How First-class AI Enterprises Become More Effective and Valuable Using AI
In this second article, we will dive deeper into addressing more of the symptoms of an academic AI strategy to help good companies evolve into great AI-first enterprises. Our goal is to help companies gain better intuition of the distinguishing factors that enable first-class AI enterprises to create an advantage specific to their industry vertical. Then we will discuss why it is essential for every job function to learn how to use data to create value with AI technology. In addition, we will provide some key steps that any enterprise could take either using in-house expertise, or a partner to architect your company to achieve maximum value from modern AI capabilities.
Common Misconceptions of AI
We will begin by addressing one of the major misconceptions that any company can implement machine learning algorithms and automatically become an AI-first enterprise. This is a key indicator of an academic AI strategy because machine learning requires additional components to extract maximum value and transform your corporate strategy.
Another misconception is the assumption that by accumulating a large amount of data, an AI team can miraculously make it valuable. This is not always true, so a best practice is not to over-invest in data acquisition unless your AI team has performed due diligence of the most high-value data suitable for your projects.
Enhancing Modern AI Capabilities
A key distinguishing factor of first-class AI companies is that they implement additional processes to enhance what modern AI capabilities makes possible for them to do well. Some of these key processes include acquiring data strategically, implementing unified big data warehouses, pervasively spotting tasks suitable for automation, creating value iteratively with cross-functional teams, and ensuring all job functions are data-savvy. There may be additional components unique to different industries. However, we will discuss how implementing these key processes with your machine learning or data science projects can help you use AI technology as an accelerator of value.
Developing Strategic Data Assets
In today’s digitally-powered society, data and machine learning are the two main drivers of value for first-class AI enterprises. The rise of big data and large neural network algorithms have empowered many companies to achieve state-of-the-art performance and drive business value. Data is extremely important in building valuable AI systems, so we recommend that enterprises learn how to acquire data strategically. To build a running AI system that automatically maps some input A to output B for a given task, an AI engineer or team can acquire data through manual labeling, observing user or machine behaviors, downloading free open data from websites, or forming strategic partnerships for your specific use case.
During this data acquisition process, it is important not to misuse data by assuming all of your data will be valuable without performing due diligence with AI experts and business stakeholders. To avoid this AI pitfall, every company should ensure that business stakeholders collaborate with AI and data engineering experts early in the data acquisition process. This will allow teams to receive feedback on what types of data to collect, and the IT infrastructure needed to support the AI systems. Early interplay between AI teams and business experts can help identify data that is valuable, and prevent you from accumulating data that is not valuable for your AI applications or data science projects.
Another method that leading AI enterprises use to acquire strategic data assets is by launching free products that collect valuable data that can be monetized on different projects. These acquisition strategies could develop a positive AI feedback loop where more valuable data is collected to improve machine learning performance. This results in AI-powered platform advantages and additional users that provide new data to the cycle to generate even more value. Most importantly, these data acquisition strategies can help enterprises use AI as an accelerator of value, and also create a defensible business model.
See the Whole Picture
Once your company has developed effective methods to strategically acquire valuable data, then it is important to implement a process to handle messy data and data silos for your projects. For machine learning to produce accurate outputs, it is critical that it learns from clean, representative, and unbiased training data. Therefore, data engineering teams will need to solve big data problems such as information overload, disparate data, mislabeled data, insufficient data, missing data, and data governance. Additionally, it is also important to learn how to deal with multiple types of data such as numbers, text, images, video, and audio to create dynamic multimodal AI systems.
After learning how to effectively acquire and clean data for your projects, another important step to consider is building a unified big data warehouse. This process will help your AI engineers handle data silos and make it easier to pull together all data sources that are valuable for your AI systems or data science projects. Furthermore, a unified data warehouse allows the company to build a single source of the truth to support business intelligence activities and enterprise decision making. Lastly, we recommend that all enterprises learn about AI implications on society, and be prepared to handle data collection issues, data regulations, and other ethical concerns that may arise to create a positive impact on the world.
Identifying Automation Opportunities
In addition to thinking through how to get data and building centralized data repositories, another distinguishing element of great AI companies is having the ability to pervasively spot automation opportunities. This process requires a broad knowledge of AI capabilities from both AI teams and business domain experts to ensure you execute feasible and valuable projects. For example, you may have a manual process for visually inspecting products with defects. A knowledgeable AI team would ideally collect data with both input and output mappings, and recommend you insert a supervised computer vision algorithm that performs an automated visual inspection of the task.
Having the ability to quickly identify and execute on automation opportunities can reduce costs, improve quality, and enable employees to create value in other areas. However, few companies have enough AI expertise to identify these opportunities, so outsourcing projects and providing in-house training is advantageous for being able to work iteratively and execute AI projects. Also, it is very common to outsource pilot projects to partners to gain momentum faster, and quickly develop in-house capabilities to build sequences of AI solutions.
Data is Impacting Every Job Function
As our world becomes increasingly digitized, modern technology empowers us to connect even better with it. The increased digitization of society means that every job function and industry are being vastly transformed by data. Consequently, it is imperative that enterprises learn how to adopt AI tools like data science and machine learning to accelerate value creation and build a defensible business model. Two ways to fast-track your adoption of AI are to partner with an expert company or invest in broad AI training so every job function learns how to augment their capabilities.
AI technology has the ability to improve every job function by optimizing processes, automating tasks, and transforming customer experiences. It can also be a huge accelerator to grow faster, so upskilling employees for new roles in the AI era is a significant investment for most leading enterprises. The key roles driving most of this value are data scientists and AI engineers. These new roles are helping enterprises strategically acquire valuable data to build running AI systems, and analyze data to extract actionable insights to optimize decisions. In addition, providing broad AI training will allow every level of your organization to understand how AI interacts with their roles. More importantly, investing in training programs will develop a foundation for in-house AI capabilities so all roles can collaborate, be more effective, generate new value across the enterprise, and augment your corporate strategy.
There are many training resources available today such as MOOCs and open source frameworks to develop data science and machine learning skills. However, many companies still struggle with developing practical AI knowledge or hiring skilled talent. This may be due to a lack of practical resources and extensive time commitments required by most training programs. To help solve this problem, we have developed an innovative training program that can help you gain practical skills using less time and resources.
Enterprise AI and Data Science Training Courses
There is currently a shortage of properly trained AI talent, and unfortunately, many companies find it difficult to hire good AI practitioners.
Our goal in these courses is to provide you with the fundamental techniques that you can use to integrate state-of-the-art AI capabilities into your enterprise and applications.
After completing these courses, you will have a deep understanding of how to set the technical direction for your AI project.
You will also develop in-house AI capabilities and talent to strategically execute on projects that deliver real value to your enterprise.
Follow our proven training track to provide broad AI knowledge to executives, business leaders, technical teams and product owners, or we can develop a customized curriculum for your team.
What We Do
funstematics.ai is an AI services and research company that provides AI-powered solutions and transformative experiences for enterprises.
We Create Value in Two Key Ways:
1. AI services and products that help you produce running AI systems that automate tasks, and data science projects that optimize business processes and extract actionable insights for decisions.
2. Secondly, our AI transformation process for enterprises provide a roadmap to evolve into an AI-first company by developing pilots to gain momentum faster, creating a modern AI and data strategy, performing training, building in-house capabilities, aligning stakeholders, and considering ethics.
We are unique in that we provide all of the key AI application areas that a vertical company would provide such as computer vision, natural language processing, speech recognition, reinforcement learning, generative modeling, network science, GIS, and recommender systems for a variety of tasks or problems. Furthermore, our research is focused on creating value by developing brand new applications of AI using multimodal systems.
Get Started on this Fun and Exciting Journey into the AI Era Today
If you are interested in learning how you can apply this transformative technology to build valuable applications and lead your enterprise into the AI era today, contact our sales team to get started. www.funstematics.ai
In this article, we discussed why machine learning alone is not sufficient in transforming into a first-class AI enterprise. Then we explained why it is important to have a targeted data acquisition process. We also provided some of the distinguishing elements that enable leading companies to be more effective. Additionally, we discussed some practical training options to help you on your journey. The next articles will provide solutions to many other issues we have seen throughout the industry to help more enterprises achieve success using this powerful technology.
Is there a specific challenge or interesting topic you would like us to cover in a future article? Then leave a comment below.
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