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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

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 ...

Amidst the threat of cyberhacks and data breaches, companies install several security measures to keep their business safely afloat. These measures aim to protect businesses, employees, and crucial data. Yet, employees perceive them as burdensome. Frustrated with complex logins, slow access, and constant security checks, workers decide to completely bypass all security set-ups ...

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Cloudbrink's Personal SASE services provide last-mile acceleration and reduction in latency

In MEAN TIME TO INSIGHT Episode 13, Shamus McGillicuddy, VP of Research, Network Infrastructure and Operations, at EMA discusses hybrid multi-cloud networking strategy ... 

In high-traffic environments, the sheer volume and unpredictable nature of network incidents can quickly overwhelm even the most skilled teams, hindering their ability to react swiftly and effectively, potentially impacting service availability and overall business performance. This is where closed-loop remediation comes into the picture: an IT management concept designed to address the escalating complexity of modern networks ...

In 2025, enterprise workflows are undergoing a seismic shift. Propelled by breakthroughs in generative AI (GenAI), large language models (LLMs), and natural language processing (NLP), a new paradigm is emerging — agentic AI. This technology is not just automating tasks; it's reimagining how organizations make decisions, engage customers, and operate at scale ...

In the early days of the cloud revolution, business leaders perceived cloud services as a means of sidelining IT organizations. IT was too slow, too expensive, or incapable of supporting new technologies. With a team of developers, line of business managers could deploy new applications and services in the cloud. IT has been fighting to retake control ever since. Today, IT is back in the driver's seat, according to new research by Enterprise Management Associates (EMA) ...

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