In today’s fast-paced digital landscape, the integration of artificial intelligence (AI) into business operations has become increasingly essential. However, despite the promises of AI revolutionizing industries, the reality is that a significant number of AI projects fail. According to a Gartner study, as many as 85% of AI projects don’t deliver on their intended objectives. “87% of data science projects never make it to production”, claims VentureBeat. CIO Online, on the other hand says “Generative AI readiness is shockingly low”.
A recent Forrester study commissioned by Salesforce claims that while nine out of ten leaders recognize the importance of a robust data strategy for AI success, only a third of them are implementing a unified data strategy across their organizations. So, how can organizations navigate this challenging landscape and ensure their AI initiatives succeed? The answer lies in strategic AI preparation, which encompasses various aspects ranging from data readiness to cultural, skills, infrastructure, and budget readiness.
“Forty percent of businesses unfortunately, will not exist in a meaningful way in 10 years… if they don’t change the way they accommodate to new technologies”
-John Chambers, Former CEO, Cisco Systems
The five pillars of AI Preparation help us to set the stage for the Machine Learning Development Lifecycle (MLDL) in which we build out our technical implementation of AI and NLP programs. Before you start to onboard your highly skilled team of data scientists, please rate your readiness on a scale of 1 being not prepared to 5 being more than prepared for each of the 5 pillars we are going to discuss.
The five pillars of AI Preparation help us to set the stage for the Machine Learning Development Lifecycle (MLDL) in which we build out our technical implementation of AI and NLP programs. Before you start to onboard your highly skilled team of data scientists, please rate your readiness on a scale of 1 being not prepared to 5 being more than prepared for each of the 5 pillars we are going to discuss.
Pillar 1: Data Readiness
Data readiness for an AI initiative refers to the state of preparedness of the data that will be used to train, validate, and deploy artificial intelligence models. It encompasses several factors that ensure the data is suitable, relevant, accurate, and accessible for the intended AI application.
Q1: Are You Storing Data?
Effective AI implementation starts with having access to quality data. Organizations must ensure they are actively collecting and storing relevant data that can fuel their AI initiatives. This includes customer data, operational data, and any other information pertinent to the business objectives.
Q2: Are You Warehousing Data?
Beyond just storing data, it’s crucial to have a robust data warehousing strategy in place. This involves organizing and structuring data in a manner that makes it easily accessible for AI algorithms to extract meaningful insights. A well-designed data warehouse lays the foundation for successful AI projects.
Q3: Structured vs Unstructured Data?
Organizations must grapple with the complexities of both structured and unstructured data. While structured data, such as databases and spreadsheets, are more organized and easier to analyze, unstructured data, like text documents and multimedia files, present challenges but also valuable insights. Balancing the utilization of both types of data is key to comprehensive AI readiness.
Pillar 2: Cultural readiness
Cultural readiness for an AI initiative refers to the organizational environment’s preparedness and receptiveness to adopting and integrating artificial intelligence technologies into its operations, processes, and decision-making practices. It encompasses various factors within the organizational culture that can either facilitate or hinder the successful implementation and utilization of AI.
Q1: Establish AI Literacy
Creating a culture of AI literacy across the organization is paramount. Employees at all levels should understand the basics of AI and its potential impact on their roles and the business as a whole. This helps foster a supportive environment for AI initiatives and encourages collaboration between different departments.
Q2: Educate Leadership
Leadership buy-in is crucial for the success of AI projects. Executives and decision-makers need to be educated about the benefits and limitations of AI, as well as its strategic implications for the organization. When leadership is aligned with AI objectives, it paves the way for smoother implementation and adoption.
Q3: Embrace Experimentation
Innovation thrives in environments that encourage experimentation and risk-taking. Organizations should create spaces for teams to experiment with AI technologies, even if it means embracing failure along the way. By fostering a culture of experimentation, organizations can learn and iterate faster, ultimately increasing the chances of AI success.
Pillar 3: Skills Readiness
Skills readiness for an AI initiative refers to the collective capability of individuals within an organization or a community to effectively engage with and leverage artificial intelligence (AI) technologies. It encompasses a range of competencies, knowledge, and capacities required to conceptualize, develop, implement, and maintain AI-driven projects or initiatives. Skills readiness involves both technical proficiencies related to AI tools, algorithms, and programming languages, as well as non-technical skills such as critical thinking, problem-solving, communication, and collaboration.
Q1: Upskill the User Base
AI implementation requires a workforce equipped with the necessary skills to leverage AI tools effectively. Investing in upskilling programs ensures employees have the knowledge and expertise to work with AI technologies, whether it’s data analysis, machine learning, or programming.
Q2: Technical Training Program
In addition to user-centric training, organizations should also invest in technical training programs for IT and data science teams. These programs help build specialized skills required for developing and maintaining AI infrastructure, algorithms, and models.
Pillar 4: Infrastructure Readiness
Infrastructure Readiness for an AI initiative refers to the state of preparedness of an organization’s technological, operational, and human resources to effectively support the implementation and deployment of artificial intelligence (AI) projects. It involves having the necessary hardware, software, data, processes, and expertise in place to leverage AI technologies efficiently and derive maximum value from them.
Q1: Standardize AI Infrastructure
Standardizing AI infrastructure is essential for scalability and interoperability. Organizations should invest in robust AI infrastructure that can support the diverse needs of AI applications, whether it’s cloud-based platforms, on-premises servers, or hybrid solutions.
Pillar 5: Budget Readiness
Budget Readiness for an AI initiative refers to the state of preparedness of an organization or project to allocate financial resources effectively and efficiently towards the implementation and maintenance of an artificial intelligence (AI) initiative.
Q1: Data Warehousing Costs
Data warehousing costs encompass the expenses associated with collecting, storing, and managing data. Organizations must allocate sufficient budget for data warehousing initiatives, including hardware, software, and maintenance costs.
Q2: Infrastructure Costs
Building and maintaining AI infrastructure comes with its own set of expenses, including hardware procurement, software licenses, and ongoing maintenance. Budgeting for infrastructure costs ensures organizations have the resources to support their AI projects effectively.
Q3: Development and Deployment Costs
Developing and deploying AI solutions require investment in talent, tools, and technologies. Organizations must budget for costs related to hiring data scientists, purchasing AI software, and conducting pilot projects to test and refine AI applications.
Q4: Training Costs
Training costs encompass expenses associated with upskilling employees and providing technical training programs. Organizations should budget for training initiatives to ensure their workforce is equipped to leverage AI effectively.
In conclusion, navigating the high failure rate of AI projects requires a holistic approach to readiness. McKinsey & Company calls for a “generative AI reset,” saying it will take “organizational surgery” to rewire companies for the necessary transformation. However, by addressing key pillars such as data, culture, skills, infrastructure, and budget, organizations can mitigate risks and increase the likelihood of AI success. Strategic AI preparation isn’t just about implementing the latest technologies; it’s about building a solid foundation that supports innovation and drives business outcomes. With the right preparation, organizations can harness the power of AI to gain a competitive edge in today’s digital economy.
