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Intel AI Article - Strategies Getting Started Yuri Arcurs Forbes

Building AI: Key Steps For Adoption And Scaling Up

Intel AI Article - Strategies Getting Started Yuri Arcurs Forbes
Intel AI

The prospect of folding artificial intelligence (AI) technologies immediately into layers of processes and personnel is enough to spook many top decision makers. And yet according to 313 executives recently surveyed by Forbes Insights—63% of whom were in the C-Suite—almost all (95%) believe that AI will play an important role in their responsibilities in the near-future. 

The majority of CEOs today are not drivers of AI adoption—that responsibility falls on C-level technology leaders who need to build a strong business case and show results that encourage a deeper dive into change. With that firmly in mind, Forbes Insights and Intel have taken their combined experience covering and developing technology to produce this introductory guide to AI adoption, from buy-in and deployment to building a corporate culture around data. Consider the below three steps your beginner’s guide to AI.

Build The Business Case 

It’s important to see beyond the swirl of hype and expectations around AI technologies and view them for what they really are—massive accelerators of processes and insights and profound amplifiers of human capability. The value to be realized is significant: Think of predictive maintenance, logistics optimization, customer-service personalization and razor-sharp marketing.

Yet to realize the full value from these technologies, you have to build the business case to senior leadership. The key is to think beyond the common metrics of success. “Traditional ways of looking at growth no longer apply,” notes KPMG in its “2018 CEO Outlook,” which was produced in collaboration with Forbes Insights. “Growth is no longer solely a quantitative measure of revenue or profits.” It should “also include new metrics such as employee engagement and customer satisfaction.

Building the business case is best framed around achievable goals that consider the positive impact on human work. When building your case for these new technologies, make sure you:

Pick the right problem to solve

1. Start with a small problem in a process or objective that will measurably benefit from a quick deployment of AI, and you’ll be able to make the case for cost savings, efficiency and competitive advantage.

2. Avoid a large-scale initiative right out of the gate—it will almost surely fail on some level, dimming enthusiasm and momentum.

3. Be ready to scale the trial run—and have another project ready to go whether the first effort fails or not.       

Be human about AI

1. Win over and include key decision makers throughout your organization to build consensus and a shared vision for the role AI technologies can play in your business. The next two steps will help you get there.

2. Describe a new environment around decision making where AI-generated insights augment and hone human ability—for example, the ability of managers in a grocery chain to order exactly the amount of sushi to meet demand in two weeks, in every store.

3. Evangelize the impact AI can have on employee and user experiences—the vast reduction in repetitive workloads at all levels of your organization, for example, or the personalized marketing and advertising that AI can generate.

Scale Effectively 

Once you’ve built your business case and refined your algorithm with fresh, accurate data from the trial, you’re ready to wade deeper. For most businesses, an AI partner can work with your own talent to ramp up new initiatives. Part of the strategy is to adopt an IT infrastructure that can handle the storage and computing power required by technologies such as machine learning and deep learning. One of the biggest trends in IT is the emergence of software- and AI-defined infrastructures, a combination of computing, storage and network architectures that decouples hardware and software in the cloud.

Transparency is another aspect to scaling AI. To an increasing degree there are calls to make the outcomes of AI more understandable, to satisfy both compliance processes (in financial services, for example, in assuring fair lending) and internal teams who will want to know how a machine produced its results or conclusions. Attaining full visibility into the deep layers of neural networks without diminishing their power and benefit is unlikely—maybe impossible. But introducing some level of understanding into digital decision making is important. Building and scaling AI across your business will require new, cloud-based IT infrastructure and an understanding of the impact AI can have on decision making.

Think cloud

1. Begin to detach from legacy systems, which are unlikely to have the storage or computing power to handle the torrent of data.

2. Go to the cloud for its ability to scale up and down quickly, as needed, for heavy computing tasks—flexibility that is extremely useful when running machine learning and deep learning training.

3. Partner with an AI specialist with a cloud-hosted hardware and software platform.

Think transparency

1. Look closely at the systems and software you’re using when introducing AI to be sure you understand the impact they will have on the AI solutions you deploy.

2. Explore explainable AI, the effort to gain a view into the decision making of deep neural networks.

3. Discuss transparency with C-suite executives and managers. They may be the most cautious about the deployment of AI, with increasing legal overview and regulations.

Create A Data-Driven Culture 

Most businesses are simply not equipped to manage and mine data. If your data or algorithms are inaccurate or biased—if human teams mismanage the AI environment—the results will be ineffective or erroneous. To continue to build scale and keep momentum going with AI, it’s crucial to make data part of your corporate culture, from the C-suite to the front lines. With this as a focus, your data science and business teams will work together more fluidly and, as a data-forward business, you’ll attract and retain top talent.

Building and scaling AI across your business will require new, cloud-based IT infrastructure and an understanding of the impact AI can have on decision-making.

Building a culture around data will keep your entire organization working in tune with trusted data. Here’s how to start:

Learn

1. Encourage and educate C-level leaders to be data-driven and scientific in their thinking—it will set a top-down example for your organization.

2. Transfer as much of the expertise and knowledge of your AI consultants or partners to your employees as possible—it will boost your in-house capabilities.

3. Bring HR leaders into the AI mix from the early stages so they can learn about the technologies as they hunt for top data-science talent—they’re on the front lines of the battle to nab the best people, a critical business goal. Particularly valuable now and in the years ahead will be executives with sharp minds for both computer science and business.

Democratize data

1. Establish a single source of truth (SSOT), so that teams throughout your organization trust and use the same data in decision making.

2. Upskill current employees through education programs (funded by cost savings from AI) so they have the data literacy to understand dashboards, reports and analytics.

3. Make a data dictionary by gathering a team of domain experts (or subject-matter experts) and stakeholders to act as an “editorial board” charged with listing and defining metrics and technologies—it will put everyone on the same page.

It’s easy to see the attention given to AI as part of the standard hype cycle—and, indeed, there is an aspect of overexcitement from some voices about the superhuman ability of these learning technologies. One important idea to bear in mind is that AI really is superhuman in its ability to handle and interpret data. That power is very much in the present and growing—and seeing the technology as an enabler of better customer relationships, greater efficiency and increased revenue is the best way to get started and scale. 

Learn more about how companies are leveraging AI today.

CREDITS: Yuri Arcurs/iStock