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10 Business AI Trends in 2022

10 Business AI Trends in 2022

Overtaking Human Intuition... 10 Business AI Trends in 2022

AI has finally settled into the mainstream. Successful proof-of-concepts have emerged in a number of industries, and there have been many examples of successful plant-floor deployments of AI. Some organizations have applied AI/ML projects across the enterprise to complete pipelines. 

This overall maturity is changing the way companies view the strategic value of AI and the areas in which they want its benefits to be realized. Let's look at 10 AI company strategy trends currently diagnosed by industry experts.

AI Approaching the Business Field

In the early days of artificial intelligence, the project was led only by data scientists. They had data and algorithms, and they chose the means to apply new tools to business problems. Sometimes it was successful. Now the dynamics are being reversed.

Business leaders have learned from some successful projects and are becoming more aware of how AI can help. As a result, front-line business units are increasingly leading the adoption of AI.

"Companies that are good at AI are leading the business," said Alex Singlela, global head of QuantumBlack at McKinsey&Co.

Honeywell, for example, is using AI in its internal operations and applying it to products and services for its customers, says Sheila Jordan, the company's CDTO, "We also have a close relationship with the business. We are value-oriented, and value for our customers comes first.”

AI permeates all fields of the enterprise

When Jordan joined Honeywell two years ago, his first large project was to implement a data warehouse strategy to collect all transaction data.

“Now every department, every business unit has a digital agenda,” she said. Honeywell, for example, has digitized all contracts. With more than 100,000 digital contracts, she said, the company has a wealth of data to help them build AI solutions for virtually any functional area.

Now with AI, all Honeywell contracts can now be automatically reviewed for areas affected by inflation or price issues. "It's impossible for a human to review 100,000 contracts," Jordan said.

Likewise, with complete inventory data, Honeywell can now make smarter decisions about managing raw materials more efficiently by knowing which inventory to discard and which to re-use. “AI is emerging in every department,” she said. It includes finance, legal, engineering, supply chain and IT.”

Augmenting automation with AI

It's been three years since Honeywell launched an aggressive automation program. Companies are now reviewing automation for all repetitive tasks. “There will probably be 100 projects this year,” Jordan said. We are automating these tasks across the board.”

And Honeywell is working to make this automation smarter, she added. “We will be applying AI to more automated bots,” she said. It is important for automated bots to get smarter.”

Another company that has started doing basic rules-based automation is Booz Allen Hamilton (BAH). Now, the company is working to integrate AI and machine learning into automation so that it can be applied to a wider range of tasks, said Justin Neroda, vice president of AI activities at Booz Allen (BA).

“People start with the simplest automation. And ask yourself, 'Is there anything else I can automate?' And you realize you need AI and ML.”

“AI-based automation can help companies deal with manpower shortages or high volumes of work,” he said. Or, after automating half of the work, people can take care of the hard part.”

Applying AI Naturally

There is an important change management component to large-scale AI adoption, McKinsey's Singler said. It takes an understanding of how people are going to use it, he said, and it's not just the skills people are developing, but also the combination of technical people and subject matter and business experts.

“If you were to ask insurers to choose three ways to apply AI, the odds of doing so are zero. But the more automatic it is based on the workflow, the more likely it is to be successful. "The less you need to change someone's behavior, the higher your adoption rate is likely to be."

After successful initial proof-of-concepts , holistically transforming AI strategy

companies often form organizations of AI experts to operationalize their technology and build talent, expertise, and best practices. But once companies reach critical mass levels, it makes sense to dismantle some of these expert organizations and build AI to move experts directly where they are most needed.

"For less mature companies, it's valuable to have an organization of professionals that retains talent and learns across institutions," McKinsey's Singlera said. Without it, companies generally do not have the ability to scale. Talented people like to work with like-minded people. And inexperienced people benefit from being part of an expert organization. Because you can grow or learn there.”

Dispersing them too early will dilute the impact and reduce a company's ability to iterate and replicate successful projects across multiple business sectors.

“However, reaching a certain level of maturity and scale has real benefits in the long run when tech professionals have deep AI expertise and domain expertise. But it is only possible when there is scale.”

Business problems are decentralized, said Amol Azgaonka, an engineer at Insight.

"You can't expect AI deployments to be centralized because the business problems don't stay in one place," he said. They too must be dispersed. But there must be a centralized AI strategy aligned with business impact.”

Like many other companies, BAH started with a core AI group. Justin Neroda, vice president of AI activities at BAH, said, “Last year we pushed this forward. We have sub-cells through companies that have acquired AI experts. But before it can diffuse, it must reach a critical mass. Otherwise, it will all fall apart.”

Business Process Transformation Driven

by AI When companies first start using AI, they often look for business processes where AI can make a difference. “We break this process into pieces, digitize each piece, and apply AI to streamline it,” said Sanjay Srivastava, CDO at Genpact. However, in the end, in most cases, the process itself is the same. The process itself doesn't change," he said.

AI has the potential to radically change business processes, he said. For example, Genfact does a lot of account processing for its customers.

“If you apply AI to invoices, you can know which invoices will be challenged,” he said. We can figure out which parts of the portfolio have the highest risk.”

The predictive power of AI can reshape entire processes, he said. “The application of AI allows us to think about and completely reorganize the end-to-end (E2E) value chain.”

ML Ops becoming a reality According to a McKinsey report

released at the end of 2021, the factor that differentiates companies that maximize profits through AI is the use of ML Ops.

This is a big emerging AI trend, said Carmen Fontana, head of cloud and emerging technologies activity at Augment Therapy, a physiotherapy technology company. Fontana was the head of cloud and emerging technologies activity at Centric Consulting.

“The goal now is to put machine learning into production,” she said. Two or three years ago, this field was booming and people thought it was necessary. But in practice, it didn't apply much," she said. But now there are off-the-shelf tools and methodologies that allow organizations to more rigorously train, deploy and monitor AI models, she said.

Companies deploying AI pipelines

BAH is currently working on about 150 AI projects with customers, BA's Neroda said. But over the past year or so, the company has begun to move away from this one-off model. "Over the past year and a half, we've been investing in modular capabilities and end-to-end pipelines," he said.

Successful AI requires more than just a practical model. As the data changes over time and the model is continually refined, there is a holistic process required to maintain the model.

“The biggest challenge is how to tie all the tools together,” he said. We are working to standardize this and build reusable ones to use across projects.”

Building trust in AI

As employees and executives become more accustomed to AI, they are increasingly embracing AI decisions that go against human intuition.

Blue Yonder Strategic Advisor and Founder Michael Paint recently worked with a large UK food retailer struggling with pandemic-related supply chain issues. When the company used manual processes to manage its supply chain, there were a lot of empty shelves, he said. In addition, there was a shortage of people with the knowledge, ability, and will to do the job.

Automated AI-based systems can provide cost savings and improved performance. But when the pandemic hit, people wanted to shut down the automatic system. "It turns out that, surprisingly, automatic systems can adapt much faster than humans," he said.

So instead of shutting down the system, the company expanded it to include distribution centers as well as stores. As a result, empty shelves and food waste are reduced. Additionally, store managers could stop reconciling orders for two hours a day and spend more time improving customer satisfaction.

There are also other ways to build trust in AI, Paid said. "Some people are critical, and based on years of experience, I don't think AI can make decisions as well as humans," he said. Adding accountability may help alleviate these concerns. An explainable AI is one where a system can explain to a human user the factors it considers in its decision.

Emerging business model possibilities

In some areas, AI is creating opportunities that did not exist before. Self-driving cars, for example, have the potential to change society and create entirely new kinds of businesses. But AI-powered business transformation can also happen on a smaller scale.

For example, one of the reasons banks fail to offer small loans is the cost of human review operations. The cost of investigation and processing is higher than any interest income a bank can earn. But when assessed and processed using AI, small loans allow banks to serve a whole new group of customers without charging excessive interest.

“These use cases haven't spread yet,” said Jai Das, president and partner at Sapphire Ventures. "It fundamentally changes the way we do business, and companies don't change it quickly."