In this fourth article, we help accelerate the proliferation of AI systems that turn data into impact and provide enterprises with a practical framework to transform into an AI-powered enterprise. Our objective is to guide you with a roadmap of some key elements to become great at AI, and avert common missteps that lead to an academic AI strategy. Furthermore, we will discuss how these initial steps better prepare you to evolve into an AI-first enterprise and empower you to create an advantage specific to your industry vertical.
A Recap of Learning What AI is to Finding the Right Use Cases
During the previous articles, we discussed how data and machine learning are at the core of many AI systems and data science projects. In addition, we examined a variety of pertinent topics such as explaining what AI really is by reviewing the different types, capabilities, and limitations. Then we emphasized the significance of having a realistic view of AI technology.
Relevantly, we discussed how every job function could benefit from learning how to use data to create value with AI. This included processes to guide organizations with implementing project workflows, strategically finding the right use cases, avoiding common pitfalls, effectively organizing teams, and performing due diligence. Suitably, we demonstrated how leading organizations apply these principles to enhance what modern AI capabilities let them do really well. Then we provided practical questions to help enterprises determine if they are ready to adopt AI. In general, we provided some initial steps, key processes, and best practices on how to use AI as an accelerator of value.
Common Mistakes That Lead to an Academic AI Strategy
AI has created tremendous value inside the software industry and will create even more value across many other industries due to recent advancements and improvements in accessibility. For any enterprise to play a key role in generating this new value, they will have to become great at AI.
However, a vital mistake that companies make when trying to adopt AI is by hastily developing an AI strategy without a systematic process to make their company good at AI. Another miscalculation is believing that the only process required to become great at AI is to create an AI strategy. Similarly, many companies naively believe that by implementing machine learning algorithms they automatically become an AI-first enterprise. Another costly assumption is that by accumulating a large amount of data, an AI team can miraculously make it valuable.
Although creating an AI strategy is a useful step along the journey, it is more critical to ensure that your enterprise is doing the things that modern AI capabilities lets you do very well. Yet, many organizations try to implement their strategy before learning fundamental steps such as what AI really is, how to do AI projects, how to build teams, or what expertise is required for complex enterprise projects. Likewise, they over-invest in data acquisition without performing due diligence of the most high-value data suitable for their projects. They also lack the additional components that help extract maximum value from machine learning to transform their corporate strategy. As a result, companies that skip these foundational steps tend to end up with academic AI strategies that are ineffective in real-world scenarios.
To solve these issues, we highly recommend to organizations to first get their
feet wet by executing pilot projects, start building a team, and also upskill existing employees. This is a more feasible approach and will be more effective once you understand AI and how it may apply to your business. Most importantly, all levels of your organization will have a better intuition for a cohesive strategy that impacts the entire enterprise to create an advantage specific to your industry vertical.
Building AI Systems Using a Systematic Process
A few companies are taking the right approach of leveraging data and machine learning tools to solve business problems. However, most do not understand how to lay the groundwork for a successful AI system or data science project. What’s more, even fewer data science and machine learning practitioners understand the amount of work to be done between the development of a useful model to the eventual customer sustainable impact. As a result, many enterprises lack the expertise on how to strategically organize and optimize AI creation in enterprise environments. Therefore, we provide a general framework of key elements of a successful AI system as a blueprint to architect and transform into an AI-powered enterprise.
Understanding Elements of a Successful AI System
Becoming great at AI also requires understanding the underlying elements of a successful AI system, including the supporting work that will enable you to achieve a meaningful objective. Ideally, it is also important to understand modeling constraints, be able to influence stakeholders, and know how supporting systems can benefit you. Identifying constraints during project workflows and understanding pressures on engineers and business owners enables better solutions that maximize the chance of success. Also, knowing how to integrate and use supportive tools such as due diligence (technical, business and ethical), escalation paths, strategic data acquisition, telemetry, manual-overrides, and guardrails against major mistakes can significantly improve modeling capabilities.
A key objective of AI systems is to implement intelligence that evolves and improves over time based on user interaction with the system. To achieve this task, an AI system should have a meaningful objective, an intelligent experience, intelligence implementation, intelligence creation, and orchestration of intelligence. Having a meaningful objective implies knowing when to use an AI system and how to make it achieve a goal. Whereas an intelligent experience delivers effective interactions between users and the system to achieve the desired outcomes.
The implementation of intelligence refers to everything to deliver the experience such as client, service and backend; including execution, management, and optimization. Intelligence creation is the building of simple heuristics or complex machine learning models that power the system and grow over time. Moreover, orchestrating an AI system involves keeping all system elements in balance; including mistakes, risks, and misuse to achieve its objectives over its lifecycle. There are many ways to approach these conceptual challenges because they are highly interrelated. If teams can learn how to balance these tasks, then they could effectively resolve these key conceptual challenges that every AI system must address.
Important Steps Prior to Formulating Your Strategy
We have demonstrated throughout each of this series of articles that AI has unique properties which present interesting challenges, as well as unbounded abilities. Developing practical expertise across the enterprise requires learning the associated concepts along with serious engineering so you can design and build AI-powered systems that are efficient, reliable, and that best-unlock the power of machine learning and data science. After you identify the various entities and abstractions, develop a deeper understanding of machine learning and data science, and have explored patterns of successful AI projects, then you will be able to implement your AI strategy with confidence.
Using AI to create an advantage specific to your industry does not occur in isolation. Instead, it is part of a systematic process where you effectively learn how to apply modern AI capabilities to make predictions or provide insights about the world. Additionally, you learn how to combine interrelated components such as the objective, experience, implementation, intelligence, and orchestration to achieve useful results. Ultimately, you will see and understand the potential that AI can unlock in your industry vertical. Which will provide the vision for a more effective and valuable strategy to transform customer experiences and build a defensible business.
Our AI Strategy & Transformation Program
AI technology is already transforming most industries, and many more will be at risk of being disrupted.
For an organization to take full advantage of AI, it needs to be fully integrated across all departments and functions.
We believe it is possible for any enterprise to become a strong AI-driven organization by setting up the right processes.
Our AI Strategists can advise you on the critical steps necessary to successfully transform your enterprise with AI.
With our proven methodologies, you will be able to grasp AI’s far-reaching strategic implications within your organization and create value from what the business implements.
We can help you build a culture that uses data as a strategic asset to extract insights, implement a modern technology infrastructure, identify new opportunities to automate manual and repetitive tasks, improve products and services, and reorganize business processes to transform your enterprise.
Every business can benefit from this transformative technology that is fast leveraging the competitive landscape to stay ahead of your competitors, and also become much more valuable and effective.
Our Enterprise AI Transformation Process Will Guide You With:
Executing pilot projects to gain familiarity and company buy-in for future investments.
Building a centralized AI team in-house to improve long-term efficiency and drive cross-departmental value.
Providing broad AI training that gives everyone the knowledge to prepare for new roles and improve capabilities.
Developing an AI and data strategy to create the most value and build a defensible business.
Creating a strategic communication process that ensures alignment with key audiences.
Applying Ethics, Law, and Privacy in your work today.
Learn how to organize your business to do the things that modern AI capabilities allows you to do very well
Evolve to an AI-Driven Enterprise by:
Systematically executing AI projects
Developing knowledge of AI
Aligning strategic direction with AI
Building a data strategy to support AI
Creating AI communications programs for key audiences
Applying ethics, law, and privacy in AI
AI Services and Transformative Experiences
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.
Technology Services
funstematics.ai has strategic partnerships with key leading and emerging AI, open-source, commercial SaaS, cloud, data management, software, and systems engineering vendors around the world.
In addition to AI systems and research, we also provide the following services:
Data Science
Business Intelligence
Data Visualization & Exploratory Data Analysis
Big Data, Cloud, IoT, Edge, HPC, Data Warehouse
Blockchain Development
Testing as a Service, DevOps Development, Software & Systems Engineering
Microservices, Containers, Orchestration
Scientific Research & Engineering
GIS, Spatial Analysis, Remote Sensing
Custom Web Application Development, Mobile Apps - iOS, Android
Data Collection & Surveying, Strategic Data Acquisition
Project Management, Scrum & Lean Six Sigma
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
Conclusion
In this article, we discussed how to develop a systematic process to help your enterprise become great at AI. Significantly, we explained how to implement the supporting processes to do the things that modern AI allows you to do very well. We also provided useful tips to prevent you from implementing an academic AI strategy. Then we provided elements of successful AI systems to help you plan, staff, manage and deliver valuable AI projects. In addition, we highlighted the significance of laying the groundwork using a systematic process before formulating your AI strategy. Finally, we provided information on our AI services and transformative experiences so any large enterprise or startup could leverage them to become great at AI.
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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