1. Challenges and success stories
To finalize this chapter about harnessing the power of AI in organizations, I will share with you a few challenges or obstacles an organization may encounter on its journey to become AI-driven, along with some well-known examples of real success stories.
2. Challenges
Despite the benefits brought by building AI-driven organizations, it is common for many of them to encounter difficulties in the effective implementation of AI in their product, services, or business activities as a whole.
Oftentimes, these difficulties are closely related to one or more of the three dimensions needed to become an AI-driven organization: business, data and infrastructure, and people.
A lack of needed resources -or poor use of them- is one of the most common challenges found. Adequate resources like infrastructure with enough computing power, a team of skilled professionals, or a sufficient budget for all of these, are mandatory to overcome this challenge.
3. Challenges
Data is another critical resource to look after, but given its importance in AI-related or digital transformation in an organization, it deserves to be mentioned as a separate challenge. AI systems heavily rely on large volumes of high-quality data to train accurate and successful AI models. Accessing data that accounts for all possible scenarios can be hindered by data silos in an organization, restricted access to external data sources, or the need to collect and aggregate data from various origins. Frameworks for secure management and compliance of data must also be in place.
4. Challenges
Another challenge occurs if a company's culture is too traditional, lacking openness to adapt to the changes that AI and data-centric operations and methodologies require. It is imperative to cultivate a culture that embraces AI, fosters collaboration, and fluently adapts to constant technological changes.
5. Challenges
A lack of a shared vision and awareness among stakeholders and teams of why AI is critical to the business is another fundamental risk to mitigate.
6. Success stories: Google
There are many organizations that have faced these challenges and successfully overcome them. Here are a few examples.
Google has been a vanguard of AI innovation, but it was not free from challenges related to data quality and accessibility. To address this, they developed robust data governance frameworks and integration strategies to be able to leverage vast amounts of data effectively.
7. Success stories: Airbnb
Airbnb, the digital platform for accommodation booking, faced talent challenges in building an AI-driven organization, but they effectively overcame them by jointly investing in external talent acquisition and internal talent development through training programs to upskill employees in AI and machine learning.
8. Success stories: IBM
IBM has been very proactive in addressing ethical and regulatory issues in its AI portfolio. They established an AI ethics board to safeguard responsible AI practices, developed guidelines to mitigate biases in AI algorithm outputs, and engaged with policymakers to define AI regulations.
9. Success stories: Netflix
And our last example, the video entertainment provider Netflix, had to overcome infrastructure and resource challenges by heavily investing in cloud computing infrastructure and developing their own AI tools and platforms, including powerful personalized recommendation engines and large-scale data processing workflows.
10. Let's practice!
You nearly made it to the finish line of this insightful chapter. Let's wrap it up with some practice!