In recent years, it has become increasingly apparent that artificial intelligence (AI) and machine learning (ML) adoption has become more of a necessity than an option for companies to remain competitive. The benefits of adopting AI initiatives cannot be understated.
In fact, according to a Google Cloud study in partnership with MIT Technology Review, the adoption of ML results in 2x more data-driven decisions, 5x faster decision-making, and 3x faster execution.
While enterprises consider AI initiatives to be a top contender for digital transformations, there are a number of challenges preventing successful business outcomes.
According to a Forrester Consulting Thought Leadership Paper published in January 2020, 90% of firms are having difficulty scaling AI across their enterprises, with data being the main problem.
With the influx of data increasing rapidly each day, it is imperative to understand that overcoming top data challenges for AI and ML is what separates industry leaders from the rest. Though the need and want for AI/ML enablement and scalability are there, there is still a gap in AI adoption and implementation efforts.
Understanding and Underestimating AI/ML Needs
The starting point for overcoming these top data challenges is understanding AI data needs. An astounding 52% of respondents in the previously mentioned Forrester survey claimed they did not understand what their needs were regarding artificial intelligence. Obviously, this can lead to enterprises investing in the wrong technologies, or not even bothering to start at all.
It’s no secret to business leaders that AI is a critical source of competitive advantage. Yet, without a proper strategy and execution, enabling AI to help overcome pain points may just lead to further challenges.
Identifying Business Use Cases
Enabling successful AI means identifying use cases that are aligned with business objectives and can be actually implemented. Defining the right business use cases that can be improved/solved with AI/ML implementation is a key consideration for any organization. The key is for organizations to take the time to understand the impact that use cases may have.
A clear strategy when identifying business use cases, combined with success metrics, allows for organizations to truly see the value first-hand. Of course, defining business use cases can outline not only the possible impacts of the project but also any obstacles to overcome.
Artificial intelligence and machine learning solutions/tools are beneficial, but can ultimately become costly and overbearing. In fact, only 1 in 10 organizations are able to get 75% or more of their AI model prototypes into production, according to the Gartner AI in Organizations Survey.
To not waste time or resources, leaders within the business (especially Chief Information Officers) need to see through the “hype” and actually determine what the high impact areas are when adopting AI solutions. This can be done with effective analysis and strategy of business use cases.
Data quality poses one of the largest challenges in adopting AI. If the quality of data is poor, then AI investments simply cannot succeed. What’s more, is that poor data quality is costly. In fact, Gartner claims that poor data quality costs organizations an average of $12.9 million annually. Data quality also becomes a necessity when amplifying analytics for better insights and for making trusted, data-driven decisions.
The challenge that arises from data quality is also compounded by the lack of quality data to train AI systems. Lacking the appropriate data is likely to worsen as information environments become increasingly complex. Ultimately, data quality challenges in regard to AI/ML projects can lead to increased costs, timelines, and regulatory risks.
Overcoming the data quality challenge is multi-faceted, but starts with a data evaluation. That way, organizations are able to set a standard for quality across the board. Also, understanding that data quality is something to improve upon constantly only betters the implementation of AI/ML models and projects.
Strategically Overcome Top Challenges When Adopting AI
Many organizations are unsure of where to even begin with artificial intelligence and machine learning. To truly realize AI’s value, business leaders need a clear strategy that identifies use cases aligned with their business objectives. And even when implemented, it is important to understand the impact that data has on AI models. The poor quality of data can further cause hurdles in AI adoption to become massive mountains.
Here at Pandera, we understand that developing AI models and deploying at scale to service your business is only half the battle.
Our AI/ML Enablement Jumpstart is designed to identify opportunities to integrate AI/ML into your organization and realize tangible business impact before making a substantial investment. Our team will help you get started with new AI/ML initiatives with confidence through the use of GCP’s AI/ML Suite.
With a foundation deeply rooted in data engineering and advanced analytics, we have helped organizations across all industries solve their most complex business challenges through data.
To learn more about how we can assist you with your cloud transformation journey, contact us today!