Home General 7 Successful Tips to Scale your AI Strategy

7 Successful Tips to Scale your AI Strategy

Artificial intelligence-based pilot projects increased rapidly in 2018, as numerous businesses began testing machine learning algorithms and multiple automation tools in a bid to strengthen their relationships with their clients, boost their cybersecurity features or even enhance network operations.

Propelled by early outcomes, most CIOs are currently getting ready for the subsequent challenge: scaling artificial intelligence (AI) across the business.

In fact, a new study conducted by PricewaterhouseCoopers revealed that 20% of 1,000 business executives in the US claimed that their enterprises intend to employ AI throughout their business in 2019.

With business aspirations increasing, companies are putting more of their money into these rising technologies.

Regardless of how huge their ambitions are as far as scaling AI is concerned, the road to achieving this desire is full of certain risks such as shifting business priorities and warring strategies that can impede the collaboration between departments.

The lack of adequate talent to deal with the technical work associated with the use of artificial intelligence technologies only makes the problem worse.

If your company has already explored and experimented in artificial intelligence (AI), it is time to start expanding your efforts.

According to Andrew Ng, an AI visionary, as well as AI experts from Deloitte and PwC, here are some key tips to help CIOs in scaling their AI efforts.

1. Create an In-house Artificial Intelligence (AI) Team

The CEO and founder of Landing AI Andrew Ng said that businesses ought to create an AI team, which will assist in keeping all projects within the enterprise.

The AI team or department could sit under the company’s chief AI officer, CDO, CIO or CIO.

By forming this team, you will benefit considerably when it comes to recruiting and retention.

“With a new AI unit, you’ll be able to matrix in AI talent to the different divisions to drive cross-functional projects,” Ng says in an AI transformation playbook published in December. “New job descriptions and new team organizations will emerge.”

While leading AI teams at Baidu and Google, Andrew Ng installed data engineers, machine learning engineers, AI product managers, and data scientists.

Nonetheless, Ng recognized that the existing war for artificial intelligence (AI) talent is “zero-sum in the short term.”

Companies have to collaborate with recruiters in a bid to fill their key positions.

2. Teach Both the Staff and AI Experts how to Work Together

The lack of artificial intelligence (AI) talent should not kill or impede AI initiatives.

Instead, companies ought to utilize tools with the potential to democratize data science and artificial intelligence (AI), including all applications that have user-friendly interfaces designed for AI developers, as well as educational programs created for non-technology experts.

Scott Likens, PwC’s emerging technology and new services leader, said enterprises could categorize employees into three different tiers including data scientists, who are expected to do the heavy lifting including creating, deploying and managing AI apps; citizen developers/power users, who can recognize data sets and use cases and collaborate with AI experts to create new AI applications; and citizen users, who are anticipated to learn how to utilize AI-improved applications.

3. Create a Center of Excellence

Likens said in the PwC report that among the best ways of creating an AI foundation is the establishment of an AI center of excellence or CoE.

This entity, which is expected to determine intellectual property management, vendors, techniques, tools, technology standards, and architecture, will not only help in figuring out how to recognize use cases but also how to cultivate governance and accountability.

Shell, a globally-renowned energy giant, for instance, has put in place data science center of excellence (CoE) that uses analytics, ML and AI in tackling projects like predictive maintenance, particularly for oil-rig machine components.

According to PwC, 24% of the respondents involved in the recent research have a given form of AI center of excellence (CoE).

4. Augment your Artificial Intelligence (AI) through Experimentation

Although it may be tempting to come up with an artificial intelligence plan immediately, Andrew Ng is convinced that many companies cannot create a thoughtful strategy until they have gained a certain level of experience with this technology.

As such, Ng suggested the creation of several difficult artificial intelligence (AI) assets that are widely aligned with a sound strategy, but customized to create a competitive edge in the industry, making it difficult for competitors to replicate.

This calls for a complex data analysis plan aimed at cultivating business insights.

Real estate company Keller Williams, for instance, leveraged thousands of meticulously curated data points relating to houses and machine learning software to boost its listings, claimed the company’s Chief Product Officer Neil Dholakia.

Real estate agents use a Keller Williams application installed on their smartphones to record a given home’s footage.

The application is linked to Google’s Cloud AutoML tool, which instantly recognizes and tags all the features including granite countertops or even hardware floors.

“This went from days and cost to minutes and free for our agents,” Dholakia told CIO.com.

Dholakia, who praises machine learning for its potential to provide a competitive advantage in the sector, said that he intends to expand Keller Williams’ use of artificial intelligence in 2019.

“An AI strategy will guide your company toward creating value while also building defensible moats,” Ng says. “Once teams start to see the success of the initial AI projects and form a deeper understanding of AI, you will be able to identify the places where AI can create the most value and focus resources on those areas.”

5. Develop Responsible AI

Explaining how a given AI model comes up with its decisions is one of the main challenges of adopting AI.

As such, Bank of America’s Chief Operations and Technology Officer Cathy Bessant said in a recent artificial intelligence (AI) Summit held in New York City that it is imperative to come up with transparent artificial intelligence (AI) models.

Enterprises can deal with these “black box” worries by answering certain questions including: Are there appropriate compliance controls in place? Who should be held responsible for the AI systems? Can an entity make sure that those decisions are accurate?

Successful deployment of AI will create accountability for each of the mentioned factors in a bid to build “responsible AI.”

6. Practice Human-Centered or Participatory Design

According to a recent report by Deloitte regarding the state of artificial intelligence (AI) in business, when it comes to establishing responsible artificial intelligence (AI), the initial step for stakeholders ought to be assuming a hands-on position, particularly when it comes to designing sophisticated artificial intelligence implementations.

Participatory design, which entails a type of human-centered design, integrates the requirements of a user “community” into the design process in an attempt to create additional sustainable solutions.

By doing so, the process allows designers to be more aware of and avert problems that they might not have foreseen, possibly by a failure of imagination or even context.

For instance, when a call center employs a chatbot to minimize employee workloads, a participatory process would consist of customers that might interact with the chatbot, a given member drawn from the leadership team and the call center employee.

To make sure that artificial intelligence is based on ethics, businesses ought to build on participatory design through “periodically reviewing and assessing the algorithms to ensure that they are doing the right thing,” said Vic Katyal, a principal, as well as global analytics and data risk leader at Deloitte.

Ultimately, enterprises have to enable a third party to authenticate the AI autonomously, allowing the filling in of gaps as well as navigating through the existing blind spots.

According to Vic Katyal, CIOs are by far the most common senior executives who are charged with the role of overseeing the adoption of artificial intelligence (AI) in business by corporate boards.

7. Come up with a Communications Plan

Since artificial intelligence is bound to impact businesses significantly, companies ought to develop a communications program in a bid to ensure alignment.

The program is set to cover investor relations (outlining a value creation thesis designed for AI); customers (think about strategic marketing); internal communications; talent (branding is essential to attract fresh blood); and government relations (if crucial).

“Because AI today is still poorly understood and artificial general intelligence specifically has been over-hyped, there is fear, uncertainty, and doubt,” Ng says.

“Many employees are also concerned about their jobs being automated by AI. Clear internal communications both to explain AI and to address such employees’ concerns will reduce any internal reluctance to adopt AI.”

Concluding thoughts

Many executives today are bullish as far as the promise of artificial intelligence is concerned.

According to the PwC research, 56% of the 1,100 business and IT executives surveyed by Deloitte claimed that artificial intelligence would change their enterprise within three years.

“The AI/analytics arms race will continue as businesses need to get lean, agile and focused on growth,” says Andy Walter, a consultant for CIOs and strategic advisor to Fractal Analytics. “Leaders that have leveraged AI capabilities across targeted business processes will expand to enterprise-wide value-driving opportunities. The ‘intelligent enterprise’ will beat the competition on the top and bottom line.”

 

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KC Cheung
KC Cheung
KC Cheung has over 18 years experience in the technology industry including media, payments, and software and has a keen interest in artificial intelligence, machine learning, deep learning, neural networks and its applications in business. Over the years he has worked with some of the leading technology companies, building and growing dynamic teams in a fast moving international environment.
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