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The State of AI Development and Operations in 2019

Mark Coleman
Dotscience

The use of AI is booming across the modern enterprise. In fact, according to Gartner's 2019 CIO Survey, the number of enterprises implementing AI grew 270% in the past four years and tripled in the past year. However, many enterprises will be unable to realize the full potential of their initiatives until they find more efficient means of tracking data, code, models and metrics across the entire AI lifecycle.

To better understand the AI maturity of businesses, Dotscience conducted a survey of 500 industry professionals in its inaugural State of Development and Operations of AI Applications 2019 report.

Research findings indicate that although enterprises are dedicating significant time and resources towards their AI deployments, many data science and ML teams don't have the adequate tools needed to properly collaborate on, build and deploy AI models efficiently.

AI Goes Mainstream

AI has moved beyond the experimentation stage and is now seen as a critical and impactful function for many businesses. Enterprises are becoming increasingly reliant on AI for its ability to deliver greater operational efficiency, streamline complex business processes, and support cost control and profit potential. This is evidenced by the survey results, which indicate that the top three drivers of AI adoption are efficiency gains (47%), growth initiatives (46%) and digital transformation (44%). Furthermore, over 88% of respondents at organizations where AI is in production indicated that AI has either been impactful or highly impactful to their company's competitive advantage. The exponential growth of AI's value and influence is also reflected in the large investments organizations are making in AI. Nearly a third of respondents (30%) are budgeting between 1 and 10 million dollars for AI tools, platforms and services.

Unfortunately, it's not all rainbows and sunshine in the world of enterprise AI. The study also found that despite this level of financial commitment, data science and ML teams continue to experience issues, including duplicating their work (33%), rewriting models after team members leave (28%), justifying the value of their projects to the wider business (27%), and slow and unpredictable AI projects (25%).

Manual Tools and Processes

Despite providing an impactful competitive advantage for enterprises, AI deployments today are largely slow and inefficient. The manual tools and processes primarily in use to operationalize ML and AI don't support the scaling and governance demanded of many AI initiatives.

The top two ways that ML engineers and data scientists collaborate with each other are by using a manually updated shared spreadsheet for metrics (44%) and sitting in the same office and working closely together (38%). These methods of collaboration ultimately disrupt efficiency and limit AI's potential. Machine learning has many moving parts, and teams require version control for their training and test data, their code and their environment, as well as metrics and hyperparameters in order to collaborate efficiently. Survey findings show that over 35% of organizations don't use any version control for their training and test data. However, of those who don't currently have any version control, over 60% would like to.

These limitations are compounded by the fact that nearly 90% of respondents either manually track model provenance (a complete record of all the steps taken to create an AI model) or do not track provenance at all. And of those that manually track model provenance, more than half (52%) do their tracking in a spreadsheet or wiki, a cumbersome and error-prone approach.

Challenges in Scaling AI Initiatives

Despite significant investment in AI, many companies are still struggling to stabilize and scale their AI initiatives. The manual tools and processes being used by many for AI model development are insufficient and do not support the required scaling and governance.

While 63% of businesses reported they are spending between $500,000 and $10 million on their AI efforts, 61% of respondents continue to experience a variety of operational challenges. This is evidenced by the fact that 64% of organizations deploying AI said that it is taking between 7 to 18 months to get their AI workloads from idea into production, illustrating the slow, unpredictable nature of AI projects today. Meanwhile, for nearly another 20%, the anticipated timeline is 19+ months to production.

DevOps Like It's 1999

The challenges faced by data science and ML teams today are reminiscent of the same challenges facing software engineers in the late 1990s. Then came DevOps, which transformed the way software engineers deliver applications by making it possible to collaborate, test and deliver software continuously.

With ML and AI projects today, collaboration is even more challenging when compared to basic software engineering. Normal software development tools focus on versions or commits of code whereas ML has many more moving parts. ML teams require version control for both training and test data, their code and their environment, as well as metrics and hyperparameters for each training run.

While ML and AI are understood as powerful technologies with the potential to reinvent the global economy, operationalizing AI still remains a major hurdle for many organizations. To simplify, accelerate and control every stage of the AI model lifecycle, the same DevOps-like principles of collaboration, fast feedback and continuous delivery should be applied to AI. Only then can enterprises realize the full potential of their AI deployments across the organization.

Mark Coleman is VP of Product and Marketing at Dotscience

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The State of AI Development and Operations in 2019

Mark Coleman
Dotscience

The use of AI is booming across the modern enterprise. In fact, according to Gartner's 2019 CIO Survey, the number of enterprises implementing AI grew 270% in the past four years and tripled in the past year. However, many enterprises will be unable to realize the full potential of their initiatives until they find more efficient means of tracking data, code, models and metrics across the entire AI lifecycle.

To better understand the AI maturity of businesses, Dotscience conducted a survey of 500 industry professionals in its inaugural State of Development and Operations of AI Applications 2019 report.

Research findings indicate that although enterprises are dedicating significant time and resources towards their AI deployments, many data science and ML teams don't have the adequate tools needed to properly collaborate on, build and deploy AI models efficiently.

AI Goes Mainstream

AI has moved beyond the experimentation stage and is now seen as a critical and impactful function for many businesses. Enterprises are becoming increasingly reliant on AI for its ability to deliver greater operational efficiency, streamline complex business processes, and support cost control and profit potential. This is evidenced by the survey results, which indicate that the top three drivers of AI adoption are efficiency gains (47%), growth initiatives (46%) and digital transformation (44%). Furthermore, over 88% of respondents at organizations where AI is in production indicated that AI has either been impactful or highly impactful to their company's competitive advantage. The exponential growth of AI's value and influence is also reflected in the large investments organizations are making in AI. Nearly a third of respondents (30%) are budgeting between 1 and 10 million dollars for AI tools, platforms and services.

Unfortunately, it's not all rainbows and sunshine in the world of enterprise AI. The study also found that despite this level of financial commitment, data science and ML teams continue to experience issues, including duplicating their work (33%), rewriting models after team members leave (28%), justifying the value of their projects to the wider business (27%), and slow and unpredictable AI projects (25%).

Manual Tools and Processes

Despite providing an impactful competitive advantage for enterprises, AI deployments today are largely slow and inefficient. The manual tools and processes primarily in use to operationalize ML and AI don't support the scaling and governance demanded of many AI initiatives.

The top two ways that ML engineers and data scientists collaborate with each other are by using a manually updated shared spreadsheet for metrics (44%) and sitting in the same office and working closely together (38%). These methods of collaboration ultimately disrupt efficiency and limit AI's potential. Machine learning has many moving parts, and teams require version control for their training and test data, their code and their environment, as well as metrics and hyperparameters in order to collaborate efficiently. Survey findings show that over 35% of organizations don't use any version control for their training and test data. However, of those who don't currently have any version control, over 60% would like to.

These limitations are compounded by the fact that nearly 90% of respondents either manually track model provenance (a complete record of all the steps taken to create an AI model) or do not track provenance at all. And of those that manually track model provenance, more than half (52%) do their tracking in a spreadsheet or wiki, a cumbersome and error-prone approach.

Challenges in Scaling AI Initiatives

Despite significant investment in AI, many companies are still struggling to stabilize and scale their AI initiatives. The manual tools and processes being used by many for AI model development are insufficient and do not support the required scaling and governance.

While 63% of businesses reported they are spending between $500,000 and $10 million on their AI efforts, 61% of respondents continue to experience a variety of operational challenges. This is evidenced by the fact that 64% of organizations deploying AI said that it is taking between 7 to 18 months to get their AI workloads from idea into production, illustrating the slow, unpredictable nature of AI projects today. Meanwhile, for nearly another 20%, the anticipated timeline is 19+ months to production.

DevOps Like It's 1999

The challenges faced by data science and ML teams today are reminiscent of the same challenges facing software engineers in the late 1990s. Then came DevOps, which transformed the way software engineers deliver applications by making it possible to collaborate, test and deliver software continuously.

With ML and AI projects today, collaboration is even more challenging when compared to basic software engineering. Normal software development tools focus on versions or commits of code whereas ML has many more moving parts. ML teams require version control for both training and test data, their code and their environment, as well as metrics and hyperparameters for each training run.

While ML and AI are understood as powerful technologies with the potential to reinvent the global economy, operationalizing AI still remains a major hurdle for many organizations. To simplify, accelerate and control every stage of the AI model lifecycle, the same DevOps-like principles of collaboration, fast feedback and continuous delivery should be applied to AI. Only then can enterprises realize the full potential of their AI deployments across the organization.

Mark Coleman is VP of Product and Marketing at Dotscience

Hot Topics

The Latest

2020 was the equivalent of a wedding with a top-shelf open bar. As businesses scrambled to adjust to remote work, digital transformation accelerated at breakneck speed. New software categories emerged overnight. Tech stacks ballooned with all sorts of SaaS apps solving ALL the problems — often with little oversight or long-term integration planning, and yes frequently a lot of duplicated functionality ... But now the music's faded. The lights are on. Everyone from the CIO to the CFO is checking the bill. Welcome to the Great SaaS Hangover ...

Regardless of OpenShift being a scalable and flexible software, it can be a pain to monitor since complete visibility into the underlying operations is not guaranteed ... To effectively monitor an OpenShift environment, IT administrators should focus on these five key elements and their associated metrics ...

An overwhelming majority of IT leaders (95%) believe the upcoming wave of AI-powered digital transformation is set to be the most impactful and intensive seen thus far, according to The Science of Productivity: AI, Adoption, And Employee Experience, a new report from Nexthink ...

Overall outage frequency and the general level of reported severity continue to decline, according to the Outage Analysis 2025 from Uptime Institute. However, cyber security incidents are on the rise and often have severe, lasting impacts ...

In March, New Relic published the State of Observability for Media and Entertainment Report to share insights, data, and analysis into the adoption and business value of observability across the media and entertainment industry. Here are six key takeaways from the report ...

Regardless of their scale, business decisions often take time, effort, and a lot of back-and-forth discussion to reach any sort of actionable conclusion ... Any means of streamlining this process and getting from complex problems to optimal solutions more efficiently and reliably is key. How can organizations optimize their decision-making to save time and reduce excess effort from those involved? ...

As enterprises accelerate their cloud adoption strategies, CIOs are routinely exceeding their cloud budgets — a concern that's about to face additional pressure from an unexpected direction: uncertainty over semiconductor tariffs. The CIO Cloud Trends Survey & Report from Azul reveals the extent continued cloud investment despite cost overruns, and how organizations are attempting to bring spending under control ...

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According to Auvik's 2025 IT Trends Report, 60% of IT professionals feel at least moderately burned out on the job, with 43% stating that their workload is contributing to work stress. At the same time, many IT professionals are naming AI and machine learning as key areas they'd most like to upskill ...

Businesses that face downtime or outages risk financial and reputational damage, as well as reducing partner, shareholder, and customer trust. One of the major challenges that enterprises face is implementing a robust business continuity plan. What's the solution? The answer may lie in disaster recovery tactics such as truly immutable storage and regular disaster recovery testing ...

IT spending is expected to jump nearly 10% in 2025, and organizations are now facing pressure to manage costs without slowing down critical functions like observability. To meet the challenge, leaders are turning to smarter, more cost effective business strategies. Enter stage right: OpenTelemetry, the missing piece of the puzzle that is no longer just an option but rather a strategic advantage ...