While artificial intelligence (AI) has been around for many years, deployment has been picking up. Between 2017 and 2018, consulting firm McKinsey & Co. foundthe percentage of companiesembedding at least one AI capability in their business processes more than doubled to 47 percent from 20 percent the year before.

Although companies are adopting it, they often lack a clear plan: A recent IDC survey found that of the companies already using AI, only 25 percent had an enterprise-wide strategy on how to implement it.

To help navigate that challenge, heres how the four pillars of Google Clouds Deployed AI vision can reshape your business.

For AI to be deployed effectively, it must be focused on a new business problem or unrealized opportunity. To that end, there are several areas of business that the technology is well-suited to address.

One key problem is fixing aging processes, notes Ritu Jyoti, program vice president of Artificial Intelligence Strategies for research firm IDC.

Companies that have been around for a long time will have a lot of archaic processes, and they need to be upgraded, Jyoti says.

Bank fraud is one prominent example. Machine learning (ML), a subset of AI, provides an opportunity to solve this problemby helping banks sort through large amounts of bank transactions to detect suspicious patterns of financial activity.

Customer relationships is another area AI can improve. Chatbots, for example, are enhancing customer service by providing support 24/7, Jyoti says. Furthermore, companies can also use AI to develop the right incentives for customers without losing money. Firms sometimes lose income due to lapsed contracts or stuck deals in which a transaction is started but unable to be completed, Jyoti notes.

This feature helps organizations optimize early payment discount offers by using ML algorithms to find the right balance between incentivizing customers while ensuring profitability for the seller, Jyoti says.

Another business problem in which AI can help is in document processing, including insurance claims, tax returns and mortgage applications, which can involve hundreds of pages of documents on income and assets, notes Vinod Valloppillil, Googles head of product for Google Cloud Language AI, who spoke at Forbes CIO Next conference.

[Document processing] is one of the few domains that actually brings in multiple parts of AI all simultaneously, Valloppillil says. It incorporates computer vision, deep learning and natural language processing.

When deploying AI to solve a business problem, the technology should be central to that solution. Many examples across industriesincluding healthcare and energyexemplify how innovative problem-solving can hinge on AI.

The medical industry, for instance, is turning to AI to build algorithms to detect pneumonia. With genomic data bringing insights on who will be susceptible to various disease conditions, disease prevention is one area that cant be solved without AI. An AI platform can also become part of an end-to-end solution when hospitals need to connect medical data to cloud platforms.

AI can also help physicians determine whether a patient has diabetic retinopathy, Valloppillil says. An Explainable AI model would help determine if screening was necessary based on the appearance of various regions of the image.

Were getting to the point where AI can do quite a bit of engineering, Valloppillil says.Meanwhile, the energy sector has found AI to be essential to keeping wind facilities safer, faster, and more accurate, according to Andrs Gluski, president and CEO of AES, a global power company. Drones and Cloud AutoML Vision, a platform that provides advanced visual intelligence and custom ML models, make these improvements in wind energy possible.

Once youve identified the business problem and decided to use AI in the solution, the next step is building customer trust and maintaining proper ethics. In a Deloitte survey of 1,100 IT and line-of-business executives, 32 percent placed ethical risks in the top three of AI-related concerns.

To build trust, ethics should come ahead of any productivity or financial gains from using AI. A crucial part of that is being transparent about how a company uses AI.Consulting firm Capgemini recommends using opt-in forms to help build transparency with customers regarding AI. Meanwhile, privacy laws like the European Unions General Data Protection Regulation (GDPR) also contribute to the transparency requirements for AI.

Jyoti also recommends including fact sheetssimilar to how food packaging includes nutrition informationabout details like data sources and lineage. A set of AI Principles from Google help businesses ensure that theyre using AI in a responsible manner and that they understand the limits of the technology. Users of AI should maintain accountability to human direction, uphold standards set by scientists and test for safety.

These are the principles we as a company orient around, things like always optimize around the fairness of AI, and try to avoid any situation where AI can get abused, Valloppillil says.

Finally, to ensure a cycle of improvement, companies should use clear, objective metrics to assess progress towards their business goals.

For example, if AI is used to assist with rsum screening in the hiring process, make sure the screening adheres to company policies on equal opportunity to maintain fairness. To form an objective metric, come up with representative numbers of candidates for various demographics and train ML algorithms accordingly.

Avoid the blinders of the homogenous teams, Jyoti says.Tools and frameworks like Explainable AI can help companies build inclusive systems that address bias, which involves the data not being representative of the decisions a business is trying to make. This makes the problem of garbage in, garbage out multiplied a hundredfold, Valloppillil says.

The concept of explainability helps provide insight into the decisions that AI helps deliver.

With explainability we're now finally getting to the point where we go peek inside the box, Valloppillil says. We actually have a shot at understanding exactly why does AI make the call.

As AI continues to evolve, there are increasing opportunities for the technology to meaningfully improve business operations. With ethical implications in mind and a clear focus on measurable metrics, Deployed AI is poised for growth.

Originally posted here:

4 Steps To Shape Your Business With AI - Forbes