AI tool selection for enterprises: ROI-focused decision criteria

Picture this: Two similar companies each spend $200,000 on AI coding tools for their development teams. Company A sees immediate productivity gains, developer satisfaction scores rise, and achieves measurable ROI within six months. Company B struggles with integration problems, faces security compliance issues, and eventually abandons the project after burning through budget and team patience.

The difference? Company A followed a systematic evaluation framework. Company B bought based on marketing promises and peer pressure.

This scenario plays out constantly across enterprises in 2025. S&P Global data shows that companies abandoning AI projects jumped to 42% this year, up dramatically from just 17% the year prior. The primary culprits? Unclear value and uncontrolled costs. Meanwhile, organizations with structured decision frameworks report average returns of 3.5X on their AI investments, with some achieving returns as high as 8X.

The gap between success and failure isn't about luck or timing—it's about having the right evaluation criteria before you sign vendor contracts or allocate development resources. The companies succeeding with AI aren't necessarily smarter or better funded. They're just more systematic about how they evaluate, select, and implement AI tools.

What makes this particularly challenging is that AI tool evaluation requires different criteria than traditional software procurement. You can't just check feature lists and compare pricing tiers. AI tools involve data security considerations that didn't exist with previous technologies, integration complexities that traditional software doesn't present, and ROI calculations that must account for productivity gains that are often difficult to measure immediately.

The evaluation frameworks that separate winning AI investments from failed experiments have emerged from studying hundreds of enterprise implementations over the past two years. This analysis examines the specific criteria, methodologies, and decision processes that consistently lead to successful AI tool adoption based on documented enterprise experiences and procurement studies.

Enterprise evaluation framework fundamentals from successful implementations

The foundation of successful AI tool selection lies in systematic evaluation approaches that address both immediate requirements and long-term organizational needs based on documented enterprise experiences.

Strategic alignment assessment methodology

Organizations achieving positive AI ROI consistently begin evaluation by connecting AI capabilities to specific business objectives rather than evaluating tools in isolation. This alignment assessment involves identifying measurable outcomes that AI tools can influence, establishing baseline metrics for comparison, and connecting tool capabilities to strategic business priorities.

The assessment process examines how AI tools support existing workflows versus requiring workflow modifications that could disrupt productive operations. Organizations report better outcomes when AI tools enhance current processes rather than demanding wholesale changes to established development or business practices.

Successful evaluation frameworks also consider organizational readiness for AI adoption, including technical infrastructure capabilities, team skill levels, and change management capacity. Companies that assess readiness honestly before tool selection avoid implementation challenges that often derail AI initiatives.

Strategic alignment extends to understanding how AI tool selection fits within broader technology strategies and existing vendor relationships. Organizations with coherent technology roadmaps typically achieve better integration outcomes and avoid creating isolated AI implementations that don't integrate well with existing systems.

Stakeholder requirements gathering and prioritization

Enterprise AI tool evaluation requires input from multiple organizational levels, from individual developers who will use tools daily to executives concerned with strategic outcomes and compliance requirements. Effective frameworks establish systematic approaches for gathering and prioritizing these diverse requirements.

Technical teams provide input on integration requirements, performance expectations, and workflow compatibility that directly affects daily productivity. Their assessment covers how AI tools fit with existing development environments, deployment processes, and quality assurance practices.

Business stakeholders contribute requirements around cost justification, productivity improvements, and strategic value that inform ROI calculations and budget approval processes. Their input ensures AI tool selection aligns with business priorities and success metrics.

Security and compliance teams assess AI tools against organizational data handling policies, regulatory requirements, and risk management frameworks. Their evaluation prevents selection of tools that create compliance violations or security vulnerabilities that could affect enterprise operations.

The prioritization process balances these diverse requirements while acknowledging that not all needs can be met by any single tool. Organizations that establish clear priority hierarchies typically make more effective tool selection decisions and avoid paralysis from trying to satisfy every possible requirement.

Vendor ecosystem evaluation and market analysis

Understanding the AI tool vendor landscape helps organizations make informed decisions about long-term viability, support quality, and integration capabilities that affect implementation success. Vendor evaluation extends beyond immediate tool features to consider company stability, development roadmaps, and ecosystem partnerships.

Financial stability assessment examines vendor funding, revenue trends, and business model sustainability to evaluate long-term partnership viability. Organizations investing in AI tools need confidence that vendors will maintain operations, provide ongoing support, and continue product development over multi-year implementation timelines.

Technical roadmap analysis evaluates vendor development priorities, integration capabilities, and alignment with industry standards. Organizations benefit from selecting vendors whose technical direction aligns with enterprise infrastructure evolution and industry best practice development.

Partnership and ecosystem evaluation considers how AI tools integrate with existing enterprise software, cloud platforms, and development toolchains. Vendors with strong ecosystem partnerships typically provide better integration experiences and reduce implementation complexity.

Support and service assessment examines vendor capabilities for enterprise-level support, training, and implementation assistance that organizations require for successful adoption. This evaluation includes support response times, documentation quality, and availability of professional services.

Timeline and resource planning for evaluation processes

Realistic evaluation timelines account for the complexity of enterprise decision-making processes, technical assessment requirements, and organizational approval workflows that affect AI tool selection. Organizations that underestimate evaluation time often make rushed decisions that lead to implementation problems.

Technical evaluation phases typically require 4-8 weeks for thorough assessment of integration capabilities, security compliance, and performance characteristics. This timeline includes proof-of-concept development, security reviews, and compatibility testing with existing systems.

Business evaluation processes often require 6-12 weeks for stakeholder input gathering, cost-benefit analysis, and approval workflows that involve multiple organizational levels. Organizations with complex approval processes should plan accordingly to avoid project delays.

Pilot program planning adds additional timeline considerations for real-world testing, user feedback collection, and results analysis that inform final selection decisions. Pilot programs typically require 4-6 weeks minimum to generate meaningful usage data and user experience insights.

The evaluation timeline should also account for vendor response times, demonstration scheduling, and proposal development that affect overall decision-making speed. Organizations that plan realistic timelines typically make more thorough evaluations and better final decisions.

Technical assessment criteria from enterprise deployment experiences

Technical evaluation of AI tools requires systematic assessment of capabilities that affect both immediate functionality and long-term operational success based on documented enterprise implementation experiences.

Security and compliance evaluation framework

Security assessment represents the most critical technical evaluation criteria for enterprise AI tools, as security failures can create significant organizational risks beyond project-specific impacts. The evaluation framework addresses data handling practices, access controls, and compliance with relevant regulatory requirements.

The SANS AI Security Guidelines outline comprehensive approaches for enterprise AI security evaluation, including risk-based control frameworks and governance strategies that address evolving AI-specific security challenges.

Data governance assessment examines how AI tools process, store, and transmit organizational data throughout the usage lifecycle. This evaluation covers data encryption standards, storage location controls, and data retention policies that affect compliance with organizational security policies and regulatory requirements.

Access control evaluation addresses authentication mechanisms, authorization frameworks, and integration with enterprise identity management systems. Organizations require AI tools that support existing access control policies and provide appropriate audit trails for security monitoring and compliance reporting.

Compliance framework assessment evaluates AI tools against relevant industry standards including NIST AI Risk Management Framework, ISO/IEC 42001, and industry-specific regulations that affect organizational operations. This assessment ensures AI tool selection doesn't create compliance violations that could affect business operations.

Vulnerability management evaluation examines vendor security practices, incident response capabilities, and transparency around security issues that could affect enterprise deployments. Organizations need confidence in vendor security practices and communication protocols for addressing security concerns.

Integration capability and compatibility analysis

Integration assessment evaluates how effectively AI tools connect with existing enterprise infrastructure, development toolchains, and business systems that affect implementation complexity and operational effectiveness.

API and integration standards evaluation examines available integration methods, data format compatibility, and adherence to industry standards that affect connection with existing systems. Organizations benefit from AI tools that support standard integration approaches and provide flexible connection options.

Development environment compatibility assessment addresses how AI tools integrate with existing IDEs, version control systems, and deployment pipelines that development teams use daily. This evaluation ensures AI tools enhance rather than disrupt existing development workflows and productivity patterns.

Enterprise system integration evaluation examines compatibility with authentication systems, monitoring platforms, and management tools that organizations use for infrastructure management. AI tools that integrate well with existing enterprise systems typically require less administrative overhead and provide better operational visibility.

Data source connectivity assessment evaluates AI tools' ability to connect with existing databases, file systems, and business applications that contain information relevant to AI functionality. This capability affects how effectively AI tools can leverage organizational data for improved assistance and decision-making.

Performance and scalability assessment methodologies

Performance evaluation addresses how AI tools behave under enterprise usage patterns, including response times, throughput capabilities, and resource utilization that affect user experience and operational costs.

Response time and latency testing examines AI tool performance under typical usage conditions, including network latency effects and processing delays that affect developer productivity and user satisfaction. Organizations need AI tools that provide responsive performance during normal business operations.

Scalability assessment evaluates how AI tools handle increased usage from larger teams, higher request volumes, and more complex processing requirements that accompany enterprise adoption. This evaluation helps organizations understand capacity planning requirements and potential performance limitations.

Resource utilization analysis examines CPU, memory, and network usage patterns that affect infrastructure costs and capacity planning. Organizations need to understand resource requirements for budget planning and infrastructure capacity management.

Availability and reliability testing addresses uptime expectations, fault tolerance capabilities, and disaster recovery approaches that affect business continuity. Enterprise organizations typically require high availability and clear service level agreements for business-critical tools.

Vendor technical capabilities and roadmap evaluation

Understanding vendor technical capabilities and development priorities helps organizations assess long-term tool evolution and alignment with enterprise requirements that may develop over time.

Technical expertise assessment evaluates vendor capabilities in AI research, software development, and enterprise integration that affect product quality and future development. Organizations benefit from vendors with strong technical foundations and relevant expertise in enterprise software development.

Development roadmap analysis examines vendor priorities for feature development, platform evolution, and integration capabilities that align with organizational requirements and industry trends. This assessment helps organizations understand whether vendor development direction supports long-term organizational needs.

Research and innovation evaluation considers vendor investment in AI research, industry partnerships, and technology advancement that affect long-term competitive positioning and feature development. Organizations benefit from vendors that maintain technical leadership and continue advancing their platforms.

Support and maintenance capabilities assessment examines vendor resources for ongoing product support, bug fixes, and feature enhancement that affect long-term operational success. This evaluation includes support team expertise, response time commitments, and escalation procedures for critical issues.

Cost-benefit analysis methodologies from enterprise investment studies

Understanding the financial implications of AI tool investments requires systematic approaches to cost calculation and benefit measurement that enable accurate ROI assessment based on documented enterprise experiences.

Comprehensive cost modeling approaches

Total cost of ownership calculations for AI tools extend beyond initial licensing fees to include implementation, training, maintenance, and operational costs that accumulate over the tool lifecycle. Organizations that underestimate total costs often experience budget surprises that affect project sustainability.

Direct cost components include software licensing, subscription fees, and usage-based charges that vendors specify in pricing models. However, these direct costs typically represent only 30-40% of total implementation expenses according to enterprise deployment studies.

Implementation costs cover integration development, configuration, testing, and deployment activities required to make AI tools operational within enterprise environments. These costs often exceed initial licensing fees and vary significantly based on integration complexity and organizational requirements.

Training and change management costs include user education, workflow adaptation, and organizational change activities required for successful AI tool adoption. Organizations typically underestimate these costs, which can represent 20-30% of total implementation expenses.

Ongoing operational costs include support, maintenance, monitoring, and administration activities required to maintain AI tools in production environments. These costs continue throughout the tool lifecycle and should be factored into long-term budget planning.

ROI calculation frameworks and measurement approaches

Enterprise ROI calculation for AI tools requires balancing quantifiable productivity improvements with less tangible benefits that contribute to organizational value but may be difficult to measure precisely.

Hard ROI metrics include measurable productivity gains, cost reductions, time savings, and revenue improvements that can be directly attributed to AI tool usage. These metrics provide concrete justification for AI investments and enable comparison with alternative investment opportunities.

PwC's ROI framework provides detailed methodologies for measuring both quantitative and qualitative returns from AI investments, including approaches for handling intangible benefits that contribute to organizational value.

Productivity measurement approaches focus on development velocity improvements, task automation benefits, and workflow efficiency gains that AI tools provide to users. Organizations typically measure these benefits through before-and-after comparisons of relevant productivity metrics.

Cost reduction analysis examines how AI tools reduce operational expenses through automation, error reduction, or process optimization that decreases manual effort requirements. These savings contribute directly to ROI calculations and often provide ongoing benefits.

Soft ROI considerations include employee satisfaction improvements, skill development benefits, competitive advantage gains, and strategic capability development that contribute to organizational value but may be difficult to quantify precisely. While harder to measure, these benefits often justify AI investments even when hard ROI appears marginal.

Investment timeline and payback period analysis

Understanding when AI investments begin generating returns helps organizations plan cash flow, set realistic expectations, and evaluate investment alternatives that compete for organizational resources.

Implementation timeline analysis examines the duration from initial investment to productive AI tool usage, including evaluation, procurement, implementation, and adoption phases that precede benefit realization. Organizations typically require 3-6 months from decision to measurable benefits.

Benefit realization curves describe how AI tool benefits accumulate over time, often starting slowly during adoption phases and accelerating as users develop proficiency and organizational processes adapt to AI-enhanced workflows.

Payback period calculation identifies when cumulative benefits equal total investment costs, providing a clear metric for investment evaluation. Enterprise AI tools typically achieve payback within 6-18 months for successful implementations.

Long-term value projection extends analysis beyond immediate payback to examine ongoing benefits, cost savings, and strategic value that AI tools provide over multi-year periods. This analysis helps justify investments that may appear expensive based solely on short-term returns.

Risk-adjusted return calculations and scenario analysis

Investment analysis should account for implementation risks and uncertain outcomes that could affect actual returns compared to projected benefits from AI tool adoption.

Success probability assessment examines factors that affect implementation success, including organizational readiness, technical complexity, and vendor reliability that influence whether projected benefits materialize as expected.

Scenario analysis evaluates AI tool performance under different adoption scenarios, usage patterns, and organizational conditions that could affect actual results compared to baseline projections. This analysis helps identify factors that most significantly impact investment returns.

Risk mitigation cost analysis examines additional investments in training, support, or alternative approaches that reduce implementation risks but increase total costs. Organizations often benefit from risk mitigation investments that improve success probability.

Sensitivity analysis evaluates how changes in key assumptions affect overall investment returns, helping organizations understand which factors most significantly impact project success and where to focus risk management efforts.

Implementation planning strategies from documented adoption patterns

Successful AI tool implementation requires systematic planning that addresses technical deployment, organizational change, and risk management based on documented patterns from enterprise deployments.

Phased rollout approaches and pilot program design

Organizations achieving successful AI tool adoption typically follow phased implementation approaches that enable learning, risk mitigation, and gradual organizational adaptation rather than attempting enterprise-wide deployment immediately.

Andreessen Horowitz research on enterprise AI adoption reveals that organizations with mature AI governance see 45% fewer AI-related incidents and 60% faster regulatory compliance achievements, supporting the value of systematic implementation approaches.

Pilot program design focuses on limited scope deployments that enable real-world testing while minimizing organizational risk. Effective pilots involve representative user groups, realistic usage scenarios, and measurable success criteria that inform broader deployment decisions.

Pilot selection criteria consider factors including user group characteristics, project complexity, and potential for meaningful results within pilot timelines. Organizations typically select pilot projects that can demonstrate value clearly while avoiding projects that are either too simple or too complex to provide useful insights.

Success metrics for pilot programs should address both technical performance and user experience factors that affect broader organizational adoption. These metrics typically include productivity improvements, user satisfaction scores, and technical performance indicators.

Scaling strategy development examines how to expand successful pilot programs to broader organizational deployment while maintaining benefits and avoiding common implementation pitfalls. This strategy addresses resource requirements, training approaches, and organizational change management.

Change management and organizational readiness

AI tool implementation requires organizational change management that addresses workflow modifications, skill development, and cultural adaptation necessary for successful adoption.

Readiness assessment evaluates organizational factors that affect implementation success, including technical capabilities, change management experience, and leadership support that influence adoption outcomes.

Training program development addresses both technical skills for AI tool usage and workflow adaptations required for effective integration with existing work practices. Training programs should be ongoing rather than one-time events to support continuous improvement.

Communication strategy development ensures stakeholders understand implementation objectives, timelines, and expected outcomes while addressing concerns or resistance that could affect adoption success. Effective communication includes regular updates and feedback mechanisms.

Resistance management approaches address common sources of implementation resistance including workflow changes, job security concerns, and skepticism about AI effectiveness. Organizations that address resistance proactively typically achieve better adoption outcomes.

Resource allocation and team structure planning

Successful AI tool implementation requires dedicated resources and clear accountability structures that ensure adequate attention and expertise throughout deployment phases.

Project team structure should include technical expertise for implementation, business stakeholders for requirements and adoption support, and change management capabilities for organizational aspects of deployment.

Resource commitment planning addresses both one-time implementation costs and ongoing operational requirements for support, maintenance, and continuous improvement activities that extend beyond initial deployment.

Skills assessment and development identify capability gaps that require training, hiring, or external support for successful implementation. Organizations should plan for skill development alongside tool deployment to ensure effective usage.

Vendor relationship management establishes clear communication channels, support procedures, and escalation processes that ensure effective partnership throughout implementation and ongoing operations.

Risk management and contingency planning

Implementation planning should address potential challenges and establish mitigation approaches that reduce the likelihood and impact of common implementation problems.

Technical risk assessment identifies potential integration challenges, performance issues, or compatibility problems that could affect implementation success. Mitigation approaches should be planned before deployment begins.

Organizational risk evaluation addresses change management challenges, user adoption issues, or resource availability problems that could affect implementation outcomes. Contingency planning should address likely scenarios and response approaches.

Vendor risk assessment considers potential issues with vendor support, product development, or business continuity that could affect long-term success. Organizations should have contingency plans for vendor relationship problems.

Budget and timeline risk management addresses potential cost overruns or schedule delays that could affect project viability. Risk management should include realistic contingencies and clear criteria for project continuation decisions.

Risk assessment criteria from enterprise security and compliance studies

Enterprise AI tool adoption introduces specific risks that require systematic assessment and mitigation strategies based on documented security incidents and compliance challenges from organizational implementations.

Security risk evaluation framework

Data security risks represent the most significant concern for enterprise AI tool adoption, as security breaches can create organizational liability, regulatory violations, and competitive disadvantage beyond project-specific impacts.

Data exposure assessment examines risks associated with sharing organizational code, documents, or sensitive information with AI systems that may store, process, or learn from this data. Organizations must understand data handling practices and implement appropriate controls.

Access control risks consider how AI tools integrate with organizational identity management and whether tool access can be compromised to gain unauthorized access to organizational systems or data. This assessment includes authentication security and authorization management.

Network security evaluation addresses how AI tools communicate with external services and whether these communications introduce vulnerabilities that could be exploited for broader organizational access. This includes encryption standards and network monitoring capabilities.

Supply chain security assessment examines risks associated with AI tool dependencies, third-party integrations, and vendor security practices that could affect organizational security posture through AI tool usage.

Compliance and regulatory risk analysis

Regulatory compliance risks vary by industry and jurisdiction but consistently represent significant concerns for enterprise AI tool adoption as compliance violations can result in substantial financial penalties and operational restrictions.

Industry-specific compliance assessment addresses requirements including HIPAA for healthcare organizations, SOX for publicly traded companies, and financial services regulations that affect AI tool selection and usage policies.

Data residency and sovereignty evaluation examines whether AI tools process data in locations that comply with organizational policies and regulatory requirements. This assessment becomes critical for organizations with cross-border operations or strict data location requirements.

Audit trail and documentation requirements assessment evaluates whether AI tools provide adequate logging and documentation capabilities to support compliance auditing and regulatory reporting requirements.

Privacy regulation compliance addresses requirements including GDPR, CCPA, and emerging AI-specific regulations that affect how organizations can use AI tools and what disclosures or protections they must implement.

Operational risk assessment and mitigation

Operational risks examine how AI tool adoption could affect business continuity, productivity, and organizational effectiveness beyond security and compliance considerations.

Vendor dependency risk assessment examines potential impacts of vendor business changes, service disruptions, or product discontinuation that could affect ongoing operations. Organizations should evaluate vendor stability and have contingency plans.

Integration failure risk evaluation addresses potential problems with AI tool integration that could affect existing systems or workflows. This assessment includes testing approaches and rollback procedures for implementation problems.

Performance degradation risk examines potential impacts of AI tool usage on system performance, network bandwidth, or user productivity that could affect broader organizational operations.

User adoption risk assessment addresses potential challenges with organizational acceptance of AI tools that could result in low usage rates, workflow disruption, or resistance that undermines implementation success.

Cost and budget risk management

Financial risks associated with AI tool adoption include both direct cost overruns and indirect impacts on organizational budgets and resource allocation that could affect project sustainability.

Cost escalation risk assessment examines potential for usage-based pricing models to generate unexpected costs or for implementation complexity to exceed budget projections. Organizations should establish cost monitoring and control procedures.

ROI realization risk evaluation addresses factors that could prevent AI tools from delivering expected productivity improvements or cost savings that justify investment decisions. This includes realistic expectation setting and benefit measurement.

Opportunity cost assessment considers potential impacts of AI tool investment on alternative technology investments or organizational priorities that could provide better returns or strategic value.

Sunk cost risk management addresses decision criteria for continuing or discontinuing AI tool implementations that encounter problems or fail to meet expectations. Organizations should establish clear success criteria and decision points.

Applying the decision framework: practical implementation guidance

Translating evaluation criteria into actionable decision processes requires systematic approaches that enable organizations to assess AI tools effectively and make informed selection decisions based on their specific requirements and constraints.

Evaluation scorecard development and weighting

Creating structured evaluation approaches ensures consistent assessment across different AI tools and enables objective comparison of alternatives based on organizational priorities and requirements.

Criteria weighting establishes relative importance of different evaluation factors based on organizational priorities, risk tolerance, and strategic objectives that affect AI tool selection decisions. Different organizations will weight criteria differently based on their specific circumstances.

Scoring methodology development creates consistent approaches for evaluating AI tools against established criteria, including both quantitative metrics and qualitative assessments that require subjective judgment but should follow defined evaluation standards.

Evaluation team structure ensures appropriate expertise and perspective in assessment processes while avoiding bias or incomplete evaluation that could lead to poor selection decisions. Teams should include technical, business, and security expertise.

Documentation and audit trail creation establishes records of evaluation processes, decisions, and rationale that support future reference, compliance requirements, and organizational learning from selection experiences.

Vendor evaluation and comparison methodology

Systematic vendor assessment enables organizations to evaluate AI tool providers beyond immediate product features to consider factors that affect long-term partnership success and implementation outcomes.

Vendor stability assessment examines financial health, business model sustainability, and market position that affect long-term viability and continued product development. This assessment helps avoid vendors that may discontinue products or reduce support levels.

Technical capability evaluation addresses vendor expertise in AI development, enterprise integration, and ongoing product enhancement that affects tool quality and future development alignment with organizational needs.

Support and service evaluation examines vendor capabilities for implementation assistance, ongoing support, and partnership approaches that affect implementation success and operational effectiveness.

Reference and case study analysis involves reviewing documented experiences from similar organizations that provide insights into real-world implementation outcomes and vendor partnership quality.

Pilot program design and success measurement

Effective pilot programs provide real-world evaluation data while minimizing organizational risk and resource commitment during the assessment phase of AI tool selection.

Pilot scope definition establishes realistic boundaries for pilot programs that enable meaningful evaluation while avoiding excessive complexity or resource requirements that could affect pilot success or delay decision-making.

Success criteria establishment creates measurable objectives for pilot programs that address both technical performance and organizational benefits that inform broader deployment decisions.

User selection and training ensures pilot participants can provide meaningful feedback and represent broader organizational user populations that will be affected by AI tool deployment.

Results analysis and decision-making establishes processes for evaluating pilot outcomes, incorporating feedback, and making informed decisions about broader deployment based on pilot experiences.

Decision documentation and approval processes

Formal decision processes ensure appropriate organizational review and approval while creating documentation that supports implementation planning and future reference.

Decision criteria documentation establishes clear rationale for AI tool selection based on evaluation results, organizational priorities, and risk assessment that provides foundation for implementation planning.

Stakeholder review and approval processes ensure appropriate organizational input and commitment for AI tool selection and implementation while addressing concerns or requirements from different organizational perspectives.

Implementation planning integration connects tool selection decisions with practical deployment planning that addresses resource requirements, timeline development, and organizational change management.

Contract negotiation guidance translates evaluation results into specific contract requirements including service levels, support commitments, and risk mitigation provisions that protect organizational interests.

The evaluation framework provides systematic approaches for AI tool selection that improve decision quality while reducing implementation risks and increasing the likelihood of achieving desired organizational outcomes from AI investments. Organizations that follow structured evaluation processes consistently report better results and fewer implementation challenges compared to those making selection decisions based on marketing claims or peer recommendations alone.