Everything You Need to Get Started with Artificial Intelligence in your company.
Technology has developed a strong affinity for artificial intelligence (AI). With its wide range of applications, from advanced data analysis to automated customer support, this technology has permeated various aspects of businesses. This discussion explores how your company can securely and effectively harness these benefits.
The New Paradigm
Artificial Intelligence (AI) is a widely recognized scientific discipline that seeks to enable computers, robots, and products to emulate human intelligence. Consequently, AI has become an intensive study area within the scientific community, comprising multiple branches. Throughout history, machine learning and optimization have been two highly researched fronts that continue to captivate researchers’ attention due to the emergence of novel and complex research topics. These topics include transfer optimization, swarm robotics, real-time drift detection, and adaptation to evolving conditions. Furthermore, new paradigms, such as deep learning and explainable AI, have solidified AI as a fundamental pillar of computer science.
Let’s delve into getting started with AI in your business or company.
1. Getting Started
To embark on your AI journey for your business or company, it is crucial to become acquainted with modern AI’s capabilities. Fortunately, a wealth of online information and resources available can aid in familiarizing yourself with the fundamental concepts of AI. For example, organizations like Udacity offer remote workshops and online courses that can serve as convenient entry points for beginners. These courses cover various areas, including machine learning (ML) and predictive analytics, enabling you to enhance your knowledge within your organization. Taking advantage of these resources is an easy and effective way to initiate your AI endeavors.
2. Getting StarIdentify the Problems You Want AI to Solveted
Once you have gained a solid understanding of the basics, the next crucial step for any business is exploring various ideas for integrating AI into your existing products and services. Consider how AI capabilities can be incorporated to enhance and improve your offerings. Equally important is to identify specific use cases where AI can address business challenges or deliver tangible value. Your company must have a clear vision of how AI can solve problems or provide measurable benefits to leverage its potential effectively.
When engaging with a company, it is essential to understand its vital technological programs and challenges. This allows you to demonstrate how technologies such as natural language processing, image recognition, machine learning (ML), and others can be integrated into their existing products or services. Typically, this involves conducting workshops with the company’s management to showcase the potential applications of these technologies. The exact approach may vary depending on the industry. For instance, in the case of a company involved in video surveillance, incorporating ML into the surveillance process can unlock significant value. The key is to tailor the discussion and implementation strategy to each industry and company’s specific needs and context.
3. Prioritize Concrete Value
After identifying potential AI implementations for your business, the next step is to evaluate the likely company and financial value they can bring. While it’s easy to get caught up in lofty discussions about AI, it is crucial to anchor your initiatives to tangible business value. Tangibly tying your AI initiatives to specific business outcomes is essential. This ensures that your AI projects are aligned with your organization’s strategic goals and have a clear return on investment, enabling you to prioritize and focus on initiatives that can deliver real value to your company.
To establish priorities, examine the aspects of potential and feasibility and organize them within a two-by-two matrix. This approach will assist you in prioritizing by considering short-term visibility and understanding the company’s financial value. To successfully execute this process, it is typically necessary to obtain support and acknowledgment from managers and executives at the highest level of authority.
4. Acknowledge the Internal Capability Gap
There’s a stark difference between what you want to accomplish and what you have the organizational ability to actually achieve within a given time frame. CNM said a business should know what it’s capable of and what it’s not from a tech and business process perspective before launching into a full-blown AI implementation.
The process of addressing your internal capability gap can be time-consuming. It involves identifying the necessary acquisitions and interior process enhancements required before proceeding. In some cases, existing projects or teams within the organization can assist in this organic development specifically for specific business units.
5. Bring In Experts and Set Up a Pilot Project
Once your business has prepared itself from an organizational and technological standpoint, it is time to build and integrate AI solutions. According to CNM, several crucial factors must be considered during this phase. First, it is advisable to start small and focus on manageable projects. Clearly define project goals to ensure a clear direction and measurable outcomes. Additionally, it is essential to acknowledge your knowledge limitations regarding AI. Recognizing what you don’t know is crucial in navigating the complexities of AI implementation. This is where leveraging the expertise of outside consultants or AI specialists can prove invaluable. Bringing in external experts can provide valuable insights, guidance, and support, ensuring a smoother and more successful integration of AI into your business.
According to CNM, allocating a significant amount of time for the first AI project is unnecessary. Typically, a pilot project can be accomplished within 2-3 months. It is advisable to assemble a small team comprising internal and external members, typically around 4-5 people. The compressed timeframe of the pilot project helps maintain focus on transparent and attainable goals. Once the pilot project is completed, assessing the viability and value proposition of more extensive and long-term projects for your business becomes more accessible. It is crucial to merge the expertise of individuals knowledgeable about the company and those well-versed in AI within the pilot project team. This integration ensures a holistic and balanced approach to the project, leveraging the collective insights of both domains.
6. Form a Taskforce to Integrate Data
CNM emphasizes the importance of data preparation before implementing machine learning (ML) in your business. It is essential to ensure your data is clean and ready to avoid the “garbage in, garbage out” scenario. Corporate data is often dispersed across various data silos within legacy systems, and other business groups with distinct priorities may control it. Forming a cross-business unit task force is crucial to obtaining high-quality data. This task force will work towards integrating disparate data sets, resolving inconsistencies, and ensuring the accuracy and richness of the data. It is imperative to have the correct dimensions and attributes in the data for successful ML implementation. By undertaking these data preparation measures, you can enhance the quality and reliability of your data, setting a solid foundation for effective ML utilization.
7. Start Small
Instead of overwhelming yourself with a large amount of data, it is advisable to begin by applying AI to a smaller sample. By taking this incremental approach, you can demonstrate its value, gather feedback, and expand accordingly. Additionally, this approach provides healthcare organizations with natural language understanding (NLU) technology and an AI platform seamlessly integrating with electronic medical records (EMRs).
The AI should focus on a specific problem related to medical specialties and be provided with selective information to address the issue. This involves narrowing down the data that the AI will access, concentrating its attention on a particular question or challenge rather than overwhelming it with much information.
8. Include Storage As Part of Your AI Plan
After you ramp up from a small sample of data, you’ll need to consider the storage requirements to implement an AI solution, according to Philip Pokorny, Chief Technical Officer (CTO) at Penguin Computing(Opens in a new window), a company that offers high-performance computing (HPC), AI, and ML solutions.
“Improving algorithms is important to reaching research results. But without huge volumes of data to help build more accurate models, AI systems cannot improve enough to achieve your computing objectives,” Pokorny wrote in a white paper(Opens in a new window) entitled, “Critical Decisions: A Guide to Building the Complete Artificial Intelligence Solution Without Regrets. That’s why the inclusion of fast, optimized storage should be considered at the start of AI system design.”
In addition, you should optimize AI storage for data ingest, workflow, and modeling, he suggested. “Taking the time to review your options can have a huge, positive impact on how the system runs once it’s online,” Pokorny added.
9. Include Storage As Part of Your AI Plan
With the additional insight and automation provided by AI, workers have a tool to make AI a part of their daily routine rather than something that replaces it, according to Dominic Wellington, Global IT Evangelist at Moogsoft (Opens in a new window), a provider of AI for IT operations (AIOps). Some employees may be wary of technology that can affect their job, so introducing the solution as a way to augment their daily tasks is important,” Wellington explained.
He emphasized the need for companies to be transparent about the functioning of technology to address workflow challenges. This transparency allows employees to gain an in-depth understanding of how AI enhances their role rather than replacing it, giving them a clearer picture of its impact.
10. Build With Balance
Building an AI system involves a delicate balance between meeting the technological requirements and fulfilling the research project’s objectives. Pokorny emphasizes the importance of maintaining this equilibrium throughout the design process. Before even beginning to design an AI system, it is crucial to prioritize balance as an overarching consideration. While it may seem obvious, many AI systems are developed with a narrow focus on specific aspects of achieving research goals, often overlooking the requirements and limitations of the supporting hardware and software. This approach can lead to suboptimal or dysfunctional systems that fail to achieve the desired objectives. A well-designed AI system can be created by taking a holistic approach and considering both the research goals and the technological constraints, increasing the chances of success in achieving the intended outcomes.
To achieve this balance, companies need to build in sufficient bandwidth for storage, the graphics processing unit (GPU), and networking. Security is an oft-overlooked component as well. AI by its nature requires access to broad swaths of data to do its job. Make sure that you understand what kinds of data will be involved with the project and that your usual security safeguards — encryption, virtual private networks (VPN), and anti-malware — may not be enough.
In addition to balancing the design considerations of an AI system, it is also important to allocate the overall budget effectively. While investing in research and development is crucial, it is equally essential to give resources for safeguarding against potential risks, such as power failure or other unforeseen scenarios. This can be achieved by implementing redundancies and backup systems to ensure reliability and continuity. Moreover, it is advisable to incorporate flexibility into the hardware infrastructure to allow for repurposing as user requirements evolve. This adaptability enables the system to be agile and responsive to changing needs, optimizing the utilization of resources and maximizing their long-term value. Striking the right balance between research investment, risk mitigation, and flexibility is essential for building a robust and sustainable AI system.