5 Ways Government Contractors Can Tackle Digital Transformation

Ronda Cilsick
CIO
Deltek

The growing adoption of efficiency-boosting technologies like artificial intelligence (AI) and machine learning (ML) helps counteract staffing shortages, rising labor costs, and talent gaps, while giving employees more time to focus on strategic projects. This trend is especially evident in the government contracting sector, where, according to Deltek's 2024 Clarity Report, 34% of GovCon leaders rank AI and ML in their top three technology investment priorities for 2024, above perennial focus areas like cybersecurity, data management and integration, business automation and cloud infrastructure.

Operating within tight profit margins and complex supply chains, government contractors face significant pressure to boost efficiency, save time, and reduce costs with automation. But with unique barriers to technology adoption, from economic constraints to regulatory hurdles, IT leaders can't rush investments in digital transformation.

Despite these challenges, digital transformation is not out of reach for this industry. By taking a measured approach and fostering a culture of innovation, IT leaders will be empowered to unlock the efficiency and cost-saving benefits of AI and ML.

Navigating Unique Hurdles to Tech Adoption

Digital transformation initiatives present challenges and considerations for any organization. However, tech adoption can be difficult for government contractors — particularly for those that are risk-averse due to the imperative for safety standards on mission critical programs, long product development lifecycles and reliance on legacy systems.

Inflation and high labor costs place additional pressure on the success of technology investments. Moreover, government contractors must navigate stringent regulatory requirements that can complicate the adoption of new technologies.

For example, managing sensitive data from government entities requires meticulous planning and execution to ensure compliance with data security standards. This complexity can lead to apprehension among leaders regarding the time and effort required to implement new software — and onboard and train both employees and external parties.

Given these constraints, it's not surprising that IT leaders who work at government contracting firms rank implementing new software systems among their top three challenges. But by understanding these obstacles, you can implement smart strategies to overcome them and pursue digital transformation.

Tips to Kickstart Your Digital Transformation Efforts

While AI plays a key role in optimizing internal processes and improving decision-making, you can't rush investments in AI — especially in the highly regulated government contracting space.

Successful digital transformation initiatives require a measured approach in which you:

1. Address cultural factors: While technology adoption is a critical aspect of digital transformation, these solutions cannot succeed without the people who implement, maintain, and provide education and training to enable their use. Effective change management and employee involvement in digital transformation can help secure employee buy-in and mitigate fears about changes in processes and technologies.

It's just as important to foster a culture of innovation in which failure is part of the learning process. Room to explore new ideas and learn from their mistakes offers space for employees to innovate and determine how AI fits into the organization's broader technology strategy.

2. Prioritize agility: IT governance is crucial to ensuring stability and compliance when integrating new technologies. But if governance is too rigid it becomes increasingly difficult to efficiently respond to market changes as technology advances.

Consider taking a "just enough" approach that focuses on implementing only the necessary amount of oversight and control to remain compliant without creating unnecessary bureaucracy. For example, instead of requiring a one-size-fits-all approval process to approve projects and digital transformation initiatives, take a tiered approach, establish clear guidelines and delegate approvals to designated teams to enable quick decision-making within those parameters.

3. Focus on data quality: AI-powered tools are only as effective as the data that trains them, making data quality a must-have foundation for any AI investment. To enhance your AI initiatives, prioritize the implementation of technologies and processes that improve data quality, like data governance frameworks and data cleaning software.

You can also increase your chances of earning leadership buy-in by identifying solutions that do more than simply automate. For example, some platforms integrate compliance management with project management and financial tools, offering a clear ROI while ensuring regulatory compliance.

4. Start small and scale: Digital transformation doesn't — and shouldn't — happen overnight. Start by identifying areas of the business that heavily depend on manual processes and could see significant improvements from technology integration.

For instance, while 58% of government contractors use enterprise resource planning (ERP) systems and accounting software, nearly the same percentage (55%) still use spreadsheets to manage financial operations. Digitizing and automating routine tasks in your financial operation enhances productivity and serves as a practical starting point to experiment with automation, paving the way for further improvements.

5. Document progress: Your ability to scale digital transformation efforts hinges on robust documentation. Make sure to set measurable goals before deploying new technology solutions so you can monitor and log progress via metrics like percentage reduction in invoice processing time or increase in on-time project delivery rates.

Use these findings to inform future decision-making and guide future initiatives. Additionally, consider asking leaders involved in these projects to share success stories and use cases across the organization to foster adoption and build confidence among employees.

AI Is Here to Stay, So Do It Right

Don't let the current hype around AI lead to rushed investments — these technologies are here to stay. It is crucial to not only prepare your data and people for AI, but also to begin with small initiatives, scale gradually, and document your progress along the way. A systematic approach to digital transformation and AI adoption can help you overcome cultural, regulatory, and technological barriers and determine how new technologies can optimize business processes and decision-making.

The result? You gain the ability to harness the full potential of AI and drive meaningful, sustainable change.

Ronda Cilsick is CIO of Deltek

Related Links

Hot Topics

The Latest

AI Confidence Is High. Readiness Is Not. What the Data Tells Us - and Why It Matters

Artificial intelligence (AI) has become the dominant force shaping enterprise data strategies. Boards expect progress. Executives expect returns. And data leaders are under pressure to prove that their organizations are "AI-ready" ...

Agentic AI, Realistic Expectations and the Future of IT Operations in 2026

Agentic AI is a major buzzword for 2026. Many tech companies are making bold promises about this technology, but many aren't grounded in reality, at least not yet. This coming year will likely be shaped by reality checks for IT teams, and progress will only come from a focus on strong foundations and disciplined execution ...

Trust, But Verify: Building Confidence in AI Through Human Oversight

AI systems are still prone to hallucinations and misjudgments ... To build the trust needed for adoption, AI must be paired with human-in-the-loop (HITL) oversight, or checkpoints where humans verify, guide, and decide what actions are taken. The balance between autonomy and accountability is what will allow AI to deliver on its promise without sacrificing human trust ...

Data Centers Plan to Reduce Reliance on Grid

More data center leaders are reducing their reliance on utility grids by investing in onsite power for rapidly scaling data centers, according to the Data Center Power Report from Bloom Energy ...

MEAN TIME TO INSIGHT Podcast - Episode 21: Agentic NetOps

In MEAN TIME TO INSIGHT Episode 21, Shamus McGillicuddy, VP of Research, Network Infrastructure and Operations, at EMA discusses AI-driven NetOps ... 

The Hidden Costs of IT Tool Sprawl

Enterprise IT has become increasingly complex and fragmented. Organizations are juggling dozens — sometimes hundreds — of different tools for endpoint management, security, app delivery, and employee experience. Each one needs its own license, its own maintenance, and its own integration. The result is a patchwork of overlapping tools, data stuck in silos, security vulnerabilities, and IT teams are spending more time managing software than actually getting work done ...

2026 Will Force Enterprises to Rethink the Cloud's "Always On" Myth

2025 was the year everybody finally saw the cracks in the foundation. If you were running production workloads, you probably lived through at least one outage you could not explain to your executives without pulling up a diagram and a whiteboard ...

Unifying Data Chaos: Effective Strategies for Modern Database Management

Data has never been more central to a greater portion of enterprise operations than it is today. From software development to marketing strategy, data has become an essential component for success. But as data use cases multiply, so too does the diversity of the data itself. This shift is pushing organizations toward increasingly complex data infrastructure ...

Enterprises Are Hitting Agentic AI Inflection Point

Enterprises are not stalling because they doubt AI, but because they cannot yet govern, validate, or safely scale autonomous systems, according to The Pulse of Agentic AI 2026, a new report from Dynatrace ...

Let's Face It: For SREs, Cost and Reliability Are Now Inseparable

For most of the cloud era, site reliability engineers (SREs) were measured by their ability to protect availability, maintain performance, and reduce the operational risk of change. Cost management was someone else's responsibility, typically finance, procurement, or a dedicated FinOps team. That separation of duties made sense when infrastructure was relatively static and cloud bills grew in predictable ways. But modern cloud-native systems don't behave that way ...

5 Ways Government Contractors Can Tackle Digital Transformation

Ronda Cilsick
CIO
Deltek

The growing adoption of efficiency-boosting technologies like artificial intelligence (AI) and machine learning (ML) helps counteract staffing shortages, rising labor costs, and talent gaps, while giving employees more time to focus on strategic projects. This trend is especially evident in the government contracting sector, where, according to Deltek's 2024 Clarity Report, 34% of GovCon leaders rank AI and ML in their top three technology investment priorities for 2024, above perennial focus areas like cybersecurity, data management and integration, business automation and cloud infrastructure.

Operating within tight profit margins and complex supply chains, government contractors face significant pressure to boost efficiency, save time, and reduce costs with automation. But with unique barriers to technology adoption, from economic constraints to regulatory hurdles, IT leaders can't rush investments in digital transformation.

Despite these challenges, digital transformation is not out of reach for this industry. By taking a measured approach and fostering a culture of innovation, IT leaders will be empowered to unlock the efficiency and cost-saving benefits of AI and ML.

Navigating Unique Hurdles to Tech Adoption

Digital transformation initiatives present challenges and considerations for any organization. However, tech adoption can be difficult for government contractors — particularly for those that are risk-averse due to the imperative for safety standards on mission critical programs, long product development lifecycles and reliance on legacy systems.

Inflation and high labor costs place additional pressure on the success of technology investments. Moreover, government contractors must navigate stringent regulatory requirements that can complicate the adoption of new technologies.

For example, managing sensitive data from government entities requires meticulous planning and execution to ensure compliance with data security standards. This complexity can lead to apprehension among leaders regarding the time and effort required to implement new software — and onboard and train both employees and external parties.

Given these constraints, it's not surprising that IT leaders who work at government contracting firms rank implementing new software systems among their top three challenges. But by understanding these obstacles, you can implement smart strategies to overcome them and pursue digital transformation.

Tips to Kickstart Your Digital Transformation Efforts

While AI plays a key role in optimizing internal processes and improving decision-making, you can't rush investments in AI — especially in the highly regulated government contracting space.

Successful digital transformation initiatives require a measured approach in which you:

1. Address cultural factors: While technology adoption is a critical aspect of digital transformation, these solutions cannot succeed without the people who implement, maintain, and provide education and training to enable their use. Effective change management and employee involvement in digital transformation can help secure employee buy-in and mitigate fears about changes in processes and technologies.

It's just as important to foster a culture of innovation in which failure is part of the learning process. Room to explore new ideas and learn from their mistakes offers space for employees to innovate and determine how AI fits into the organization's broader technology strategy.

2. Prioritize agility: IT governance is crucial to ensuring stability and compliance when integrating new technologies. But if governance is too rigid it becomes increasingly difficult to efficiently respond to market changes as technology advances.

Consider taking a "just enough" approach that focuses on implementing only the necessary amount of oversight and control to remain compliant without creating unnecessary bureaucracy. For example, instead of requiring a one-size-fits-all approval process to approve projects and digital transformation initiatives, take a tiered approach, establish clear guidelines and delegate approvals to designated teams to enable quick decision-making within those parameters.

3. Focus on data quality: AI-powered tools are only as effective as the data that trains them, making data quality a must-have foundation for any AI investment. To enhance your AI initiatives, prioritize the implementation of technologies and processes that improve data quality, like data governance frameworks and data cleaning software.

You can also increase your chances of earning leadership buy-in by identifying solutions that do more than simply automate. For example, some platforms integrate compliance management with project management and financial tools, offering a clear ROI while ensuring regulatory compliance.

4. Start small and scale: Digital transformation doesn't — and shouldn't — happen overnight. Start by identifying areas of the business that heavily depend on manual processes and could see significant improvements from technology integration.

For instance, while 58% of government contractors use enterprise resource planning (ERP) systems and accounting software, nearly the same percentage (55%) still use spreadsheets to manage financial operations. Digitizing and automating routine tasks in your financial operation enhances productivity and serves as a practical starting point to experiment with automation, paving the way for further improvements.

5. Document progress: Your ability to scale digital transformation efforts hinges on robust documentation. Make sure to set measurable goals before deploying new technology solutions so you can monitor and log progress via metrics like percentage reduction in invoice processing time or increase in on-time project delivery rates.

Use these findings to inform future decision-making and guide future initiatives. Additionally, consider asking leaders involved in these projects to share success stories and use cases across the organization to foster adoption and build confidence among employees.

AI Is Here to Stay, So Do It Right

Don't let the current hype around AI lead to rushed investments — these technologies are here to stay. It is crucial to not only prepare your data and people for AI, but also to begin with small initiatives, scale gradually, and document your progress along the way. A systematic approach to digital transformation and AI adoption can help you overcome cultural, regulatory, and technological barriers and determine how new technologies can optimize business processes and decision-making.

The result? You gain the ability to harness the full potential of AI and drive meaningful, sustainable change.

Ronda Cilsick is CIO of Deltek

Related Links

Hot Topics

The Latest

AI Confidence Is High. Readiness Is Not. What the Data Tells Us - and Why It Matters

Artificial intelligence (AI) has become the dominant force shaping enterprise data strategies. Boards expect progress. Executives expect returns. And data leaders are under pressure to prove that their organizations are "AI-ready" ...

Agentic AI, Realistic Expectations and the Future of IT Operations in 2026

Agentic AI is a major buzzword for 2026. Many tech companies are making bold promises about this technology, but many aren't grounded in reality, at least not yet. This coming year will likely be shaped by reality checks for IT teams, and progress will only come from a focus on strong foundations and disciplined execution ...

Trust, But Verify: Building Confidence in AI Through Human Oversight

AI systems are still prone to hallucinations and misjudgments ... To build the trust needed for adoption, AI must be paired with human-in-the-loop (HITL) oversight, or checkpoints where humans verify, guide, and decide what actions are taken. The balance between autonomy and accountability is what will allow AI to deliver on its promise without sacrificing human trust ...

Data Centers Plan to Reduce Reliance on Grid

More data center leaders are reducing their reliance on utility grids by investing in onsite power for rapidly scaling data centers, according to the Data Center Power Report from Bloom Energy ...

MEAN TIME TO INSIGHT Podcast - Episode 21: Agentic NetOps

In MEAN TIME TO INSIGHT Episode 21, Shamus McGillicuddy, VP of Research, Network Infrastructure and Operations, at EMA discusses AI-driven NetOps ... 

The Hidden Costs of IT Tool Sprawl

Enterprise IT has become increasingly complex and fragmented. Organizations are juggling dozens — sometimes hundreds — of different tools for endpoint management, security, app delivery, and employee experience. Each one needs its own license, its own maintenance, and its own integration. The result is a patchwork of overlapping tools, data stuck in silos, security vulnerabilities, and IT teams are spending more time managing software than actually getting work done ...

2026 Will Force Enterprises to Rethink the Cloud's "Always On" Myth

2025 was the year everybody finally saw the cracks in the foundation. If you were running production workloads, you probably lived through at least one outage you could not explain to your executives without pulling up a diagram and a whiteboard ...

Unifying Data Chaos: Effective Strategies for Modern Database Management

Data has never been more central to a greater portion of enterprise operations than it is today. From software development to marketing strategy, data has become an essential component for success. But as data use cases multiply, so too does the diversity of the data itself. This shift is pushing organizations toward increasingly complex data infrastructure ...

Enterprises Are Hitting Agentic AI Inflection Point

Enterprises are not stalling because they doubt AI, but because they cannot yet govern, validate, or safely scale autonomous systems, according to The Pulse of Agentic AI 2026, a new report from Dynatrace ...

Let's Face It: For SREs, Cost and Reliability Are Now Inseparable

For most of the cloud era, site reliability engineers (SREs) were measured by their ability to protect availability, maintain performance, and reduce the operational risk of change. Cost management was someone else's responsibility, typically finance, procurement, or a dedicated FinOps team. That separation of duties made sense when infrastructure was relatively static and cloud bills grew in predictable ways. But modern cloud-native systems don't behave that way ...