AI Strategy Consulting Services: The 6 Success Pillars

AI itself is changing business models and business strategies but in order to profit from AI (and not just experiment with it) companies need a clear plan - an AI strategy. Recent data from McKinsey shows that over 80% of companies now use AI in at least one business function, yet many organisations still can’t extract tangible value from their GenAI use. This disconnect highlights a critical truth: implementing AI successfully requires more than mere technology adoption - it demands a comprehensive AI strategy framework that aligns technical capabilities with real business outcomes and long-term value creation.

At Dreamix we've spent almost two decades helping enterprises transform their business operations through custom software development. As a leading provider for custom AI development and AI/ML consulting services across aviation, manufacturing, healthcare, and transportation sectors, we've witnessed firsthand how AI strategy consulting can revolutionise business operations when implemented with precision and expertise.

This comprehensive guide will walk you through an AI strategy blueprint designed to help you move your enterprise from initial AI vision to successful implementation. Drawing on our extensive experience helping clients navigate the complexities of AI adoption, we'll provide actionable insights that bridge the gap between technological potential and business value.

What are AI strategy consulting services?

AI strategy consulting services are high-level B2B offerings aiming to help businesses the complexity of the AI-driven landscape. By outlining how an organisation can use AI technology and achieve tangible ROI, AI consulting directly aligns all AI-directed initiatives to business objectives such as improving efficiency, enhancing customer experience, or driving innovation.

These foundations, however, include reliable data infrastructure, skilled AI talent, scalable technologies, and strong AI governance to manage risks, ethics, and ensure compliance. A well-crafted AI strategy doesn't operate in isolation; it complements and builds upon a company’s broader digital transformation and data strategy, turning raw data into actionable insights through AI-driven automation and business intelligence.

The diagram below illustrates the two complementary approaches to AI strategy consulting: top-down and bottom-up. In the top-down approach, organisational strategy is the key driver. Broad strategic goals are established first, which then determine priority areas to ultimately define specific AI focus areas and practical applications. This creates a direct line from high-level business vision to concrete AI implementation

Alternatively, in the bottom-up approach the process starts with identifying specific use cases at the operational level, which are then organised into AI themes/domains, and ultimately prioritised based on how they support strategic business priorities. However, notice the bidirectional connections between the strategic priorities and AI themes, suggesting that there should be ongoing alignment and feedback between business, AI strategy and AI capabilities.

Why every business needs an AI strategy now

The race to adopt AI is intensifying - and the latest data confirms it. The chart shows that as of 2024, nearly 80% of surveyed organisations (see chart below) now use AI in at least one business function, with a steep rise in adoption over the past year alone. The surge in generative AI (GenAI) use, nearly matching traditional AI adoption, signals a tipping point: AI is rapidly becoming embedded across core operations.

Many businesses already deploy AI in three, four, or even five functions, from marketing and sales to supply chain optimisation and customer service. In such a competitive environment, companies that delay investing in AI risk falling behind, missing out on cost-saving efficiencies and entirely new business opportunities. But as the technology with the most transformative potential since the cloud, AI demands more than scattered experiments. It requires a clear, enterprise-wide AI strategy. Think of it as the "why" and “how” behind every investment, aligning use cases with business goals, and ensuring sustainable, scalable impact.

Without further ado, let’s see the 6 core pillars of a successful AI strategy for businesses.

1. Strategic vision and business alignment

The journey toward effective AI strategy implementation begins with a clear strategic vision aligned with your business objectives. This critical first step establishes the foundation for all subsequent activities.

As a first step, think how AI is capable of supporting your specific business goals and KPIs. Gartner’s AI roadmap also states that organisations must establish a clear steps strategy that defines how AI will create business value before implementing technology solutions. Business leaders will need to identify concrete business challenges that AI can help solve, whether it's reducing operational costs, enhancing customer experiences, or creating new revenue streams.

Here are the most common components companies need to consider to ensure their AI initiatives are aligned with their business vision:

• Set clear business objectives: Define what success looks like and how AI will contribute to strategic goals.
• Prioritise use cases: Focus on high-impact, feasible AI initiatives that deliver measurable business value.
• Ensure executive sponsorship: Make sure you have a leadership buy-in to drive alignment, resource allocation, and accountability.
• KPIs and measurable outcomes: Set performance metrics to track AI’s impact on core business goals over time.

At Dreamix, we help partners develop robust business cases for AI initiatives by quantifying potential benefits, estimating implementation costs, and calculating expected ROI. 

2. Data readiness and architecture

AI data readiness assessment

Before embarking on implementation, it's essential to assess your organisation's data readiness for AI adoption. Recently, we’ve published our AI Readiness guide explaining that being AI-ready requires combining six critical areas: a long-term strategy beyond the hype, data foundation, cultural shift, infrastructure readiness assessment, governance and regulation and the talent element. 

There’s a widespread belief that data scientists spend a huge amount of their time just cleaning and preparing data rather than on actual AI analysis and modeling. Although numbers can vary depending among companies and projects, messy and low-quality data is in fact one of the massive bottlenecks to AI adoption. In fact, Gartner claims that, on average, poor data quality costs organisations at least $12.9 million per year. Below are the most essential steps companies preparing for AI implementation need to be aware of in terms of data readiness:

• Data audit & documentation: Conduct a thorough inventory of existing data across business departments, identifying sources, formats, and collection methods.
• Quality & consistency evaluation: Assess data for temporal gaps, inconsistencies, and variations in completeness that could impact AI effectiveness.
• Standardised collection protocols: Implement uniform methods for data gathering to ensure consistent quality moving forward.
• Accessibility & integration focus: Develop systems that make data readily available while maintaining appropriate security controls.
• Governance & metadata management: Establish clear policies for data handling alongside systems to track sources, transformations, and quality metrics.

At Dreamix, our data engineers work closely with domain experts to incorporate business context into data preparation, enhancing its relevance and utility for AI models.

Architecture readiness

Moving beyond the data level, a company’s technological architecture plays a pivotal role in determining the success of AI/ML consulting. Architecture itself is the technical backbone that supports data collection, processing, storage, model training, and deployment at scale. Without a solid architecture in place, even the most promising AI use cases can stall due to performance bottlenecks, integration challenges, or operational inefficiencies. 

Many organisations still underestimate the architectural readiness required for AI, only to face delays and cost overruns during implementation. In particular, technical debt accumulated through legacy systems, ad hoc integrations, or short-term fixes - can severely constrain the agility needed to adopt AI technologies effectively.

Key architecture components that influence AI readiness include:

• Data infrastructure: Centralised, scalable, and secure data storage (e.g., data lakes or lakehouses) that support structured and unstructured data.
• Data pipelines: Reliable ETL/ELT processes that ensure high data quality, freshness, and lineage.
• Compute infrastructure: Access to GPU/TPU-enabled environments for model training and inference, ideally in scalable cloud or hybrid setups.
• MLOps and model lifecycle management: Tools and workflows for automating model versioning, deployment, monitoring, and retraining are critical for scaling and maintaining AI in production.
• Integration layer - A modular and service-oriented architecture (typically microservices) as well as custom API integrations seamlessly enable AI outputs into business applications and workflows.
• Security and governance: Robust access controls, audit trails, and compliance mechanisms for data and model usage.
• Technical debt: Knowing how to manage technical debt effectively allows companies to identify and reduce bottlenecks related to legacy systems that slow down innovation.

Read next: Migrating Legacy Applications to the Cloud: 7 Success Strategies

Our expertise in building enterprise-grade data architectures ensures your AI initiatives are built on a solid foundation that can scale as your needs evolve.

3. Technology infrastructure

With a strong data foundation in place, the next phase focuses on the right technology infrastructure. Here are the key considerations for companies aiming to build a future-ready AI infrastructure:

• Scalable and secure computing environments: Modern AI applications demand infrastructure that can efficiently handle large-scale data processing and complex AI model training. Tailored for AI workloads, such environments need to also come with robust security and compliance with regulatory standards.
• Seamless integration with existing legacy systems: Most enterprises still operate on some legacy systems while investing in modern technologies. Successful AI strategy consulting regards the existing infrastructure as a bridge these environments, enabling AI capabilities without disrupting ongoing business operations.
• Energy-efficient and sustainable infrastructure: As environmental regulations tighten and operational costs rise, integrating green technologies and energy-saving practices into AI infrastructure becomes essential—not just for reducing expenses but for aligning with broader corporate sustainability goals. For example, in industries like aviation, where the pressure to decarbonise is particularly high, embracing such strategies is becoming a strategic imperative, as we’ve discussed in our article on sustainability in aviation.
• Technological agnosticism: Collaborations between cloud providers, hardware manufacturers, and investment groups can accelerate infrastructure development, reduce risk, and foster innovation through shared knowledge and capabilities. Embracing a cloud-agnostic development approach further strengthens this strategy by avoiding vendor lock-in, promoting interoperability, and enabling scalable, future-proof AI solutions.
• Focus on operational excellence: We at Drеamix believe that operational excellence helps enterprises continuously improve and evolve through proven methodologies. That’s why since 2023 we’ve been awarded as a continuous improvement software organisation and this reflects in the way we work and all our partnerships. 

4. Governance and risk

Without strong governance, AI initiatives risk unintended consequences, regulatory setbacks, and reputational damage-making this pillar indispensable for companies aiming to innovate confidently and ethically with AI.

• Assign roles and accountability for AI oversight: Assign specific responsibilities to leadership and cross-functional teams, such as agile POD teams, for monitoring, managing, and updating AI systems. This ensures transparency, accountability, and effective decision-making throughout your AI strategy.
• Implement continuous risk assessment and compliance monitoring: Regularly evaluate AI systems for emerging risks, compliance with regulations, and ethical standards.
• Prioritise transparency and explainability: Ensure that AI models and their decision-making processes are interpretable, especially in high-stakes applications. 
• Integrate ethical guidelines and data governance: Embed ethical principles into your AI strategy, addressing fairness, bias, and data privacy. Maintain strict data controls and regularly review data quality to support responsible and compliant AI deployment.

5. Strategic partnerships 

Strategic partnerships are another critical pillar of a successful AI strategy, enabling companies to accelerate development, reduce risk, and gain access to deep domain expertise. By collaborating with top AI development companies with a proven track record of delivering AI business solutions, companies can fast-track implementation, avoid costly mistakes, and benefit from proven methodologies. 

Key steps to building strong AI partnerships:

• Research potential tech partners: Start by identifying top AI development companies with a strong track record of delivering business value. Look for verified client reviews, case studies, and high rankings on trusted B2B platforms like Clutch. Dreamix holds a 5/5 rating from 26 verified clients.
• Evaluate cultural and strategic fit: Beyond technical expertise, ensure the partner understands your industry and shares your values around innovation, collaboration, and responsible AI use. We at Dreamix nurture a culture of collaboration, knowledge sharing, and a commitment to excellence, ensuring that partnerships are built on mutual understanding and shared goals.
• Prioritise proven delivery models: Choose partners with clear implementation frameworks like agile or hybrid models) and transparency in timelines and cost.
• Look for cross-functional expertise: Ideal partners combine deep AI/ML knowledge with strengths in data science, cloud development, DevOps and MLOps.

6. Implementation and scaling

Once high-impact use cases are identified, organisations must move from strategy to delivery with focus and discipline. The most effective approach begins with focused Proof of Concept (PoC) projects. 

• Start with focused proof-of-concept projects: These small-scale, low-risk initiatives allow businesses to validate technical feasibility, assess data readiness, and demonstrate business value before committing to full-scale deployment. 
• Design for scalability from the outset: While PoCs are small, the architecture should support future expansion. Dreamix leverages modular design, cloud-agnostic platforms, and containerised deployment strategies to ensure smooth scaling.
• Establish strong MLOps practices: Dreamix embeds automation and governance into model lifecycle management, including CI/CD pipelines, version control, monitoring, and retraining to support sustainable AI operations.
• Monitor performance continuously: Beyond deployment, Dreamix helps businesses implement real-time monitoring and alerting to detect issues early and measure impact against business KPIs.

Dreamix: Your AI co-innovation partner

As you embark on your AI implementation journey, Dreamix offers more than just technical expertise - we serve as a true co-innovation partner committed to your long-term success.

Our approach to partnership

Dreamix prioritises collaborative partnerships founded on transparent and active dialogue, helping us identify and establish realistic objectives that align with our partners' unique business requirements and strategic priorities.

• Strategy development: We work alongside your team to develop AI strategies aligned with your business objectives.
• Knowledge transfer: We emphasise building internal capabilities, ensuring your team gains valuable expertise throughout our partnership.
• Flexible engagement models: We offer various engagement models to accommodate different project needs and organisational preferences.

Our technical expertise

With over 18 years of experience delivering quality end-to-end software product development services, our 95% employee retention rate and partnerships lasting 10+ years testify to our commitment to excellence. 

• Custom AI development: Our Dreamix team excels in end-to-end AI and ML development and provides MLOps services - from Proof of Concept, MVP development to scaled AI implementation.
• AI strategy consulting: We help prospective partners define clear goals, develop comprehensive strategies, create detailed implementation plans, and track progress with regular reporting. 
• Data engineering and architecture: We design and implement robust data infrastructures that support AI initiatives, including data engineering, data science as well as legacy system modernisations and custom API integrations.