Companies of all sizes and across all verticals continue to embrace artificial intelligence (AI) and machine learning (ML) for myriad reasons. They're eager to leverage AI for big data analytics to identify business trends and become more innovative, while also improving services and products. Companies are also using AI to automate sales processes, marketing programs and customer service initiatives with the common goal of increasing revenue. But the unfortunate reality is that 85% of AI and machine learning projects fail to deliver, and only 53% of projects make it from the prototype to production. Nevertheless, according to a recent IDC Spending Guide, spending on artificial intelligence in the United States will grow to $120 billion by 2025, representing growth of 20% or more.

Companies should consider a multi-faceted approach to recruited artificial intelligence (AI) and machine language (ML) talent. The race to hire artificial intelligence and machine language talent is more competitive than ever. Enterprise executives are demanding greater automation in all sectors of business and that means hiring and retraining. Is there a sweeping answer to the AI talent challenge? No, but there seem to be plenty of narrow fixes.

Can businesses trust decisions that artificial intelligence and machine learning are churning out in increasingly larger numbers? Those decisions need more checks and balances -- IT leaders and professionals have to ensure that AI is as fair, unbiased, and as accurate as possible. This means more training and greater investments in data platforms. A new survey of IT executives conducted by ZDNet found that companies need more data engineers, data scientists, and developers to deliver on these goals. The survey confirmed that AI and ML initiatives are front and center at most enterprises.

Can businesses trust decisions that artificial intelligence and machine learning are churning out in increasingly larger numbers? Those decisions need more checks and balances -- IT leaders and professionals have to ensure that AI is as fair, unbiased, and as accurate as possible. This means more training and greater investments in data platforms. A new survey of IT executives conducted by ZDNet found that companies need more data engineers, data scientists, and developers to deliver on these goals. The survey confirmed that AI and ML initiatives are front and center at most enterprises.

When it comes to artificial intelligence (AI) and machine learning (ML) projects, the biggest challenge for CXOs isn't necessarily deployment, but rather, managing these initiatives. For example, what do you anticipate your AI/ML budget will look like? What business areas are you applying AI/ML in? How knowledgeable is your upper management about AI/ML? Sometimes even determining the manager of managing initiatives can become an issue.

A new study commissioned by Trifacta is shining the light on the costs of poor data quality, particularly for organizations implementing AI initiatives. The study found that dirty and disorganized data are linked to AI projects that take longer, are more expensive, and do not deliver the anticipated results. As more firms ramp up AI initiatives, the consequences of poor data quality are expected to grow. The relatively sorry state of data quality is not a new phenomenon. Ever since humans started recording events, we've had to deal with errors.

The popularity of AI and ML have wide-reaching effects on your enterprise. Here are three important trends driven by AI to look out for next year. As the need for additional AI applications grows, businesses will need to invest in technologies that help them accelerate the data science process. However, implementing and optimizing machine learning models is only part of the data science challenge. In fact, the vast majority of the work that data scientists must perform is often associated with the tasks that preceded the selection and optimization of ML models such as feature engineering -- the heart of data science.