How to Implement AI in Business Free eBook

AI is meant to bring cost reductions, productivity gains, and in some cases even pave the way for new products and revenue channels. AI value translates into business value which is near and dear to all CxOs—demonstrating how any AI project will yield better business outcomes will alleviate concerns they may have. I’m teaching a new course this semester on cognitive technologies (AKA artificial intelligence) to Babson MBAs. Many of them are new to this set of technologies, and seeing the topic through my students’ eyes has made me realize how overwhelming it can be.

Depending on the size of the organization and its needs new groups may need to be formed to enable the data-driven culture. Examples include an AI center

of excellence or a cross-functional automation team. Large organizations may have a centralized data or analytics group, but an important activity is to map out the data ownership by organizational groups. There are new roles how to implement ai in your business and titles such as data steward that help organizations understand the governance

and discipline required to enable a data-driven culture. “You have to go both for impact and build the foundations in parallel, and that is the most challenging part,” advises Najat Khan, PhD, Chief Data Science Officer and Global Head of Strategy & Operations for Janssen Research & Development.

The Majority of Business Owners Expect AI Will Have a Positive Impact on Their Business

After the AI program becomes operational, now is the time to test the system to see how your efforts are helping reach your goals. When you know your metrics, such as order times, sales improvement and productivity, you can decide how to best implement AI in your business. During the dot-com boom of the late 1990s and early 2000s, numerous internet service providers (ISPs) and search engines emerged, but not all survived the eventual market correction. Engines like Infoseek, Lycos, WebCrawler and Ask Jeeves didn’t make the cut.

But there are just as many instances where algorithms fail, prompting human workers to step in and fine-tune their performance. Artificial intelligence is capable of many things — from taking your customers’ calls to figuring out why your equipment is consuming way more energy than it used to.

Most organizations fear AI failure, but those that implement AI do report benefits

It is believed to have the potential to make a transformation in any industry and offer a promising future for businesses with its learning algorithms. The global technology intelligence organization ABI Research predicts the number of businesses that will adopt AI worldwide will scale up to 900,000 this year, with a compound annual growth rate of 162%. This revolutionary technology helps improve customer decision management, forecasting, QA manufacturing and writing software code, increasing revenue with the data it generates every day. Many things must come together to build and manage AI-infused applications.

• Artificial intelligence is capable of many things — from taking your customers’ calls to figuring out why your equipment is consuming way more energy than it used to.
• Most AI practitioners will say that it takes anywhere from 3-36 months to roll out AI models with full scalability support.
• Survey results indicate that businesses are adopting AI for a variety of applications such as customer service, customer relationship management (CRM) and cybersecurity.
• For instance, AI can save pulmonologists plenty of time by identifying patients with COVID-related pneumonia, but it’s doctors who end up reviewing the scans to confirm or rule out the diagnosis.
• This list is not exhaustive; still, it could be a starting point for your AI implementation journey.

Using a battery of statistics, we found that the odds of generating profit from using AI are 50 percent higher for companies that have strong experience in digitization. At this point in their journeys, organizations realize they need to leverage the resources they can get their hands on to keep up, whether human or technology resources. In fact, continuous improvement is the key to maintaining a competitive advantage in your business. Establish key performance indicators (KPIs) that align with your business objectives, so you can measure the impact of AI on your organization. Regularly analyze the results, identifying challenges and areas for potential improvement.

Turn AI into real-time AI.

Managing AI models requires new type of skills that may or

may not exist in current organizations. Companies have to be prepared to make the necessary culture and people job role adjustments to get full value out of AI. As Wim observes, organizations often focus on using AI to streamline their internal processes before they start thinking about what problems artificial intelligence could solve for their customers. Consider using the technology to enhance your company’s existing differentiators, which could provide an opportunity to create new products and services to interest your customers and generate new revenue.

Recognize that the path to AI starts with understanding the data and good old-fashioned rearview mirror reporting to establish a baseline of understanding. Once a baseline is established, it’s easier to see how the actual AI deployment proves or disproves the initial hypothesis. „To successfully implement AI, it’s critical to learn what others are doing inside and outside your industry to spark interest and inspire action,“ Wand explained. When devising an AI implementation, identify top use cases, and assess their value and feasibility. In addition, consider your influencers and who should become champions of the project, identify external data sources, determine how you might monetize your data externally, and create a backlog to ensure the project’s momentum is maintained. Ok… so now you know the difference between artificial intelligence and machine learning — it’s time to answer two related questions before we dive into actual implementation.

Resist the temptation to put technology teams solely in charge of AI initiatives.

After launching the pilot, monitoring algorithm performance, and gathering initial feedback, you could leverage your knowledge to integrate AI, layer by layer, across your company’s processes and IT infrastructure. Also, a reasonable timeline for an artificial intelligence POC should not exceed three months. If you don’t achieve the expected results within this frame, it might make sense to bring it to a halt and move on to other use scenarios.

Data scientists who build machine learning models need infrastructure, training data, model lifecycle management tools and frameworks, libraries, and visualizations. Similarly,

an IT administrator who manages the AI-infused applications in production needs tools to ensure that models are accurate, robust, fair, transparent, explainable, continuously and consistently learning, and auditable. AI-infused applications should be consumable in the cloud (public or private) or within your existing datacenter or in a hybrid landscape. All this can be overwhelming for companies trying to deploy AI-infused applications. Companies are actively exploring, experimenting and deploying AI-infused solutions in their business processes. Businesses are employing artificial intelligence (AI) in a variety of ways to improve efficiencies, save time and decrease costs.

Finally, there are deep neural networks that make intelligent predictions by analyzing labeled and unlabeled data against various parameters. Deep learning has found its way into modern natural language processing (NLP) and computer vision (CV) solutions, such as voice assistants and software with facial recognition capabilities. But if we take labeled data out of the ML model training process, we’ll get unsupervised machine learning algorithms that crunch vast amounts of information — again, let’s use cat picks as an example — down to meaningful insights. For instance, we could tell algorithms that a particular database contains images of cats and dogs only and leave it up to the AI to do the math. While business owners see benefits in using AI, they also share some concerns.