Two business opportunities as per billionaire Kevin O'Leary
Why small AI consulting companies are “mushrooming”
AI demand is rising faster than large enterprises can absorb internally.
Many companies want AI adoption, but they do not have enough in-house AI talent, clean data, governance models, or implementation experience. This creates space for small consulting firms that can quickly help businesses move from “AI interest” to “AI pilot” and then to production.Clients now want practical implementation, not just strategy decks.
Traditional consulting was often about strategy, market studies, and transformation roadmaps. In AI, clients increasingly ask: “Can you automate this process?”, “Can you build a chatbot?”, “Can you integrate AI into our ERP/CRM?”, “Can you reduce manual reporting?” Small firms are able to offer hands-on execution faster.AI tools have lowered the entry barrier for consultants.
A small team can now use cloud platforms, open-source models, low-code tools, automation platforms, vector databases, and AI agents to deliver work that earlier required a much larger team.Specialization is becoming more valuable than size.
Boutique AI firms are gaining attention because they focus on specific problems such as pricing, cost reduction, document automation, AI governance, analytics, marketing automation, or workflow optimization. Business Insider reported that many new AI-driven consulting firms are smaller, more specialized, and focused on making consulting services faster and more accessible. [businessinsider.com]Large consulting firms are expensive and slower for SMEs.
Many small and mid-sized companies cannot afford McKinsey, BCG, Deloitte, Accenture, or similar firms. They prefer smaller consultants who can deliver targeted solutions at lower cost and with quicker turnaround.There is an execution gap in AI adoption.
Many organizations are experimenting with AI, but few have mature, scalable AI systems. This gap creates opportunity for few-person firms that can help with use-case identification, data preparation, model selection, integration, training, and change management.
Typical team structure of a few-person AI consulting company
Founder / Principal Consultant
Usually someone with experience in management consulting, technology delivery, digital transformation, analytics, product management, or enterprise IT. This person handles client conversations, problem discovery, solution framing, pricing, and delivery oversight.AI / Machine Learning Engineer
Builds AI models, integrates APIs, creates prototypes, works with LLMs, develops retrieval-augmented generation systems, and connects AI tools with business applications.Data Engineer / Data Analyst
Handles data extraction, cleaning, transformation, dashboarding, reporting automation, database integration, and analytics pipelines.Cloud / DevOps Engineer
Sets up cloud platforms, manages deployment, security, scaling, monitoring, containers, APIs, and cost optimization.Business Analyst / Domain Specialist
Understands business processes in sectors such as finance, healthcare, retail, HR, supply chain, manufacturing, legal, or customer service. This person converts business pain points into AI use cases.Automation Specialist
Uses tools like Power Automate, Zapier, Make, UiPath, n8n, ServiceNow workflows, CRM automation, and AI agents to reduce manual work.Prompt Engineer / AI Workflow Designer
Designs prompts, agent workflows, knowledge-base structures, AI copilots, chatbot behavior, evaluation criteria, and human-in-the-loop review processes.Cybersecurity / Governance Advisor
Helps define policies for data privacy, access control, model risk, responsible AI, bias checks, audit logs, and compliance.UI / Product Designer
In some teams, a designer builds simple interfaces, dashboards, internal tools, chatbot screens, or proof-of-concept apps for client users.
Skills these small consulting teams usually have
Generative AI and LLM skills
They understand how to use large language models for summarization, search, Q&A, drafting, coding assistance, customer support, document analysis, and workflow automation.Data analytics skills
They work with structured and unstructured data, build dashboards, create reports, and identify patterns for decision-making.Cloud platform skills
Many work with Microsoft Azure, AWS, Google Cloud, Databricks, Snowflake, and similar platforms.Automation skills
They automate repetitive tasks such as invoice processing, customer email routing, report generation, HR screening, lead qualification, and document comparison.Integration skills
They connect AI tools with ERP, CRM, HRMS, ticketing systems, databases, SharePoint, Teams, email, and internal knowledge repositories.Business process understanding
Their value is not only technical. They must understand how business functions operate—sales, finance, procurement, HR, operations, IT support, and customer service.AI governance and risk skills
Companies are worried about hallucination, data leakage, regulatory exposure, bias, and unreliable outputs. Consultants who can build controls around AI usage have a strong advantage.Change management and training skills
AI adoption fails if users do not trust or understand the tools. Small firms often provide training, SOPs, workshops, and adoption support.
Services small AI consulting firms are offering
AI readiness assessment
They review a company’s data, systems, processes, skills, risks, and business goals to identify where AI can realistically add value.AI strategy and roadmap
They prepare a phased plan covering use cases, investments, technology choices, data requirements, governance, and expected ROI.Use-case discovery workshops
They conduct workshops with business teams to identify repetitive, high-volume, high-cost, or decision-heavy processes suitable for AI.Proof of concept development
They build quick prototypes such as internal chatbots, document search tools, automated reporting systems, demand forecasting models, or customer support assistants.Custom AI chatbot development
They create chatbots for HR, IT helpdesk, customer support, sales enablement, policy search, onboarding, and knowledge management.Document intelligence solutions
They automate reading, classification, extraction, comparison, and summarization of contracts, invoices, tenders, resumes, policies, claims, and technical manuals.Data analytics and dashboards
They build dashboards for leadership reporting, sales forecasting, operational performance, financial analysis, inventory planning, and customer behavior.AI automation of business workflows
They automate email responses, ticket triage, report preparation, meeting summaries, CRM updates, invoice routing, and compliance checks.AI governance framework
They help companies define responsible AI policies, access rights, approval workflows, model usage rules, risk controls, and audit mechanisms.Employee training and AI literacy
They train employees on prompt writing, safe AI usage, productivity tools, Copilot adoption, workflow automation, and department-specific AI use cases.AI product development support
Some firms help startups and enterprises embed AI features into existing products, such as recommendation engines, search, copilots, predictive alerts, or automated insights.Managed AI support services
After implementation, they monitor performance, update prompts, improve knowledge bases, fix errors, manage usage, and optimize cost.
Business opportunity 1: Data centre development
AI is creating massive demand for data centres.
Modern AI workloads need thousands of GPUs, high-speed networking, advanced storage, strong power systems, and specialized cooling. Deloitte estimates that power demand from AI data centres in the United States could grow more than thirtyfold by 2035, from 4 GW in 2024 to 123 GW. [deloitte.com]Power availability is becoming the biggest bottleneck.
Data centre growth is no longer limited only by land, capital, or internet connectivity. Electricity availability, grid connection timelines, and power reliability are now major constraints. Data Center Frontier notes that power has become a defining constraint for AI data centre expansion. [datacenter...ontier.com]Cooling is a major opportunity.
AI racks generate much higher heat than traditional servers. This creates demand for liquid cooling, immersion cooling, direct-to-chip cooling, heat reuse, cooling design, and energy-efficient thermal management. McKinsey notes that power and cooling equipment are becoming critical as data centre demand grows. [mckinsey.com]Networking is another hidden bottleneck.
AI data centres need extremely fast communication between GPUs. As GPU clusters grow, network fabric design, congestion control, switches, NICs, and interoperability become as important as GPUs themselves. TechRepublic highlighted networking as a growing bottleneck in AI data centres. [techrepublic.com]Small consulting firms can serve the data centre ecosystem, even if they do not build data centres directly.
Few-person firms can support feasibility studies, site selection analytics, power-demand modelling, vendor evaluation, project documentation, sustainability reporting, compliance tracking, automation dashboards, and operational analytics.Opportunity areas around data centre development include:
Site feasibility studies.
Power availability assessment.
Renewable energy sourcing advisory.
Cooling technology evaluation.
Data centre project management support.
ESG and sustainability reporting.
Data centre cost modelling.
AI workload capacity planning.
Data centre operations dashboards.
Predictive maintenance solutions.
Security and compliance documentation.
Vendor comparison and procurement support.
There is opportunity for specialized engineering-advisory boutiques.
Teams with electrical engineering, HVAC, energy management, cloud infrastructure, and project finance skills can position themselves as data-centre advisory firms.There is opportunity in edge and regional data centres.
Because hyperscale projects face grid, permitting, and political constraints, smaller distributed data centres and edge facilities may become more attractive for latency-sensitive AI workloads. Data Center Frontier reported that edge and regional architectures can help spread load and reduce permitting pressure. [datacenter...ontier.com]
Business opportunity 2: Supporting the broader AI ecosystem
Most businesses want AI but do not know where to start.
Companies across sectors are asking how to use AI in sales, HR, finance, procurement, operations, legal, customer service, IT, and leadership reporting. This creates strong demand for advisory and implementation partners.AI adoption requires more than buying a tool.
Businesses need clean data, process redesign, security controls, user training, integration with existing systems, and measurable ROI. This is where small consulting firms can create value.SMEs are a large market.
Small and medium enterprises often cannot hire full-time AI teams. They need external experts who can deliver practical, affordable, and focused solutions.AI support services are becoming recurring revenue opportunities.
After implementation, clients need continuous improvement, monitoring, prompt tuning, knowledge-base updates, user support, governance checks, and cost optimization.There is demand for vertical-specific AI solutions.
A generic AI consultant may struggle, but a consultant focused on one industry can win. For example:AI for hospitals.
AI for real estate.
AI for schools.
AI for logistics.
AI for manufacturing.
AI for retail.
AI for finance.
AI for legal documentation.
AI for HR and recruitment.
There is a big opportunity in AI training.
Many employees use AI casually but not professionally. Companies need structured training on safe prompting, productivity use cases, Copilot usage, data privacy, and role-based AI adoption.There is opportunity in AI governance.
As companies adopt AI, they need rules for what data can be used, who can access AI tools, when human review is required, how outputs are audited, and how risks are managed.There is opportunity in AI agents.
Businesses are moving from simple chatbots to AI agents that can take action: create tickets, update CRMs, draft emails, summarize meetings, prepare reports, check documents, and trigger workflows.
Why few-person firms can win in this market
Speed
Small firms can create a prototype in days or weeks, while large firms may take months to begin.Lower cost
Their overhead is lower, so they can serve SMEs and departments with limited budgets.Senior involvement
In a small firm, the founder or senior expert often works directly with the client.Niche specialization
A firm that focuses only on AI for finance automation or AI for HR operations can become more credible than a generalist.Flexible delivery models
They can offer workshops, pilots, retainers, fixed-price projects, or outcome-based pricing.AI-native operating model
They use AI internally for research, proposal writing, coding, testing, documentation, analysis, and delivery, which makes them leaner and faster.
Most promising consulting niches
AI adoption consulting for SMEs.
Microsoft Copilot implementation and training.
AI chatbot development for internal knowledge management.
Document automation for legal, finance, HR, and procurement.
Data centre feasibility and energy advisory.
AI governance and responsible AI frameworks.
AI workflow automation using low-code tools.
Industry-specific AI copilots.
Predictive analytics for supply chain and operations.
AI training academies for enterprises.
Cloud cost optimization for AI workloads.
Data engineering and data readiness services.
AI security and compliance advisory.
AI project rescue services for failed pilots.
AI-enabled business intelligence and automated reporting.
Bottom line
Few-person consulting companies are growing because AI has created a huge gap between ambition and execution. Large enterprises, SMEs, and infrastructure providers all need practical help. The biggest opportunities are in two directions:
Physical AI infrastructure — data centres, power, cooling, networking, energy planning, and operational efficiency.
Business AI adoption — automation, chatbots, analytics, governance, training, and industry-specific AI solutions.
The firms that will succeed are those that combine technical AI skills, business process understanding, fast execution, governance awareness, and measurable ROI delivery.
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