scale, the use of artificial intelligence in business falls to:
This list is not exhaustive as artificial intelligence continues to evolve, fueled by considerable advances in hardware design and cloud computing.
Algorithms that facilitate or take over standalone tasks and entire processes differ in their data sourcing, processing, and interpretation power — and that’s what you need to keep in mind when working on your AI adoption strategy.
Let’s take supervised machine learning, for instance. AI engineers could train algorithms to detect
cats in Instagram posts by feeding them annotated images of our feline friends. When faced with unfamiliar objects, these algorithms fall badly short.
But if we take labeled data out of the ML model training process, we’ll get unsupervised machine learning algorithms that crunch vast amounts of information — again, let’s use cat picks as an example — down to meaningful insights. Unsupervised ML models still require some initial training, though. For instance, we could tell algorithms that a particular database contains images of cats and dogs only and leave it up to the AI to do the math.
If you’re considering AI implementation in your company, you should also be aware of reinforcement learning. This technique involves letting algorithms loose in the wild so that they can propose solutions to business problems and learn from their own mistakes. This type of AI can help summarize long texts or predict stock market trends.
Finally, there are deep neural networks that make intelligent predictions by analyzing labeled and unlabeled data against various parameters. Deep learning has found its way into modern natural language processing (NLP) and computer vision (CV) solutions, such as voice assistants and software with facial recognition capabilities.
There’s one more thing you should keep in mind when implementing AI in business.