10 practical AI tips for your business

What does artificial intelligence (AI) look like in actual business situations? Check out these suggestions for incorporating deep learning algorithms, machine learning, and more into your current goods and services.

1. Get Familiar With AI

Spend some time learning about the capabilities of contemporary AI. Through its collaborations with institutions like Stanford University and businesses active in the AI industry, the TechCode Accelerator provides its entrepreneurs with a wide range of resources. Also, you ought to utilise the plethora of online data and tools at your disposal to become acquainted with the fundamental ideas of AI. Tang suggests taking use of some of the remote workshops and online courses provided by businesses like Udacity as simple ways to get started with AI and to improve your organization’s expertise of subjects like ML and predictive analytics.

2. Identify the Problems You Want AI to Solve

The next step for every organisation is to start exploring various concepts once you are familiar with the fundamentals. Consider how you might enhance the capabilities of your current products and services with AI. More essential, your organisation should have in mind particular use cases where AI may help with business issues or offer tangible benefits.

“When we deal with a company, we begin by giving a general overview of its most important tech initiatives and issues. We want to be able to demonstrate how machine learning, image recognition, and other technologies fit into those goods, typically through some kind of workshop with the business management “Tang clarified. “Industry-specific details are usually different. For instance, by incorporating ML into the video monitoring process, the organisation can gain a lot of value.”

3. Prioritize Concrete Value

The potential business and financial value of the various AI implementations you have found should next be evaluated. Tang emphasised the significance of connecting your initiatives directly to business value, saying that it’s simple to get lost in “pie in the sky” AI discussions.

Put the dimensions of potential and viability into a 2×2 matrix to determine your priorities, Tang advised. “This should enable you to set priorities based on short-term visibility and determine the company’s financial value. You often require ownership and acknowledgment from managers and senior executives for this step.”

4. Acknowledge the Internal Capability Gap

Between what you desire to accomplish and what you can actually accomplish within a certain time limit, there is a significant disparity. Before beginning a full-scale AI adoption, Tang advised a company to be aware of its tech and business process capabilities and limitations.

Tang remarked, “This can sometimes take a while to do. “In order to close your internal capability gap, you must first determine what you need to buy and any internal process changes that must be made. Depending on the company, there might already be teams or projects that can assist in achieving this organically for some business units.”

5. Bring In Experts and Set Up a Pilot Project

When your company is prepared organizationally and technologically, it’s time to begin constructing and integrating. Start small, have project goals in mind, and, most importantly, be conscious of what you do and don’t know about artificial intelligence, according to Tang. This is the situation where hiring outside consultants or AI expertise can be really helpful.

“You don’t need a lot of time for a first project; often, 2-3 months is a suitable range for a pilot project,” Tang added. “A small team of no more than four persons should be formed to include both internal and external participants. The team will remain focused on simple objectives due to the shorter time frame. You should be able to determine the longer-term, more complex project after the pilot is over, as well as whether the value proposition makes sense for your company. Also, it’s critical that the team working on your pilot project incorporate knowledge from both the commercial and the AI worlds.”

6. Form a Taskforce to Integrate Data

To avoid a “garbage in, garbage out” situation, Tang advised cleaning your data before integrating machine learning into your company. Internal corporate data is frequently dispersed throughout numerous data silos of various legacy systems and may even be in the possession of various business units with various goals, according to Tang. Hence, creating a cross-[business unit] taskforce to integrate various data sets, remove inconsistencies, and ensure that the data is reliable, rich, and has all the necessary dimensions for ML is a crucial first step in acquiring high-quality data.

7. Start Small

Instead of attempting to handle too much at once, start by applying AI to a tiny sample of your data. Aaron Brauser, Vice President of Solutions Management at M*Modal, which provides natural language understanding (NLU) technology for health care organisations as well as an AI platform that integrates with electronic medical records, said: “Start simple, use AI incrementally to prove value, collect feedback, and then expand accordingly” (EMRs).

Data on specific medical specialties could be one such type of data. The Chief Medical Information Officer (CMIO) at M*Modal, Dr. Gilan El Saadawi, advised being selective about what the AI would read. “For instance, choose a particular issue you wish to address, concentrate the AI on it, and pose a particular query to it rather than saturating it with data.”

8. Include Storage As Part of Your AI Plan

According to Philip Pokorny, Chief Technical Officer (CTO) at Penguin Computing, a business that provides high-performance computing (HPC), AI, and ML solutions, as you ramp up from a small sample of data, you’ll need to take into account the storage requirements to build an AI solution.

“Achieving research findings requires improving algorithms. However, Pokorny said in a white paper titled “Critical Decisions: A Guide to Creating the Entire Artificial Intelligence Solution Without Regrets” that “AI systems cannot advance enough to reach your computing objectives without vast volumes of data to help construct more accurate models.” “Thus, while designing an AI system, rapid, optimal storage should be taken into account.”

He also recommended that you optimise AI storage for data ingest, workflow, and modelling. When the system is live, “taking the time to study your alternatives can have a significant, positive impact,” Pokorny continued.

9. Incorporate AI as Part of Your Daily Tasks

According to Dominic Wellington, Global IT Evangelist at Moogsoft, a provider of AI for IT operations, workers have a tool to make AI part of their everyday routine rather than something that replaces it with the additional insight and automation provided by AI (AIOps). Introduce the solution as a way to supplement their everyday activities as some employees might be sceptical of technology that could damage their employment, according to Wellington.

In order to overcome problems in a process, he continued, businesses need be open about how the technology functions. Employees can easily see how AI augments rather than replaces their work thanks to this “behind the bonnet” experience, according to the author.

10. Build With Balance

According to Pokorny, creating an AI system requires balancing the needs of the research endeavour with those of the technology. The overarching principle is that you should develop the system with balance, Pokorny added, even before you begin to design an AI system. “While it may seem intuitive, too frequently, AI systems are created without consideration for the needs and constraints of the hardware and software that would enable the research, instead focusing on specific features of how the team envisions attaining its research goals. The end consequence is a subpar, even defective, system that is unable to accomplish the specified objectives.”

Companies must allocate enough bandwidth for networking, storage, and graphics processing units (GPUs) in order to strike this balance. Another aspect that is sometimes disregarded is security. By its very nature, AI requires access to large amounts of data in order to function. Verify that you are aware of the different types of data that will be utilised in the project and that your standard security measures, such as encryption, virtual private networks (VPNs), and anti-malware, might not be adequate.

The necessity to protect against power outages and other circumstances through redundancy must be balanced with the total budget’s use for research, according to Pokorny. “You might also need to provide flexibility to permit hardware repurposing as user requirements change.”

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