Ekipa AI is a company that provides AI strategy consulting and implementation support services to businesses. They specialize in helping organizations identify, develop, and deploy AI solutions to address specific business challenges and opportunities. Their services range from initial AI strategy formulation and use case discovery to defining AI requirements, providing implementation support, and offering team enablement workshops.
Ekipa.ai serves mid‑sized companies and scale‑ups seeking strategic guidance or support in building AI use cases, whether they are in early exploration or ready to develop prototypes or MVPs.
Unlike traditional firms that rely on expensive reports with little follow‑through, Ekipa.ai uses AI tools supported by human consultants to deliver strategy and execution planning at a fraction of the cost—enabling clients to invest in action rather than just presentations.
AI consulting firms typically fall into three types: • Big Tech: Push their own tools • Boutique Firms: Offer custom builds, often pricey • Strategy-First Consultants: Focus on solving real business problems Ekipa.ai fits the third. They help identify practical use cases, build clear roadmaps, and implement only what's needed.
An AI strategy is a customized plan that identifies where AI can meaningfully impact your business, aligns those opportunities with priorities, and outlines clear requirements and a roadmap for implementation. You need one to avoid failed initiatives, prioritize high‑impact projects, and move beyond pilot phases with structured execution.
If you are in the exploration phase, you can receive a strategic report within 48 hours; if building with AI, the team typically generates a detailed AI roadmap or custom build plan within 24 hours, supplemented by expert refinement and development support.
The strategy report includes a clarified briefing on your organization's priorities, a curated set of high‑impact AI use cases tailored to your business context, validated by AI tools and reviewed by human consultants, plus a strategic roadmap with timelines, milestones, and guidance for execution
Ekipa.ai uses its AI-powered discovery tools combined with expert input to analyze your industry and organizational needs, presenting use cases that align with your pain points and priorities. These are then refined and prioritized through collaboration with your AI consultant
Absolutely. Once we've agreed on what to build, we help define the scope and then work with you to build a proof of concept, MVP, or even a full solution - step by step.
No. Ekipa.ai provides support at every step - from strategy and use case discovery to implementation. So, technical expertise on your part is optional. Their consultants and developers handle technical scoping and build tasks.
After delivery, Ekipa.ai continues to support you through phased development, ongoing expert consultation, and implementation guidance to help you transition a PoC into a deployed solution aligned with your strategic objectives.
An AI workshop includes tailored, hands‑on sessions designed to raise awareness, teach ethical practices, introduce tools, and develop practical use cases. These sessions are structured to build confidence and readiness across your leadership and teams.
Yes. Ekipa.ai offers workshops and team enablement focused on AI awareness, ethics, tool adoption, and practical applications to up‑skill your internal teams and prepare them for AI readiness.
Ekipa.ai begins with clarifying your business priorities and pain points, then maps AI use cases directly to those objectives. The result is a strategic roadmap that ensures each AI initiative is aligned with measurable business outcomes
You collaborate through structured briefings, strategy sessions, workshops, and ongoing consultations. Each step combines AI tools with human consultants who co-design the plan, refine use cases, scope requirements, and guide implementation.
While Ekipa.ai's site does not list barriers explicitly, common challenges in AI adoption generally include aligning initiatives with business goals, managing data readiness, cultivating talent and cultural readiness, ensuring ethical compliance, and preparing for scale. Ekipa.ai addresses these through their structured strategy, workshops, and implementation support
Ekipa.ai offers industry‑specific strategy reports for sectors including insurance, pharma, banking, construction, mining, energy, oil and gas, education, telecommunications, fisheries, media advertising, tech consulting, utilities, manufacturing, global mobility, automotive, BPM and IT, and logistics
Success is measured by tracking impact against the objectives defined in your AI roadmap such as revenue growth, cost savings, time efficiency, or customer satisfaction improvements. Ekipa.ai's approach emphasizes clear KPIs tied to business outcomes to ensure measurable returns.
Ekipa.ai integrates ethical considerations into workshops, governance frameworks, and team enablement sessions. They emphasize responsible practices, AI literacy, and bias awareness to ensure solutions are transparent, compliant, and fair.
While Ekipa.ai doesn't list specific public examples, typical use cases include automating internal workflows, enhancing customer service, improving data-driven decision-making, and developing predictive models aimed at operational efficiency and strategic advantage.
An AI strategy consultant guides organizations through identifying AI opportunities, aligning use cases with business goals, designing implementation roadmaps, and overseeing deployment and adoption.
An AI strategist works with your leadership team to clarify priorities, identify and prioritize AI use cases, create a roadmap with milestones, and ensure each initiative ties directly to measurable business value.
Core pillars include aligning AI initiatives with business objectives, understanding data and technical readiness, designing ethical governance, enabling organizational change, and preparing people and processes to adopt AI.
AI strategy consulting is a service that helps organizations assess where AI can add value, develop a tailored roadmap for use cases, build internal capabilities, and support implementation to drive real business outcomes.
AI is used to analyze data, surface high-impact opportunities, forecast trends, automate decisions, and guide strategic choices - enabling businesses to scale insight-driven initiatives across functions.
A strategy advisor may focus broadly on business or management topics, while an AI strategy consultant specializes in identifying AI-based opportunities, planning data-driven transformations, managing technical execution, and ensuring responsible integration.
The AI 10-20-70 rule states that approximately 10% of AI success comes from algorithms, 20% from technology and data infrastructure, and 70% from people, processes, and organizational change
A strong AI strategist clarifies business goals, assesses data and tech readiness, identifies high‑value use cases, builds a phased roadmap with clear KPIs, embeds ethical and governance practices, and supports team readiness and change management.
To write an AI strategy, start with executive objectives, evaluate data and infrastructure, identify relevant use cases, define success metrics, plan a phased implementation roadmap, and establish ethical oversight and organizational readiness.
Implement AI by selecting a validated use case based on strategic value, building a prototype or MVP with clear KPIs, deploying iteratively, integrating into workflows, training teams, and monitoring performance for scaling and refinement.
Successful companies adopt AI by aligning initiatives to strategic goals, ensuring data readiness, investing in tech and governance, engaging leadership, upskilling teams, piloting, and scaling through a structured roadmap.
AI can be used to automate repetitive tasks, enhance decision-making, personalize customer experiences, improve forecasting and supply chain performance, and support innovation in products and services.
Adapting to AI requires upskilling through workshops, integrating AI tools into daily workflows, reshaping processes for human-AI collaboration, and fostering a culture of continuous learning and experimentation.
Effective integration involves workflow redesign to embed AI outputs, change management to encourage adoption, training teams to use AI tools confidently, and governance structures to ensure ethical and reliable operation.
You align AI by starting with clear business priorities, selecting use cases that directly serve those goals, defining measurable outcomes, and embedding AI initiatives into strategic and operational planning.
AI is increasingly relevant because it enables automation, insight-driven decision-making, scalability, and innovation across industries - delivering competitive advantages and operational efficiencies.
Companies monetize AI by improving efficiency and reducing costs, creating smarter products or services, enhancing customer engagement, opening new revenue streams, and enabling data-driven innovation.
AI is effectively used to personalize customer experiences (e.g., Netflix for recommendations, Amazon for product suggestions, and Starbucks with personalized offers). In business settings, AI drives fraud detection, inventory optimization, dynamic pricing, and supply chain efficiency.
Custom AI solutions are tailored systems created to address a specific organization's needs ranging from data analysis and predictive models to bespoke applications such as workflow automation, customer support bots, or recommendation engines, designed to integrate seamlessly into unique business contexts.
Collaborative AI refers to systems where humans and AI work together - for instance, recommender systems that combine algorithmic suggestions with human refinement, or chatbots that handle routine queries and escalate complex issues to human agents.
Generally, AI software can be categorized as rule-based systems, machine learning platforms, natural language processing tools, and computer vision applications - each serving different functions like automation, decision support, text analysis, or image interpretation.
Building an AI solution typically involves defining the problem aligned with business goals, collecting and preparing data, selecting or training the appropriate model, integrating the AI into workflows or systems, testing and refining the output, and deploying with a plan for monitoring and iterative improvement.
An AI solutions architect designs end-to-end AI systems: they define architecture, choose technologies, map data flows, ensure scalability, align the solution with business requirements, coordinate with technical teams, and oversee ethical and governance considerations.
Typical use cases include recommendation systems, predictive maintenance, customer service chatbots, fraud detection, process automation, demand forecasting, and personalized marketing campaigns.
You collaborate with AI by integrating it into everyday tools such as chatbots for customer support, analytics platforms for insights, or assistants that co-author content while humans guide, validate, and augment the AI outputs throughout workflows.
AI accelerates ideation by generating suggestions, spotting patterns, simulating scenarios, and enabling teams to prototype features rapidly - supporting human creativity and speeding innovation cycles.
AI personalization works by collecting user data (such as behavior, preferences, or demographics), processing it through models like collaborative filtering or content-based filtering, then dynamically adapting content, offers, product recommendations, or interfaces to each individual