According to recent research from McKinsey, IBM, and others, 40% of companies are already integrating artificial intelligence into their operations, while 82% are either actively implementing or exploring AI solutions. With the global AI market projected to reach $4.8 trillion by 2033, one thing is certain: diving into AI without a clear strategy is no longer an option — it’s a risk.
That’s where the AI consulting team comes in — not just as expert guidance, but as a strategic advantage to identify AI opportunities. If you’re evaluating how AI could drive value in your business, you’re in the right place. This guide breaks down the essential steps, real-world examples, and proven frameworks to help you move from AI hype to impact.
Key Takeaways
- AI strategy consulting is not just about technology — it’s about business alignment.
- AI implementation is a multi-phase journey, not a quick install.
- Responsible AI includes governance, ethics, and change management.
What AI Strategy Consulting Is Not?
At its core, AI strategy consulting is an advisory service designed to assist organizations in effectively integrating artificial intelligence into their operations. “Assist” is a key word here, and it can sometimes be misunderstood. That’s why it’s important to set clear expectations from the start. AI management consulting is not about:
- End-to-end software development: AI consultants guide technical architecture and pilot builds, but are not full-scale development agencies responsible for coding and maintaining entire software products.
- IT infrastructure overhaul: AI consultants advise on tech stack integration but do not handle large-scale IT upgrades or network redesigns.
- One-off tool installation: AI strategy focuses on long-term value rather than quick-fix solutions like installing a chatbot or automation tool.
- Guaranteed ROI from day one: AI consultants identify high-potential opportunities, but outcomes depend on variables like data quality and execution, so immediate ROI isn't guaranteed.
- Full internal team replacement: AI strategy consulting empowers internal teams rather than replacing them, ensuring staff involvement.
- Short-term tactical fixes: AI consultants focus on long-term strategies, not on addressing isolated technical issues or short-term automation requests.
- Ignoring governance and risk: Responsible consulting includes compliance, ethics, and accountability.
The Role of an AI Implementation Consultant
AI implementation consultants guide AI adoption for businesses, focusing on strategy and alignment with business goals, unlike AI engineers who develop models or data scientists who analyze data. Their key skills are business acumen, strategic thinking, communication, and AI understanding. Confusing these roles can hinder projects; effective AI adoption requires collaboration among them.Â
Check the table below to see the difference between AI implementation consultant and other tech specialists:
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‍💡Insight: Axis Bank introduced an AI voice assistant that now handles 12–15% of incoming calls at 90% accuracy, significantly reducing call-center workload and contributing to business transformation. Source.
5 Key Frameworks in AI Strategy Consulting
AI technology consulting teams use established frameworks to structure AI strategies that transform operations and keep projects aligned with objectives. These models break AI initiatives into stages, ensuring thorough planning and execution.
Cross-Industry Standard Process for Data Mining
CRISP-DM is a widely used six-phase process that can lead to greater ROI through well-structured AI initiative:
- Business understanding
- Data understanding
- Data preparation
- Modeling
- Evaluation
- Deployment
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This iterative cycle helps ensure the AI solution supports the business objective and uncovers new business opportunities. For example, a retailer forecasting sales would define goals, analyze historical data, prepare features, build a model, evaluate its accuracy, and deploy it in their ordering system.
Team Data Science Process (TDSP) FrameworkÂ
Developed by Microsoft, TDSP is a team-focused strategy framework often used in Azure environments. It includes:
- Business understanding
- Data acquisition & understanding
- Modeling
- Deployment
TDSP emphasizes collaboration and standardization, which are critical components of an effective AI strategy. For example, a healthcare provider might use it to align data engineers and scientists on a predictive care model using Azure Machine Learning.
Lean AIÂ
Inspired by Lean startup principles, Lean AI consulting services focus on fast iteration, prototyping, and feedback. It reduces waste by deploying minimally viable models quickly.
Consultants may create reusable “data-product” modules and launch prototypes early. For example, a startup might test an AI recommendation engine with a small user group, refining the model based on real feedback to speed up time-to-market.
Agile AI Development
The Agile AI Development approach applies Agile software practices to AI projects. It breaks work into short sprints and emphasizes stakeholder reviews and adaptability.Â
For example, Spotify uses Agile sprints to iteratively improve its music recommendation algorithms, and ING adopted Agile to prototype AI solutions in finance. By treating each ML model or data pipeline as a series of small deliverables, Agile AI Development ensures quick iterations and constant alignment with the overall business strategy.
AI Canvas
The AI Canvas is a one-page planning tool (similar to the Business Model Canvas) that helps the consulting team connect AI projects to strategic goals. Key components include:
- Problem definition
- Data
- Training
- Testing
- Deployment
- Business impact
In consulting workshops, teams use the AI Canvas to answer essential questions: What problem are we solving? What data do we have? How will success be measured?
For example, consultants guide stakeholders in defining the opportunity, required data, proposed solution, success metrics, and risks — ensuring the AI use case solves a real business need.
đź’ˇNote: Even great AI solutions fail without adoption. Consulting emphasizes change management: training users, setting up governance, and building internal buy-in.Â
Example of a Successful AI Strategy ImplementationÂ
Entrepreneur Patrick Bet-David partnered with Lazarev. to reimagine how people in the US. consume news. The result was VTnews.ai — an AI-powered platform designed to expose bias, deliver balanced perspectives, and engage users in shaping public discourse through predictive and interactive features.
Challenge
Mainstream media often distorts reality with hidden agendas and bias. The goal was to create the first truly unbiased news platform to help Americans step outside the “information bubble.”
AI Strategy Consulting Approach
Our team initiated a comprehensive discovery phase to address media distrust and user disengagement. The strategy harnessed AI to expose bias and transform user interaction with news. Instead of passive consumption, users actively engage in understanding, questioning, and predicting events.
We crafted an AI-first product roadmap incorporating real-time analysis, personalization, and engagement tools, ensuring the platform delivered transparency and impact at scale.
Key Results
- 90% of users said the platform helped them escape information bubbles
- 85,000+ new users onboarded in the first month after launch
- 7+ AI tools integrated to analyze, summarize, curate, and predict news in real time
- 100+ thematic filters enabled personalized news consumption at scale
Today, the platform’s real-time bias detection engine scans over 130,000 sources, offering side-by-side perspectives from left, center, and right-leaning media outlets helping users instantly spot narrative imbalances. An interactive AI chat assistant adds another layer of value, summarizing complex topics and answering politically nuanced questions in seconds.Â
Users can also track how stories evolve over time through configurable timelines, while predictive features allow them to explore possible future outcomes and compare them to real-world events.
If you’d like to explore the full scope of this project, you can read the complete case study.
How to Implement an AI Technology in Your Organization
First and foremost, organizations looking to leverage AI technology consulting should take two core steps:
- Identify specific business challenges: Pinpoint areas where AI can provide measurable value rather than implementing technology indiscriminately.
- Establish clear success metrics: Define specific KPIs to measure implementation success before beginning any technical work.
Once these two foundations are in place, the next step is to partner with an experienced consultant. At Lazarev., our AI strategy consulting process includes the following steps:
1. Discovery & Business Alignment
Goal: Understand the company’s goals, challenges, and current capabilities.
- Conduct stakeholder interviews and workshops.
- Audit existing data infrastructure, talent, and AI maturity.
- Identify strategic priorities (e.g., cost reduction, innovation, customer experience).
Example: A logistics company might express interest in reducing delivery times; the consultant identifies route optimization and demand forecasting as high-potential use cases.
2. Use Case Identification & Prioritization
Goal: Map business problems to feasible AI solutions.
- Brainstorm and validate possible use cases.
- Score them based on business impact, feasibility, and urgency.
- Prioritize a short list of AI initiatives for further exploration.
Example: A bank may have 10 AI ideas, but fraud detection and customer service chatbots might be fast wins, so they’re prioritized.
3. Data Assessment & Gap Analysis
Goal: Determine whether the right data is available to support AI initiatives.
- Audit existing data sources, quality, and accessibility.
- Identify gaps in data or data governance.
- Recommend improvements if needed (e.g., data lake setup, real-time ingestion).
Example: A retailer wants to predict churn, but has poor customer purchase history tracking — the consultant recommends integrating POS and CRM systems.
4. Strategic Roadmap Development
Goal: Build a phased AI adoption plan tied to business objectives.
- Define timelines, milestones, and KPIs.
- Allocate roles, responsibilities, and required resources.
- Include model development, testing, deployment, and governance.
Example: A three-year roadmap might include pilot projects in Year 1, scaling infrastructure in Year 2, and full AI integration in Year 3.
5. Proof of Concept (PoC) / Pilot Execution
Goal: Test high-priority use cases in a low-risk environment.
- Build minimal viable models or prototypes.
- Evaluate performance, user feedback, and alignment with goals.
- Refine based on findings.
Example: A pilot chatbot for HR queries is rolled out to 100 internal employees for feedback before broader deployment.
6. Deployment & Integration
Goal: Move from pilot to production-level implementation.
- Integrate models into business workflows (CRM, ERP, apps).
- Set up MLOps pipelines for model retraining and monitoring.
- Ensure cross-functional coordination (IT, operations, legal, etc.).
Example: A demand forecasting model is integrated into the supply chain system to automate inventory decisions.
7. Change Management & Training
Goal: Prepare teams for AI adoption and ongoing use with effective strategy consulting services .
- Run internal training programs and documentation.
- Align leadership, ops, and end-users on benefits and usage.
- Build governance structures (AI ethics, accountability).
Example: The consultant trains customer support teams on how to escalate complex queries from the AI assistant.
8. Continuous Monitoring & Optimization
Goal: Ensure long-term performance and adaptability of AI solutions.
- Monitor KPIs, model drift, and user satisfaction.
- Update models based on new data or shifting conditions.
- Recommend new use cases as AI maturity grows.
Example: A recommendation engine’s performance is reviewed quarterly, and adjusted for seasonal trends.
💡Recommendation: If you’re looking for  trusted partner in AI consulting, check out our article where we’ve researched and analyzed the top service providers.
Conclusion: The Strategic Imperative of AI Implementation
AI implementation is not about replacing human workers or pursuing technology for its own sake. It's about strategically enhancing existing systems to drive business transformation and growth through data-driven insights and automating repetitive tasks. Organizations that approach AI with clear objectives and guidance from AI implementation consultants can achieve sustainable competitive advantage. Success lies in thoughtful application to solve real business challenges.
At Lazarev., we help businesses identify and optimize AI solutions tailored to their needs. Our experienced AI strategy consulting professionals develop implementation roadmaps that deliver measurable results. Ready to transform your operations? Contact us to discuss AI solutions tailored to your industry.
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