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Hexagon reviews business workflows, users, data, systems, integrations, constraints, risk, and evidence requirements to identify where AI may create practical value. The engagement produces a prioritized opportunity portfolio and a clear recommendation for what should be tested, planned, postponed, or rejected.
Founded in 2007
U.S.-Led Discovery
Global Engineering
300+ Software Projects
Scope confirmed around the number of workflows, teams, systems, and data sources.
Business and technical participants review opportunities, constraints, and evidence together.
Opportunities are ranked by value hypothesis, feasibility, readiness, risk, and dependency.
Recommended experiments, pilots, integrations, governance actions, and next decisions.
Leadership sees AI potential but has not agreed on the best business use cases.
Teams have a long list of ideas without a shared prioritization method.
The organization needs to understand data, system, workflow, risk, and adoption dependencies.
A pilot or vendor proposal needs independent review before more investment.
The business wants a roadmap before approving a custom AI application.
Active AI initiatives already need recurring governance and delivery decisions.
A specific approved use case is ready for dedicated design and development.
The expectation is a legal certification, guaranteed ROI, penetration test, or production implementation within the assessment.
Define the task, users, current process, decisions, delays, errors, handoffs, volume, and business owner.
Review source availability, quality, permissions, sensitivity, structure, ownership, retrieval, retention, and missing information.
Map applications, APIs, identity, documents, databases, vendor access, environments, dependencies, and operational constraints.
Consider whether the opportunity is better addressed through configuration, automation, analytics, integration, an existing AI product, or a custom AI application.
Identify data, security, privacy, legal, policy, model, vendor, human-review, safety, and operational risks that require ownership.
Define the expected operational value, baseline, measurable evidence, evaluation approach, cost considerations, and next decision.
Recommended Outcomes
Strong enough evidence and readiness for a controlled pilot or build.
Promising opportunity with unresolved workflow, data, integration, evaluation, or risk questions.
Business value may exist, but data, process, system, ownership, or governance work must happen before AI.
Limited value, high complexity, weak evidence, or a better non-AI solution is available.
IMPORTANT: The scoring supports decisions; it does not predict guaranteed financial return.
Collect the business objectives, workflows, current tools, data sources, AI ideas, vendor proposals, constraints, policies, and existing evidence. Confirm participants and assessment scope.
Review priority workflows with business and technical stakeholders. Clarify users, decisions, data, systems, risks, evidence, dependencies, and alternative solutions.
Score opportunities, document assumptions, identify dependencies, define validation or pilot actions, and provide the recommended roadmap and next decisions.
1
A structured list of reviewed opportunities with business owner, users, workflow, value hypothesis, data, systems, readiness, risk, dependencies, and current recommendation.
2
A decision view showing which opportunities should move forward, require validation, wait for dependencies, or be deprioritized.
3
A summary of data, integration, security, governance, human-oversight, adoption, vendor, and operating issues that affect the next decision.
4
Recommended validation, pilot, integration, governance, data, or custom-application actions in an agreed sequence.
5
The main conclusions, assumptions, unresolved questions, owners, and recommended next-step engagement.
Business goals and operating challenges
Candidate AI ideas, pilots, tools, and vendor proposals
Current workflow descriptions or process owners
Relevant systems, integrations, documents, and data sources
Available baseline metrics or examples of current output
Security, privacy, legal, or policy constraints already known
Representatives from business, operations, data, technology, and risk where relevant
The decisions leadership expects the assessment to support
Workflow and stakeholder review
Opportunity identification
Readiness assessment
High-level system and data review
Risk considerations
Prioritization
Roadmap and next decisions
Production application development
Detailed data remediation
Formal security testing
Legal or regulatory certification
Model training
Full enterprise architecture
Ongoing portfolio governance
Guaranteed ROI or model performance
Vendor procurement
Run a focused feasibility, data, workflow, or vendor validation before approving a build.
Use recurring advisory and governance when several active initiatives require decisions, KPI planning, risk ownership, and delivery oversight.
Move an approved opportunity into product design, development, integration, evaluation, deployment, and improvement.
It is a structured review of business workflows, users, data, systems, integrations, risk, readiness, and evidence requirements used to identify and prioritize practical AI opportunities.
No. Data quality and availability are part of the review. However, the assessment requires enough information to understand the workflow, source systems, ownership, and likely data dependencies.
No. It can define value hypotheses, baselines, measurable evidence, cost considerations, and decision criteria. Actual outcomes depend on the use case, data, implementation, users, controls, and operating environment.
Participants typically include the executive sponsor, business and workflow owners, product or operations representatives, data and technical teams, and security, legal, privacy, or compliance stakeholders where relevant.
The agreed deliverables include a structured opportunity portfolio, prioritization summary, readiness and risk findings, strategic roadmap, and executive decision summary.
Daks founded Hexagon IT Solutions in 2007 and leads business discovery and solution planning for custom software, integrations, CRM, ERP, automation, AI, data, digital transformation, and industry technology engagements. He works with clients to connect operational requirements, technology decisions, and long-term delivery planning.
Certified and trusted across leading cloud, CRM, security, and enterprise technology ecosystems.
Share the business goals, candidate use cases, current systems, data concerns, pilots, vendors, and decisions leadership needs to make. Hexagon will confirm the appropriate assessment scope and next step.
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Whether you need custom software, CRM, ERP, AI, automation, integrations, or modernization, tell us what you’re trying to improve. We’ll review your requirements and recommend the most practical next step.
300+
Software projects delivered
37+
Enterprise apps built
150+
delivery team members
10+
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