Introduction
NexaCore Consulting, a mid-sized global technology consulting and IT services firm headquartered in Bangalore with operations in North America, Europe, and Southeast Asia, built its reputation over 18 years by helping clients modernize legacy systems, migrate workloads to the cloud, and run large-scale managed services. With 12,000 employees and annual revenues of USD 850 million, the company had a strong presence in BFSI, retail, and manufacturing sectors.
Until 2023, NexaCore had grown steadily by delivering projects with high-quality engineering talent, scalable offshore delivery, and deep domain understanding. However, the emergence of generative AI, automation-first delivery, and AI-native consulting models began to disrupt their foundational business assumptions. Global clients were increasingly demanding not just software and operations, but human-plus-machine systems, AI-driven value creation, and measurable business impact within months—not years.
This case explores the strategic inflection point NexaCore faces in 2025, analyzing internal challenges, client pressures, market shifts, and the need to build AI-native service offerings to survive the next decade.
Part 1: The Market Landscape (2024–2025)
By early 2025, the technology consulting landscape had undergone a structural shift driven by four forces:
1. The Explosion of Generative AI and Multi-Agent Architectures
Across industries, enterprises transitioned from pilot experiments to scaled AI adoption:
- BFSI clients automated KYC, underwriting, fraud detection, and claims processing.
- Retail firms introduced AI-driven supply chain and marketing optimization.
- Manufacturing companies built predictive maintenance and digital twin solutions.
- IT operations became increasingly automated with AI-driven observability.
Generative AI platforms (OpenAI, Anthropic, open-source LLMs, domain-specific foundation models) created new expectations for speed and value.
2. Shrinking Traditional IT Services Spend
Clients began reallocating budgets:
- 20–35% of traditional ADM (application development & maintenance) budgets moved toward AI-driven modernization.
- Managed services expected productivity gains of 30–70% via automation, reducing demand for large teams.
- Cloud migration slowed, replaced by AI-enabled cloud optimization and data engineering modernization.
3. Consulting Firms Becoming AI-Native
Top-tier IT consulting firms (Accenture, Deloitte, IBM, TCS, Infosys) were:
- Building AI studios
- Creating proprietary models and industry agents
- Publishing domain-specific AI blueprints
- Hiring thousands of AI engineers and prompt architects
4. New Competitors Emerging
Smaller, AI-native startups could build complex automation solutions in weeks instead of months. This caused price pressure and accelerated expectations.
In this environment, NexaCore’s leadership felt increasing pressure to reinvent itself.
Part 2: Internal Issues and Strategic Gaps
1. Legacy Offerings No Longer Competitive
NexaCore’s flagship offerings—cloud migration, app modernization, and managed services—were seeing reduced margins. A major BFSI client asked for a 40% reduction in managed service cost based on generative AI productivity benchmarks.
2. Limited AI Capabilities
Although NexaCore had an “AI & Analytics” practice, it:
- Focused mainly on dashboards, ML models, and data warehousing
- Had only 120 AI engineers out of 12,000 employees
- Lacked GenAI architects, prompt engineers, and AI product managers
- Had no proprietary AI accelerators or platforms
The firm was delivering AI-enhanced services—not AI-native services.
3. Delivery Model Still Labor-Driven
The offshore delivery structure relied on:
- Large pyramid teams
- Manual QA and regression testing
- Manual status reporting and tracking
- Traditional project management
This model fundamentally contradicted AI-era expectations of small, high-skilled pods augmented by AI agents.
4. Cultural Resistance
Many senior project managers resisted AI adoption, worrying it would:
- Reduce team sizes
- Reduce billability
- Disrupt established processes
5. Financial Pressure
Revenue growth slowed from 16% to 7% in 2024. Operating margin dropped by 3%. Investors began asking how NexaCore would compete in an AI-driven market.
Part 3: The Trigger Event — The Loss of a Major RFP
In April 2025, NexaCore lost a $110 million multi-year digital transformation deal with a large European insurer. The client cited the winning vendor’s “AI-native transformation strategy,” including:
- AI-generated code (40% of development)
- AI observability solution (reducing downtime by 65%)
- Automated test generation and self-healing dev environments
- AI-driven business workflow automation
- A proprietary insurance domain foundation model
The client concluded that NexaCore’s proposal was “traditional consulting repackaged” and lacked:
- Domain-specific AI assets
- Generative AI accelerators
- AI-assisted delivery
- Outcome-based pricing
This was a wake-up call. The CEO called an emergency strategy workshop with the CTO, Chief Delivery Officer, Head of AI Practice, and regional heads.
Part 4: The Strategic Pivot Towards AI-Native Service Offerings
NexaCore realized that survival required a fundamental reinvention, not incremental upgrades. The leadership team proposed a 3-year transformation plan to become an AI-native consulting company.
Below are the core pillars of the plan.
1. Build an AI-Native Portfolio of Offerings
(a) Industry-Specific AI Solutions
Six industry solution studios were launched:
- BFSI AI Studio: Claims AI, underwriting copilots, fraud detection agents
- Retail AI Studio: Pricing intelligence, demand forecasting copilots
- Healthcare AI Studio: Clinical documentation agents, patient engagement AI
- Manufacturing AI Studio: Predictive maintenance twins, automation AI
- Energy AI Studio: Regulatory reporting AI, grid optimization agents
- Hi-Tech AI Studio: AI lifecycle automation, product engineering copilots
(b) AI-Powered Application Engineering
Complete redesign of ADM services:
- AI-assisted code generation and refactoring
- Automated full-stack testing
- AI-driven requirements-to-code pipelines
- Multi-agent software engineering
(c) AI-Enabled Managed Services
Targeting 40–60% productivity gains through:
- Autonomous monitoring agents
- Self-healing infrastructure
- Automated incident triage
- AI service desk
(d) Advisory Services for AI Governance
Growing regulatory pressure created strong demand for:
- Responsible AI frameworks
- Data governance & model risk management
- AI compliance audit services
2. Build an Internal AI Engine
(a) NexaCore Enterprise AI Platform (NEAP)
A unified platform offering:
- Model catalog and orchestration
- Agent development framework
- Prompt factory
- Connectors for enterprise data
- Guardrail and governance modules
(b) AI Delivery Pods
Small “pod-based delivery” teams:
- AI Architect
- GenAI Engineer
- Domain Specialist
- Data Engineer
- Automation Engineer
(c) Reskilling 6,000 Employees
Company-wide program:
- AI engineering certifications
- Prompt engineering bootcamps
- GitHub Copilot and similar tool adoption
- AI-first delivery training for PMs
- AI fluency for sales and consulting teams
(d) AI-Augmented Workforce Tools
Every employee received:
- AI meeting assistant
- AI coding copilot
- AI research assistant
- AI PMO assistant
3. Ecosystem Partnerships
NexaCore signed new strategic partnerships with:
- OpenAI (enterprise model usage)
- HuggingFace (open-source model accelerator)
- Microsoft, AWS, and Google Cloud (industry clouds)
- MLOps platform startups
- Universities and research institutes
4. New Commercial Models
The firm launched:
- Value-based pricing for AI-based cost savings
- Subscription models for industry AI accelerators
- Joint IP development with clients
- Success-fee-linked automation programs
Part 5: Client Impact and Early Wins
Within the first year, NexaCore saw strong early successes:
1. A U.S. Retailer Reduced Supply Chain Costs by 18%
Using AI demand forecasting, route optimization, and intelligent replenishment.
2. A Global Bank Cut Testing Effort by 60%
Thanks to AI-generated test cases and autonomous regression testing.
3. A Healthcare Provider Improved Documentation Efficiency by 45%
Via medical scribe AI agents.
4. A Manufacturing Plant Reduced Downtime by 30%
Using AI-driven predictive maintenance.
These wins brought credibility, improved margin by 2.5%, and increased deal wins in competitive RFPs.
Part 6: Ongoing Challenges
The transformation was not without difficulties.
1. Talent Gap
The firm still had shortage of:
- Senior AI architects
- Applied ML experts
- Domain AI strategists
2. Managing Client Expectations
Some clients wanted “magic-box AI” without investing in data quality or change management.
3. Cultural Transformation
Middle managers found it difficult to shift from manpower-led to capability-led models.
4. IP Creation Pace
Competitors were releasing new AI accelerators every month. Keeping up required rapid iteration.
Part 7: The Strategic Outlook
By 2027, NexaCore envisioned itself as:
- A platform-and-AI-first consulting company
- A provider of industry leading AI-native transformation solutions
- A firm with thin, powerful teams augmented by multi-agent AI systems
- A creator of reusable AI accelerators and micro-platforms
- A trusted partner for AI governance and responsible AI
The leadership declared the transformation as “existential but achievable.”
Conclusion
NexaCore’s journey represents the challenge many mid-tier IT consulting firms face in the AI age. As generative AI accelerates the shift from labor-based to intelligence-based delivery models, firms must rethink their offerings, delivery structures, talent models, pricing frameworks, and strategic partnerships. Becoming AI-native is no longer optional—it is essential for survival.
Case Questions
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Dr. Selvaraj Vadivelu 