How AI Evolved from Research to Business Infrastructure
The idea of artificial intelligence is not new. The term "artificial intelligence" was formally introduced in the 1950s, with the Dartmouth workshop in 1956 often considered a foundational moment in the field.
Early AI focused largely on symbolic reasoning and rule-based systems. Later, machine learning allowed computers to learn patterns from data rather than relying entirely on manually programmed rules.
The 2010s brought major advances in deep learning, computer vision, speech recognition, and natural language processing. Then came transformer-based models and the rapid growth of generative AI.
Today, AI is entering another stage: agentic AI.
Instead of simply answering a question, an AI agent can potentially retrieve information, make decisions, call software tools, update systems, and complete multiple steps toward a defined objective.
This evolution changes the build-versus-buy decision. Companies are no longer evaluating only whether they can train a model. They must consider data infrastructure, integrations, security, monitoring, governance, user adoption, and ongoing AI operations.
Why the Build-or-Buy Decision Has Changed in 2026
Recent industry research shows that AI adoption is moving toward scaled deployment, although many organizations are still struggling to convert experiments into measurable business value.
McKinsey's 2026 State of AI research reports that 40% of respondents from organizations with more than $1 billion in revenue say they are scaling AI agents, compared with 27% the previous year. The same research also found that nearly one-third of organizations have decided not to purchase certain software products or features because they could build the functionality internally using AI coding tools.
That creates an interesting shift.
AI is simultaneously becoming:
Something companies buy
Something companies build
Something companies hire specialists to implement
And increasingly, something companies use to build other software internally
The right decision therefore depends less on ideology and more on the specific business problem.
When an AI Consultant Makes More Sense
An AI consultant is particularly valuable when an organization is at the beginning of its AI journey.
For example, suppose a manufacturing company wants to reduce equipment downtime using predictive maintenance. The company may have years of machine data but no machine-learning specialists, no established AI architecture, and no experience deploying models into production.
Hiring an entire AI department before proving the business case could be expensive and slow.
An experienced AI consulting team can instead:
Identify the highest-value use case.
Assess the available data.
Design the technical architecture.
Build a proof of concept.
Measure business impact.
Deploy the solution.
Document the system and transfer knowledge to the internal team.
This approach reduces the initial commitment while giving the organization practical experience.
Consultants can also provide specialized expertise when a project requires skills that the existing engineering team does not have, such as retrieval-augmented generation, AI agents, model evaluation, MLOps, data pipelines, or complex enterprise integrations.
When Building an In-House AI Team Is Better
An internal team becomes more attractive when AI is no longer an experiment but an ongoing business capability.
Consider a software company that expects to develop dozens of AI-powered features over the next several years. Hiring permanent machine-learning engineers, AI engineers, data engineers, and AI product specialists may make more sense than commissioning every project externally.
An in-house team offers several advantages.
Deep business knowledge
Internal employees understand the company's customers, data, products, processes, and constraints.
Long-term ownership
The organization develops its own technical knowledge rather than relying entirely on external expertise.
Faster iteration
Once the team is established, it can experiment, test, deploy, and improve systems without starting a new procurement process for every project.
Strategic advantage
If AI itself is part of the company's competitive advantage, keeping the capability internally can be strategically important.
However, an internal team also brings hiring costs, management overhead, infrastructure expenses, retention challenges, and the responsibility for maintaining production systems.
Real-World Applications of AI Consulting
AI consulting is not limited to creating chatbots.
Businesses are using AI across a wide range of workflows.
Customer service
AI assistants can answer routine questions, summarize conversations, recommend responses to human agents, and route complex cases.
For example, Vodafone worked with IBM to improve its TOBi digital assistant using generative AI. The project included workshops to identify use cases for making conversational journey creation and testing more efficient. IBM reports a 99% improvement in turnaround time for journey testing.
Insurance
AI can support claims processing by extracting information from documents, assessing claims, routing cases, and helping employees make faster decisions.
McKinsey's 2026 technology research highlights Aviva as an example. The insurer deployed more than 80 AI models across its claims journey. Reported outcomes included a 23-day reduction in liability-assessment time, a 30% improvement in routing accuracy, and a 65% reduction in customer complaints.
Financial services
Banks and financial institutions can use AI for fraud detection, document analysis, customer support, risk monitoring, compliance, and employee productivity.
The important point is that these systems require more than a model. They must operate within controlled data environments with appropriate security, monitoring, auditability, and human oversight.
Software development
AI coding agents are becoming another important application.
Instead of simply suggesting individual lines of code, newer systems can help developers investigate issues, modify multiple files, generate tests, and work through development tasks.
This is contributing to an important change in the build-versus-buy equation: organizations can increasingly create internal software capabilities faster than they could previously.
Case Study: Klarna and AI-Powered Customer Service
Klarna provides an example of what happens when AI becomes part of a company's core operating model.
Its AI assistant was designed to handle customer-service interactions, including questions related to shopping, payments, refunds, and returns.
According to OpenAI's published case study, during its first month the assistant handled 2.3 million conversations, representing approximately two-thirds of Klarna's customer-service chats. The company reported that the system performed work equivalent to 700 full-time agents and reduced average resolution time from 11 minutes to less than two minutes.
The lesson is not simply that companies should replace customer-service teams with AI.
The more important lesson is that AI works best when it is connected to real workflows, business information, and operational systems.
A company attempting something similar needs more than a chatbot. It needs integration, governance, monitoring, escalation processes, and continuous improvement.
Case Study: Wipro and AI-Assisted Customer Service
Another example comes from Wipro's work on AI-powered customer-service solutions.
Its solution incorporates capabilities such as intelligent call routing, virtual agents, agent assistance, chat summarization, and automated complaint documentation.
IBM reports that a 30-day proof of concept produced an average 30% reduction in wait times, with approximately 20% to 40% of calls potentially automated and about a 25% reduction in call transfers.
This demonstrates an important principle: AI does not have to automate an entire department to generate value.
Improving one stage of a workflow can produce measurable benefits.
The Hybrid Model: Build With Experts, Then Build Internally
For many organizations, the most practical answer is neither "consultant" nor "in-house."
It is both.
A company can engage an AI consulting partner for its first major implementation while simultaneously developing internal capability.
A typical sequence could look like this:
Phase 1: Discover
Identify business problems where AI can produce measurable value.
Phase 2: Validate
Run a focused proof of concept using real business data.
Phase 3: Deploy
Move the successful use case into production with appropriate security, monitoring, and governance.
Phase 4: Transfer
Document the architecture, workflows, evaluation process, and operational procedures.
Phase 5: Build internally
Hire or assign an internal AI team to manage the system and develop additional use cases.
Phase 6: Specialize
Continue using external experts for complex architecture, advanced AI systems, audits, or major transformations.
This approach allows companies to gain speed without creating permanent dependence on an external provider.
What Should Companies Evaluate Before Choosing? Before deciding, leadership should answer five questions:
Do we already have AI talent?
If the company has experienced ML engineers and data engineers, an internal build may be realistic.
How quickly do we need results?
If the organization needs a production system within weeks rather than months, an experienced external team may provide a faster starting point.
How frequently will we build AI systems?
One project is very different from a roadmap containing twenty AI initiatives.
Is AI part of our competitive advantage?
If AI is central to the product, internal ownership becomes more important.
Can we operate the system after launch?
A successful AI project requires monitoring, evaluation, security, retraining, infrastructure management, and ongoing improvement.
If the answer to the final question is no, the organization should include knowledge transfer and long-term support in its implementation plan.
The 2026 Decision: Build, Buy, or Partner?
The AI market has moved beyond the simple question of whether to use artificial intelligence.
The real challenge is creating an operating model that can turn AI into measurable business value.
McKinsey's 2026 research emphasizes that leading organizations are increasingly combining insourcing, reskilling, and targeted hiring rather than relying on a single talent strategy. It also highlights continuing challenges around AI talent, system integration, data foundations, and change management.
The practical answer is therefore straightforward:
Use an AI consultant when you need speed, specialized expertise, or validation. Build an internal team when AI becomes a sustained and strategically important capability. Use a hybrid model when you want to move quickly while developing long-term internal ownership.
The companies most likely to succeed will not necessarily be those that build everything themselves or outsource everything.
They will be the companies that know which capabilities they need to own, which they should buy, and where outside expertise can accelerate the journey.
In 2026, the competitive advantage is no longer simply having access to AI.
It is having the right combination of people, data, technology, processes, and expertise to turn AI into repeatable business results.
This article was originally published on Perceptive Analytics. At Perceptive Analytics our mission is "to enable businesses to unlock value in data." For over 20 years, we've partnered with more than 100 clients — from Fortune 500 companies to mid-sized firms — to solve complex data analytics challenges. Our services include Power BI implementation and underwriting automation, turning data into strategic insight. We would love to talk to you. Do reach out to us.
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