March 17, 2026
The Road to an Effective and Secure Enterprise AI Strategy
Learn how your organization can move from AI exploration to execution by aligning strategy, governance, security and infrastructure to drive measurable business value.
Several years into the era of generative AI, many organizations are still struggling to map out a strategy to guide their investments in a way that keeps data secure, engages employees around high-value use cases and maximizes business value. AI success requires more than cutting-edge technology; the most transformative initiatives feature executive sponsorship, long-term planning, deliberate security measures and rigorous governance practices that put guardrails on the use of sensitive data. Infrastructure is a key component of any effective AI strategy. Organizations must assess the readiness of their existing infrastructure, map out model dependencies, optimize costs and plan for long-term scalability. Ultimately, the planning of an AI strategy is simple compared with the execution. Many organizations turn to a trusted partner like CDW for expertise and hands-on assistance to supplement the efforts of busy internal teams.
Several years into the era of generative AI, many organizations are still struggling to map out a strategy to guide their investments in a way that keeps data secure, engages employees around high-value use cases and maximizes business value. AI success requires more than cutting-edge technology; the most transformative initiatives feature executive sponsorship, long-term planning, deliberate security measures and rigorous governance practices that put guardrails on the use of sensitive data. Infrastructure is a key component of any effective AI strategy. Organizations must assess the readiness of their existing infrastructure, map out model dependencies, optimize costs and plan for long-term scalability. Ultimately, the planning of an AI strategy is simple compared with the execution. Many organizations turn to a trusted partner like CDW for expertise and hands-on assistance to supplement the efforts of busy internal teams.
Since public large language models burst onto the scene in late 2022, organizations in nearly every industry have effectively become AI laboratories, cycling through countless pilot projects in search of “silver bullet” applications that will improve productivity, increase revenue and automate significant portions of labor-intensive tasks.
Many of these early initiatives were launched by individual business units exploring emerging tools such as generative AI assistants, copilots or customer-service automation platforms. While these projects helped teams understand the potential of AI, they have not always connected to broader enterprise priorities or infrastructure planning. Even amid the breathless hype surrounding the technology, it seems as though a new headline-making report comes out every few months showing that relatively few AI pilots ever become production-ready, enterprise-class tools. In particular, a 2025 Massachusetts Institute of Technology report turned heads with the claim that only about 5% of AI pilots make it into production and create any measurable value.
The problem, most agree, lies not with the technology itself, but rather with a lack of strategy. “Technology doesn’t fix misalignment,” Forbes wrote on the heels of the MIT report. “It amplifies it. Automating a flawed process only helps you do the wrong thing faster. Add AI, and you risk runaway damage before anyone realizes what’s happening. MIT’s research echoes this: Most enterprise tools fail not because of the underlying models, but because they don’t adapt, don’t retain feedback and don’t fit daily workflows.”
Executive sponsorship and cross-functional ownership can ensure that AI initiatives are aligned with business outcomes. Without the support of executives, AI investments are at risk of losing funding as soon as something goes wrong. Just as concerning, users may resist adopting AI if they know that leadership does not consider it a top priority. By contrast, when leaders establish a clear vision for how AI will transform work across the enterprise, they create the conditions needed to push the technology from pilot to production, where it can demonstrate measurable business impact.
Structured executive engagement can build this buy-in and ensure that leadership teams are fully aligned on their organization’s AI strategy. Facilitated workshops help surface the infrastructure constraints, organizational silos and competitive priorities that often derail poorly planned AI initiatives. By giving leaders the time, space and training they need to set objectives, identify high-value use cases and assess organizational readiness, these workshops can help create the clarity needed to craft and implement a successful AI strategy.
40%
The percentage of agentic AI projects that Gartner predicts will be canceled by the end of 2027 due to escalating costs, unclear business value or inadequate risk controls
Source: gartner.com, “Gartner Predicts Over 40% of Agentic AI Projects Will Be Canceled by End of 2027,” June 25, 2025
Assess your AI readiness and align initiatives to your business priorities.
Since public large language models burst onto the scene in late 2022, organizations in nearly every industry have effectively become AI laboratories, cycling through countless pilot projects in search of “silver bullet” applications that will improve productivity, increase revenue and automate significant portions of labor-intensive tasks.
Many of these early initiatives were launched by individual business units exploring emerging tools such as generative AI assistants, copilots or customer-service automation platforms. While these projects helped teams understand the potential of AI, they have not always connected to broader enterprise priorities or infrastructure planning. Even amid the breathless hype surrounding the technology, it seems as though a new headline-making report comes out every few months showing that relatively few AI pilots ever become production-ready, enterprise-class tools. In particular, a 2025 Massachusetts Institute of Technology report turned heads with the claim that only about 5% of AI pilots make it into production and create any measurable value.
The problem, most agree, lies not with the technology itself, but rather with a lack of strategy. “Technology doesn’t fix misalignment,” Forbes wrote on the heels of the MIT report. “It amplifies it. Automating a flawed process only helps you do the wrong thing faster. Add AI, and you risk runaway damage before anyone realizes what’s happening. MIT’s research echoes this: Most enterprise tools fail not because of the underlying models, but because they don’t adapt, don’t retain feedback and don’t fit daily workflows.”
Executive sponsorship and cross-functional ownership can ensure that AI initiatives are aligned with business outcomes. Without the support of executives, AI investments are at risk of losing funding as soon as something goes wrong. Just as concerning, users may resist adopting AI if they know that leadership does not consider it a top priority. By contrast, when leaders establish a clear vision for how AI will transform work across the enterprise, they create the conditions needed to push the technology from pilot to production, where it can demonstrate measurable business impact.
Structured executive engagement can build this buy-in and ensure that leadership teams are fully aligned on their organization’s AI strategy. Facilitated workshops help surface the infrastructure constraints, organizational silos and competitive priorities that often derail poorly planned AI initiatives. By giving leaders the time, space and training they need to set objectives, identify high-value use cases and assess organizational readiness, these workshops can help create the clarity needed to craft and implement a successful AI strategy.
Assess your AI readiness and align initiatives to your business priorities.
AI Strategy Reality Check
49%
The percentage of AI decision-makers who have seen a positive bottom-line ROI from their generative AI investments
Source: forrester.com, “Areas of Positive ROI From Generative AI Are Now on Par With Predictive AI,” Nov. 6, 2024
91%
The percentage of organizations that demonstrate a high level of AI maturity, in line with the Gartner AI Maturity Model, who have appointed dedicated AI leaders
Source: gartner.com, “Gartner Survey Finds 45% of Organizations With High AI Maturity Keep AI Projects Operational for at Least Three Years,” June 30, 2025
48%
The percentage of AI projects that make it from pilot to production — a process that takes an average of eight months
Source: gartner.com, “Gartner Survey Finds Generative AI Is Now the Most Frequently Deployed AI Solution in Organizations,” May 7, 2024
AI Strategy Reality Check
49%
The percentage of AI decision-makers who have seen a positive bottom-line ROI from their generative AI investments
Source: forrester.com, “Areas of Positive ROI From Generative AI Are Now on Par With Predictive AI,” Nov. 6, 2024
91%
The percentage of organizations that demonstrate a high level of AI maturity, in line with the Gartner AI Maturity Model, who have appointed dedicated AI leaders
Source: gartner.com, “Gartner Survey Finds 45% of Organizations With High AI Maturity Keep AI Projects Operational for at Least Three Years,” June 30, 2025
48%
The percentage of AI projects that make it from pilot to production — a process that takes an average of eight months
Source: gartner.com, “Gartner Survey Finds Generative AI Is Now the Most Frequently Deployed AI Solution in Organizations,” May 7, 2024
- ESTABLISHING AI GOVERNANCE
- AI WORKLOADS AND INFRASTRUCTURE
- MEASURING AI STRATEGY
In the first two or three years of the rise of generative AI, it was fairly common to see leaders issue sweeping mandates about rapid AI adoption across their organizations, with little guidance about which tools to use or what tasks to automate, let alone a pause to establish and implement governance and security practices. This “move fast and break things” ethos may yield results for some Silicon Valley startups, but it can be extremely dangerous when applied to AI in fields like healthcare, government, finance and law. For organizations defining their AI strategies, it is critical that leaders take time to set guardrails around data quality and privacy, cybersecurity and the responsible use of AI tools before scaling adoption.
DATA INTEGRITY AND PRIVACY: In short order, “AI is only as good as your data” has become a nearly universal IT truism. “As a leader, it’s critical to recognize that data — its quality, diversity, governance and operational pipelines — will make or break your generative AI initiatives,” writes Tom Godden, an AWS executive in residence. “Investing in robust data practices is not an optional nice-to-have, but a core requisite for unlocking generative AI’s full potential while mitigating risks.”
This means ensuring that all data used to train AI models is valid and current; otherwise, organizations might make multimillion-dollar decisions on the basis of AI “hallucinations.” Just as important, organizations must ensure that their sensitive data is not used to train public models and must also prevent internal AI tools from providing information to unauthorized users. For example, without governance guardrails, an AI tool trained on confidential HR records could include that information in its outputs for anyone to see.
SECURITY BY DESIGN: Cybersecurity leaders are playing catch-up to protect their organizations against threats that almost no one had heard of five years ago. To build a foundation for AI security, organizations must protect AI assets and data through version control, integrity checks and role-based access controls. They also need to harden themselves against AI-specific threats such as prompt injection and model poisoning.
Methods like anomaly detection can reduce the risk of malicious changes that could sabotage or introduce intentional bias into models, while monitoring for unusual query patterns can help prevent model extraction and theft. For generative systems, prompt-hardening and content filters can prevent models from exfiltrating secrets or executing untrusted instructions. Before new AI tools are deployed, IT teams should conduct “red teaming” exercises specifically designed to trick AI tools into bypassing safety filters or leaking proprietary secrets, and then make adjustments based on the results.
RESPONSIBLE AI: Leaders need to establish specific, enforceable policy frameworks to ensure that AI is used responsibly and ethically across the organization. Some companies have attempted to ban AI entirely, but this is largely impractical, as many employees simply end up using consumer-grade “shadow AI” on their personal devices, creating much more risk than a well-governed enterprise AI program.
One important area of responsible AI policy is explainability. While AI tools are well known for the “black box” problem that limits visibility into their decision-making, organizations must be able to explain the thought process behind important decisions, especially those that could leave them vulnerable to legal or regulatory exposure. Rather than a static list of AI guidelines, organizations need to implement dynamic policy frameworks that are integrated into their DevOps workflows. This may include setting up “human in the loop” checkpoints for high-stakes decisions, as well as establishing an AI ethics committee to coordinate between legal, IT and business units.
Before organizations can fully realize the promise of AI, leaders must first gain a comprehensive understanding of what they’re running, where they’re running it and what infrastructure gaps need to be closed to support their AI strategies. It's important not only to build AI environments for today, but also to create flexible ecosystems that adapt with changing technologies and business needs.
WORKLOAD VISIBILITY: During pilot phases, organizations can gain critical insight into how AI tools perform under real-world conditions by monitoring user activity and infrastructure usage. Without this visibility, teams risk underestimating resource requirements or creating isolated AI “islands” that operate independently across departments, often with significant redundancy. By creating a shared, centralized AI environment, leaders can reduce overlap and complexity, resulting in better efficiency and less wasted spending. They can also collect valuable usage analytics that help them model production-scale deployments with greater confidence.
MODEL DEPENDENCIES: Within many organizations, IT and business leaders devote a great deal of time and energy early on to deciding which model to use. However, the reality is that AI models are still evolving rapidly, meaning that organizations need to build out infrastructure that is adaptable enough to support multiple models over time. Modern AI models do not operate in isolation but rather depend on a complex web of data pipelines, application programming interfaces and third-party services that must remain available for AI applications to function as intended. Mapping these dependencies is essential to understanding both operational risk and infrastructure requirements. If a critical data feed is interrupted, for example, downstream applications may fail in ways that are difficult to diagnose without a clear dependency map.
INFRASTRUCTURE READINESS: Legacy IT infrastructure was not designed with the performance needs of AI applications in mind. To support production-level scale, organizations must assess their existing infrastructure, identify the capacity and performance needed to support their AI initiatives, and rapidly deploy new infrastructure to close the gap. To more accurately determine their needs, many organizations begin with lab-based demonstrations using synthetic data and then move on to proofs of concept that incorporate their own data. Next, organizations often launch limited pilots — with careful monitoring of resource consumption — before using this data to inform a broader rollout.
COST OPTIMIZATION: During the first stages of the generative AI era, leaders largely prioritized keeping up with their peers rather than insisting on a detailed cost-benefit analysis to support the purchase of every new tool and service. As a result, few organizations have yet seen a positive return on their AI investments. That dynamic is changing somewhat, with many executives paying closer attention to costs now that their organizations have already engaged in numerous AI pilots. Infrastructure clarity allows teams to better optimize AI spending by helping to uncover instances of noncritical training or oversized clusters.
SCALABILITY PLANNING: There likely isn’t a single enterprise IT or business leader who would say that their organization’s AI environment is fully mature. In the coming years, pilot projects will expand into enterprisewide deployment, and new AI agents and other emerging solutions will spark entirely new rounds of experimentation and testing. Even for leading organizations, today’s AI infrastructure will likely be inadequate to support tomorrow’s needs. Scalability planning requires leaders to anticipate growth trajectories and build flexibility into their infrastructure architectures from the start. This means selecting platforms and vendors that can scale without prohibitive cost increases, designing data architectures that can accommodate growing model complexity, and establishing governance frameworks that remain applicable as the number of models and users expands.
Click Below To Continue Reading
Governance as an Enabler
While some see governance as a hindrance to innovation, it can actually accelerate AI adoption. When organizations establish clear data standards, security controls and oversight frameworks early on, they reduce rework, avoid costly missteps and build the trust required to scale their initiatives. Done right, governance can streamline decision-making and smooth the path from AI experimentation to true business impact.
BETTER DATA: By taking time to implement governance of their data pipelines, organizations can ensure both that models are trained on trustworthy information and that confidential data doesn’t make its way into AI outputs.
LESS RISK: Embedding governance and security protects organizations from emerging AI threats such as model theft, prompt injection and data manipulation, helping to prevent potential regulatory exposure and costly remediation.
INCREASED TRUST: Users and leaders will be more confident in AI tools that are governed by clear accountability, policy enforcement and monitoring practices.
SUSTAINABLE SCALABILITY: With guardrails in place, organizations are more likely to expand AI applications beyond initial pilots. A governed, adaptable foundation allows organizations to switch models, add new workloads and scale across departments.
When measurable goals, ongoing optimization and elite expertise guide execution, organizations can turn their AI strategies into practical IT programs that create tangible business value.
CREATE AN EXECUTION ROADMAP: Even the best AI strategy is worth little without disciplined execution. To create an effective AI execution roadmap, leaders should begin by defining both their organization’s “as is” environment and desired “to be” state, and then identify the gaps in infrastructure, data, talent and other factors that still need to be bridged. An execution roadmap should also outline high-impact use cases, prioritized by their real-world potential to deliver business value, rather than on trends or novelty. Effective roadmaps will clearly define how tools move from lab demonstrations to proofs of concept, then to limited-user pilots and ultimately to full deployment, with careful thought given to governance and security measures. To the extent possible, organizations should define success metrics early on, and also establish executive sponsorship and cross-functional ownership to prevent AI tools from becoming siloed or abandoned.
TRACK MEASURABLE OUTCOMES: As AI matures, business leaders are increasingly demanding hard numbers that reflect a positive ROI. While technical performance or user adoption may have satisfied stakeholders during the initial rush to implement generative AI tools, leaders today are largely looking for evidence that these tools are helping them to achieve important business outcomes. These goals may include reductions in operating costs, improved customer service scores, increased employee productivity or faster time-to-market. By measuring usage patterns and resource consumption during pilots, leaders can get an early idea of how impactful and cost-efficient AI tools are, helping to determine whether a given application warrants broader deployment. Measurement should extend beyond initial launch, incorporating ongoing performance tracking and user feedback to ensure the tools continue delivering value as conditions evolve.
OPTIMIZE OVER TIME: To some, it may feel as though AI has already consumed the entire business world, but the truth is that most organizations are still in the very beginning of their AI journeys. Early pilots provide valuable data about user behavior, workload performance and infrastructure demands, but those insights must inform continuous improvement as AI applications move into production. As adoption grows, organizations should regularly revisit their model choices, their resource allocation and their governance and security practices to ensure their AI initiatives remain aligned with business needs and the current technology landscape. By being intentional about continuing to optimize over time, organizations can sustain performance gains, reduce inefficiencies and prevent drift from their original objectives.
LEVERAGE TRUSTED PARTNERS: Successfully scaling AI initiatives, from pilot to production, demands specialized expertise in infrastructure, software tools and security. No matter how enthusiastic leaders are about their AI efforts, and no matter how willing their workforce is to embrace and adopt new solutions, these initiatives will fall flat without experts who can help define the initial vision, keep projects on track and identify opportunities and areas for growth. A trusted partner like CDW can provide organizations with the expertise they need to craft, execute and optimize AI strategies that create value, both today and well into the future. CDW’s comprehensive suite of services helps businesses address core elements such as leadership alignment, use cases, data quality and infrastructure readiness, leading to sustained innovation and measurable success.
- ESTABLISHING AI GOVERNANCE
- AI WORKLOADS AND INFRASTRUCTURE
- MEASURING AI STRATEGY
In the first two or three years of the rise of generative AI, it was fairly common to see leaders issue sweeping mandates about rapid AI adoption across their organizations, with little guidance about which tools to use or what tasks to automate, let alone a pause to establish and implement governance and security practices. This “move fast and break things” ethos may yield results for some Silicon Valley startups, but it can be extremely dangerous when applied to AI in fields like healthcare, government, finance and law. For organizations defining their AI strategies, it is critical that leaders take time to set guardrails around data quality and privacy, cybersecurity and the responsible use of AI tools before scaling adoption.
DATA INTEGRITY AND PRIVACY: In short order, “AI is only as good as your data” has become a nearly universal IT truism. “As a leader, it’s critical to recognize that data — its quality, diversity, governance and operational pipelines — will make or break your generative AI initiatives,” writes Tom Godden, an AWS executive in residence. “Investing in robust data practices is not an optional nice-to-have, but a core requisite for unlocking generative AI’s full potential while mitigating risks.”
This means ensuring that all data used to train AI models is valid and current; otherwise, organizations might make multimillion-dollar decisions on the basis of AI “hallucinations.” Just as important, organizations must ensure that their sensitive data is not used to train public models and must also prevent internal AI tools from providing information to unauthorized users. For example, without governance guardrails, an AI tool trained on confidential HR records could include that information in its outputs for anyone to see.
SECURITY BY DESIGN: Cybersecurity leaders are playing catch-up to protect their organizations against threats that almost no one had heard of five years ago. To build a foundation for AI security, organizations must protect AI assets and data through version control, integrity checks and role-based access controls. They also need to harden themselves against AI-specific threats such as prompt injection and model poisoning.
Methods like anomaly detection can reduce the risk of malicious changes that could sabotage or introduce intentional bias into models, while monitoring for unusual query patterns can help prevent model extraction and theft. For generative systems, prompt-hardening and content filters can prevent models from exfiltrating secrets or executing untrusted instructions. Before new AI tools are deployed, IT teams should conduct “red teaming” exercises specifically designed to trick AI tools into bypassing safety filters or leaking proprietary secrets, and then make adjustments based on the results.
RESPONSIBLE AI: Leaders need to establish specific, enforceable policy frameworks to ensure that AI is used responsibly and ethically across the organization. Some companies have attempted to ban AI entirely, but this is largely impractical, as many employees simply end up using consumer-grade “shadow AI” on their personal devices, creating much more risk than a well-governed enterprise AI program.
One important area of responsible AI policy is explainability. While AI tools are well known for the “black box” problem that limits visibility into their decision-making, organizations must be able to explain the thought process behind important decisions, especially those that could leave them vulnerable to legal or regulatory exposure. Rather than a static list of AI guidelines, organizations need to implement dynamic policy frameworks that are integrated into their DevOps workflows. This may include setting up “human in the loop” checkpoints for high-stakes decisions, as well as establishing an AI ethics committee to coordinate between legal, IT and business units.
Before organizations can fully realize the promise of AI, leaders must first gain a comprehensive understanding of what they’re running, where they’re running it and what infrastructure gaps need to be closed to support their AI strategies. It's important not only to build AI environments for today, but also to create flexible ecosystems that adapt with changing technologies and business needs.
WORKLOAD VISIBILITY: During pilot phases, organizations can gain critical insight into how AI tools perform under real-world conditions by monitoring user activity and infrastructure usage. Without this visibility, teams risk underestimating resource requirements or creating isolated AI “islands” that operate independently across departments, often with significant redundancy. By creating a shared, centralized AI environment, leaders can reduce overlap and complexity, resulting in better efficiency and less wasted spending. They can also collect valuable usage analytics that help them model production-scale deployments with greater confidence.
MODEL DEPENDENCIES: Within many organizations, IT and business leaders devote a great deal of time and energy early on to deciding which model to use. However, the reality is that AI models are still evolving rapidly, meaning that organizations need to build out infrastructure that is adaptable enough to support multiple models over time. Modern AI models do not operate in isolation but rather depend on a complex web of data pipelines, application programming interfaces and third-party services that must remain available for AI applications to function as intended. Mapping these dependencies is essential to understanding both operational risk and infrastructure requirements. If a critical data feed is interrupted, for example, downstream applications may fail in ways that are difficult to diagnose without a clear dependency map.
INFRASTRUCTURE READINESS: Legacy IT infrastructure was not designed with the performance needs of AI applications in mind. To support production-level scale, organizations must assess their existing infrastructure, identify the capacity and performance needed to support their AI initiatives, and rapidly deploy new infrastructure to close the gap. To more accurately determine their needs, many organizations begin with lab-based demonstrations using synthetic data and then move on to proofs of concept that incorporate their own data. Next, organizations often launch limited pilots — with careful monitoring of resource consumption — before using this data to inform a broader rollout.
COST OPTIMIZATION: During the first stages of the generative AI era, leaders largely prioritized keeping up with their peers rather than insisting on a detailed cost-benefit analysis to support the purchase of every new tool and service. As a result, few organizations have yet seen a positive return on their AI investments. That dynamic is changing somewhat, with many executives paying closer attention to costs now that their organizations have already engaged in numerous AI pilots. Infrastructure clarity allows teams to better optimize AI spending by helping to uncover instances of noncritical training or oversized clusters.
SCALABILITY PLANNING: There likely isn’t a single enterprise IT or business leader who would say that their organization’s AI environment is fully mature. In the coming years, pilot projects will expand into enterprisewide deployment, and new AI agents and other emerging solutions will spark entirely new rounds of experimentation and testing. Even for leading organizations, today’s AI infrastructure will likely be inadequate to support tomorrow’s needs. Scalability planning requires leaders to anticipate growth trajectories and build flexibility into their infrastructure architectures from the start. This means selecting platforms and vendors that can scale without prohibitive cost increases, designing data architectures that can accommodate growing model complexity, and establishing governance frameworks that remain applicable as the number of models and users expands.
Click Below To Continue Reading
Governance as an Enabler
While some see governance as a hindrance to innovation, it can actually accelerate AI adoption. When organizations establish clear data standards, security controls and oversight frameworks early on, they reduce rework, avoid costly missteps and build the trust required to scale their initiatives. Done right, governance can streamline decision-making and smooth the path from AI experimentation to true business impact.
BETTER DATA: By taking time to implement governance of their data pipelines, organizations can ensure both that models are trained on trustworthy information and that confidential data doesn’t make its way into AI outputs.
LESS RISK: Embedding governance and security protects organizations from emerging AI threats such as model theft, prompt injection and data manipulation, helping to prevent potential regulatory exposure and costly remediation.
INCREASED TRUST: Users and leaders will be more confident in AI tools that are governed by clear accountability, policy enforcement and monitoring practices.
SUSTAINABLE SCALABILITY: With guardrails in place, organizations are more likely to expand AI applications beyond initial pilots. A governed, adaptable foundation allows organizations to switch models, add new workloads and scale across departments.
When measurable goals, ongoing optimization and elite expertise guide execution, organizations can turn their AI strategies into practical IT programs that create tangible business value.
CREATE AN EXECUTION ROADMAP: Even the best AI strategy is worth little without disciplined execution. To create an effective AI execution roadmap, leaders should begin by defining both their organization’s “as is” environment and desired “to be” state, and then identify the gaps in infrastructure, data, talent and other factors that still need to be bridged. An execution roadmap should also outline high-impact use cases, prioritized by their real-world potential to deliver business value, rather than on trends or novelty. Effective roadmaps will clearly define how tools move from lab demonstrations to proofs of concept, then to limited-user pilots and ultimately to full deployment, with careful thought given to governance and security measures. To the extent possible, organizations should define success metrics early on, and also establish executive sponsorship and cross-functional ownership to prevent AI tools from becoming siloed or abandoned.
TRACK MEASURABLE OUTCOMES: As AI matures, business leaders are increasingly demanding hard numbers that reflect a positive ROI. While technical performance or user adoption may have satisfied stakeholders during the initial rush to implement generative AI tools, leaders today are largely looking for evidence that these tools are helping them to achieve important business outcomes. These goals may include reductions in operating costs, improved customer service scores, increased employee productivity or faster time-to-market. By measuring usage patterns and resource consumption during pilots, leaders can get an early idea of how impactful and cost-efficient AI tools are, helping to determine whether a given application warrants broader deployment. Measurement should extend beyond initial launch, incorporating ongoing performance tracking and user feedback to ensure the tools continue delivering value as conditions evolve.
OPTIMIZE OVER TIME: To some, it may feel as though AI has already consumed the entire business world, but the truth is that most organizations are still in the very beginning of their AI journeys. Early pilots provide valuable data about user behavior, workload performance and infrastructure demands, but those insights must inform continuous improvement as AI applications move into production. As adoption grows, organizations should regularly revisit their model choices, their resource allocation and their governance and security practices to ensure their AI initiatives remain aligned with business needs and the current technology landscape. By being intentional about continuing to optimize over time, organizations can sustain performance gains, reduce inefficiencies and prevent drift from their original objectives.
LEVERAGE TRUSTED PARTNERS: Successfully scaling AI initiatives, from pilot to production, demands specialized expertise in infrastructure, software tools and security. No matter how enthusiastic leaders are about their AI efforts, and no matter how willing their workforce is to embrace and adopt new solutions, these initiatives will fall flat without experts who can help define the initial vision, keep projects on track and identify opportunities and areas for growth. A trusted partner like CDW can provide organizations with the expertise they need to craft, execute and optimize AI strategies that create value, both today and well into the future. CDW’s comprehensive suite of services helps businesses address core elements such as leadership alignment, use cases, data quality and infrastructure readiness, leading to sustained innovation and measurable success.
Scott Davis
Enterprise Architect
