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AI Transforming Business

Artificial Intelligence (AI) and machine learning are already reshaping the way we do business across all major sectors. AI is being used to assist with strategic decision-making through data processing and synthesization on a scale much larger and faster than the human brain could accomplish. Certainly, many major breakthroughs in AI are yet to come, with industries boasting large amounts of data likely seeing the most significant benefits from the AI revolution.

Here are a few examples of how AI is already shaping day-to-day business: 

Customer Relationship Management (CRM)

CRM tools help professionals create positive, productive client relationships that lead to successful business outcomes for both parties. Nonetheless, CRM tools still require a high level of human input and interaction to achieve desired results. Luckily, CRM tools are starting to use AI to become self-updating and auto-correcting. The CRM tool can stay on top of the relationship management component, freeing up time for more strategic work over redundant tasks. 

Energy

Clean, affordable energy is increasingly paramount as our pool of natural resources decreases. AI is being used in energy management systems to more accurately define energy usage of various assets, such as wind turbines, and predict when these assets are in need of repair. Sensors affixed to these assets provide the data, which is then synthesized by AI machines and passed on to researchers.

Finance

AI is already starting to transform finance by reducing time spent on tasks and providing a more cost-efficient, personalized experience for the consumer. Experts, for instance, predict that regular bank operations will be handled almost exclusively by AI in the near future – for example, by predicting when an individual may be soon needing a mortgage loan and targeting marketing toward them. 

Learn more at Business News Daily.

AI Breakthroughs Coming in the Next Five Years

AI will continue to shape the business world for years to come in ways we can’t yet imagine. Experts, however, are already making predictions on what’s to come. Here are three exciting trends they say to watch out for in the next five years:

Non-human pattern recognition

The application of reinforcement training to AI could be a significant cost and time savings to organizations. The idea is that AI would be taught to learn knowledge from scratch and train against itself. With this ability, machines could learn a year’s’ worth of knowledge in seconds, and, critically, that learning would be free from human bias.

Health care

Each day, doctors and medical professionals evaluate troves of data to make diagnoses and treatment plans. But what if a machine could diagnose and treat patients with even greater accuracy? AI could be used to extrapolate from symptoms and lab results to better diagnose common childhood illnesses, certain forms of cancer, and even Alzheimers. Machines can sort through thousands of radiology images to identify signs of specific injuries or diseases at a faster and more accurate rate than a human ever could, leading to better health outcomes. Interestingly, experts are predicting that in several years from now doctors may be risking malpractice if they don’t use AI to help reach their diagnostic conclusions.

Manufacturing

AI design systems are already at work transforming the manufacturing process, particularly in high-impact areas like clean energy, military, and space travel. AI can be used to discover new, more sustainable materials at a faster rate than pre-AI systems, leading to breakthroughs and innovations in the clean-tech space in particular. Currently, it takes on average more than 15 years to create a new material. This process is already speeding up as AI integrates more readily into the design and discovery process.

Read more at SingularityHub

Preparing Your Organization for the AI Revolution

As AI integrates more intricately into day-to-day business processes across a variety of industries, one thing is clear: organizations that embrace AI will have a competitive advantage in the future. There are several priorities organizations should have to ensure they’re prepared for the AI revolution:

• Access to fast processes and clean data. Organizations must streamline their internal processes to be able to accommodate the fast pace of AI developments. Most importantly, organizations should have a reliable source of clean data to extract from and work with.
• A strategic plan. Organizations should already be re-shaping their business strategy to incorporate AI. Those that don’t may lose competitive advantage in the future AI landscape.
• A ready workforce. Not only does this mean having enough personnel to handle new AI-based processes, but also having personnel trained in AI systems. Experts believe the existing workforce falls short of the requirements needed to accommodate the AI revolution.
• Integration of AI with other technologies. For the greatest impact and value-add, organizations should be strategically incorporating AI with other existing and growing technologies.

The AI revolution has already started, and the impacts are being felt across the business sector in health care, energy, and other major industries. Organizations must be strategic in how they approach AI and assimilate it into their existing business plan and workforce.

Is your organization ready?

Find out more at PWC.

Some conversations are easy to plan for. Take the cup of coffee you ordered this morning for example. Chances are it started with a reciprocal greeting and moved onto a request. Maybe there was a short back-and-forth hammering out small particulars (what size, do you need room for cream), followed by payment, and a quick exchange of thank yous and pleasantries.

Chatbots do especially well with normal interactions like that. But, what’s important to note here is that just because thousands of conversations might be about the same thing generally, the details, phrasing, and particulars will essentially never be identical. You might ask for the “biggest coffee you’ve got” while someone else will say “24 ounce” while the next guy in line will just say “extra large.” You might all want the same humongous coffee, but you’ve got your own way of asking for it.

For more nuanced conversations, the variations are manifold. So while you might know exactly the sorts of issues you need a chatbot to handle, the variations of those interactions are incredibly vast. How do you train a bot to handle discrete issues you can predict but language that you simply can’t?

Enter Yalo. Yalo is an innovative company who leverages partnerships with everyone from Facebook and Whatsapp to AWS and Appen to help companies build 1-on-1 personal relationships with their clients. They partnered with Aeromexico to build the first airline chatbot in the Americas to help Aeromexico better handle the massive amount of traffic and engagement they get on their Facebook page. As we mentioned, two of the central premises of chatbots are that they interact with customers where they already are (Facebook, in this instance) and they’re available 24/7. And when you’re dealing with a business that never sleeps like air travel, having accessible, accurate, always-available support goes a long way.

Now, back to that point about nuance and variance: air travel is not a simple issue. Just booking a flight is a lot more complex than ordering a cup of coffee and, when you factor in how much more expensive and important air travel is, getting it right is incredibly important. There are thousands of variations here that require a bot to understand context, what airports are where, and what layovers are unreasonable.

And that’s just for bookings. Rescheduling, frequent flyer rewards, travel documentation, promotions, upgrades, amenities, seat changes, you name it: the list is long. Training a bot to handle all of this takes significant effort but doing it well can be a massive benefit for both the company and their customers. The company can farm out more subtle, difficult conversations to live reps who can best handle those requests while keeping their reps from spending time on rote requests a bot can handle in less time for less money. Customers, meanwhile, get faster service whenever they need it.

Yalo tapped us to help train this bot. They’re using our platform to pair customer questions (sometimes called “utterances”) with both broad categories and smart, distinct answers. These judgments are used to train and tune a chatbot so it can provide instant, intelligent replies to some of the most pressing issues travelers have.

Bots will change service organizations in the same way chat did, allowing for more efficient solutions to real world problems and consumers should expect to see more and more of these kind of implementations in the future. And the smart folks at Yalo understand both the legwork it takes to create a smart chatbot and the value in doing so, all while injecting those bots with the right sort of attitude and flair.

We’ll be talking a lot about bots in the next couple weeks, complete with a eBook about what we’ve learned about training, tuning, and testing them. So if you’re interested, stay tuned and visit this space in the coming month. The most important bit?