Build Your AI Capability with Dicker Data

The Dicker Data AI Framework is a structured approach that helps partners guide customers from initial AI exploration through to deployment, adoption and ongoing management. It brings together eight foundations of AI success: readiness, compliance, governance, security, integration, training and adoption, model access and lifecycle management. 

How Dicker Data supports partners: We help you assess each customer’s environment, identify capability gaps and bring together the technologies, expertise and services required to turn AI opportunities into measurable customer outcomes. 

Start by defining the customer’s desired business outcome, what success looks like and how return on investment will be measured. Then assess whether the organisation has both the appetite for AI and the capacity to support it across data, infrastructure, security, governance and skills. This helps identify gaps, prioritise action and establish the foundations for AI value creation.

How Dicker Data supports partners: Our AI Framework and readiness assessment help you evaluate customer appetite and capacity, identify priority actions and build a practical pathway towards measurable AI outcomes.

Customer AI Readiness Matrix:

AI value creation occurs when organisations combine strong executive and business commitment with the data, infrastructure, security, governance and skills required to support AI initiatives.

A customer is ready to progress with AI when they have a clearly defined use case and sufficient foundations across data, infrastructure, security, governance, licensing and skills. An AI readiness assessment identifies where the customer is prepared to move forward, where gaps remain and what should be addressed first. 

How Dicker Data supports partners: Through AI Accelerate, we help partners assess AI readiness and turn the findings into prioritised customer opportunities. 

An AI readiness assessment should review the customer’s business objectives, intended use cases, data quality and accessibility, infrastructure, cybersecurity, governance, compliance requirements, licensing and internal skills. It should produce practical priorities rather than simply assigning the organisation a readiness score. 

How Dicker Data supports partners: We help partners evaluate these interconnected areas and identify the technologies, services and expertise required to move customers from exploration towards deployment. 

AI systems can access sensitive business, employee and customer information. Compliance helps organisations understand how data can be accessed, processed, stored and used while meeting relevant privacy, regulatory and industry requirements. Considering compliance early also reduces the risk of controls needing to be retrofitted later. 

How Dicker Data supports partners: We help you navigate data classification, privacy, security and compliance considerations and connect customers with appropriate vendor technologies and specialist expertise. 

AI governance refers to the policies, responsibilities, controls and review processes that guide how AI is selected, deployed and used. Effective governance defines who can use AI, what information it can access, how outputs are reviewed and who remains accountable for decisions. 

How Dicker Data supports partners: We help partners guide customers towards governance frameworks, responsible AI practices and supporting technologies that align AI use with business policies and risk requirements. 

Organisations should secure the identities, devices, applications, infrastructure, models and data connected to their AI environments. This includes controlling access, protecting sensitive information, monitoring activity and reviewing security controls throughout the AI lifecycle. 

How Dicker Data supports partners: We provide access to a broad cybersecurity ecosystem and specialist guidance to help you design AI solutions with identity, data protection, monitoring and cyber resilience built in. 

Data is ready for AI when it is relevant to the intended use case, sufficiently accurate, accessible to authorised users and systems, and appropriately classified and governed. Fragmented, outdated or poorly controlled information can limit the accuracy and reliability of AI-generated results. Poorly managed data can also increase the risk of users gaining unintended access to sensitive information, creating potential security, privacy and compliance concerns.

How Dicker Data supports partners: We help you assess customer data foundations and select the platforms, integration tools and architectures required to make information accessible, governed and ready for AI. 

AI delivers more relevant outcomes when it can securely access reliable information from the systems and workflows where work occurs. Integration connects AI platforms with applications, data sources and business processes, enabling AI-generated insights to support meaningful actions. 

How Dicker Data supports partners: We help partners connect data, platforms and services across multi-vendor environments to create secure, scalable and AI-ready foundations. 

The infrastructure required depends on the customer’s use case, data requirements, performance expectations and preferred deployment model. AI workloads may require accelerated compute, scalable storage, secure networking, cloud services or edge capabilities to support model access, training and inferencing. Depending on customer needs, these capabilities can be delivered through on-premises infrastructure, cloud platforms, or hybrid environments that combine both.

How Dicker Data supports partners: We provide access to the technologies, expertise and vendor ecosystem required to deliver AI solutions across on-premises, hybrid and cloud environments, helping you align the right infrastructure strategy to each customer's technical, operational and business requirements.

An AI PC or Copilot+ PC may be appropriate when a customer wants to support modern AI experiences, improve performance or run compatible AI capabilities locally on the device. The right device will depend on the user’s role, workloads, security requirements, applications and expected AI use cases. 

How Dicker Data supports partners: We help you evaluate customer requirements and select appropriate AI-ready devices, form factors and deployment models across leading technology brands. 

AI can help employees work more efficiently by reducing time spent on repetitive tasks, finding and summarising information, generating content, analysing data and supporting day-to-day decision making. Productivity-focused AI solutions such as Microsoft Copilot can help users draft documents, summarise meetings, analyse spreadsheets and retrieve organisational knowledge, while tools such as Adobe Firefly can accelerate the creation of marketing, creative and digital content. The greatest value comes from identifying high-volume, low-value tasks where AI can augment employees and help them make better use of business information and expertise.

How Dicker Data supports partners: We help you identify productivity and knowledge work use cases, assess organisational readiness, and select the right AI solutions across our vendor ecosystem. From Microsoft Copilot and Adobe Firefly through to supporting infrastructure, security and adoption services, we help partners deliver secure AI experiences that drive measurable business outcomes.

Partners can validate an AI solution before deployment by testing the use case, data requirements, architecture, security controls and expected outcomes in a proof-of-concept (POC) environment. This helps identify technical gaps, refine solution design, assess performance and demonstrate business value before progressing to production. Running a POC also provides customers with greater confidence that the proposed solution aligns to their operational, security and business requirements.

How Dicker Data supports partners: We provide partners with access to dedicated AI proof-of-concept environments, including the Dell Technologies AI Pod and Cisco AI Pod, enabling them to test, validate and demonstrate AI solutions before deployment. Backed by specialist expertise, these purpose-built environments help partners explore architectures, evaluate performance, reduce deployment risk and build stronger customer business cases without the upfront investment of creating their own AI lab.

Partners can create a repeatable AI offering by defining target customer needs, standardising readiness discussions, focusing on practical use cases and developing consistent pathways from assessment through to deployment and support. Training, proof-of-concept resources and specialist partnerships can help turn individual engagements into a scalable go-to-market approach. 

How Dicker Data supports partners: AI Accelerate helps you strengthen your skills, prioritise opportunities, validate solutions and access expertise as you build a more repeatable AI practice. 

AI lifecycle management is the ongoing process of monitoring, governing, securing and optimising an AI solution after deployment. It includes reviewing performance, access, data quality, user adoption, governance controls and changing business requirements to ensure the solution continues to deliver value. 

How Dicker Data supports partners: We help partners support customers beyond initial deployment by connecting them with the technologies, vendor expertise and services required for ongoing monitoring, optimisation and improvement.