hwchase17

Global AI Investments, Initiatives, and Outputs by Major International Consulting Firms: A Comprehensive Analysis

Executive Summary

The global consulting industry has undergone a dramatic transformation with the advent of Artificial Intelligence, with major firms collectively investing over $25 billion in AI capabilities, platforms, and acquisitions since 2023. Leading international consulting firms across the Big Four, MBB (McKinsey, BCG, Bain), technology giants (Accenture, IBM), and global IT services providers have established AI as their primary growth driver, fundamentally reshaping service delivery models and competitive positioning.

This comprehensive analysis reveals that AI has evolved from experimental initiatives to core business transformation engines, with firms generating billions in AI-specific revenue, developing proprietary platforms, and racing to dominate the emerging agentic AI market projected to reach $990 billion by 2027.

Research Methodology: This report is based on compr

hwchase17

Global AI Investments and Initiatives by Major International Consulting Firms: A Comprehensive Analysis

Executive Summary

The global consulting industry has undergone a transformative shift in artificial intelligence investments, with major international firms collectively investing over $10 billion in AI capabilities between 2020-2024. This comprehensive analysis examines AI initiatives across the Big Four (Deloitte, PwC, EY, KPMG), MBB firms (McKinsey, BCG, Bain), and technology-focused consultancies (Accenture, IBM, Capgemini), revealing unprecedented strategic repositioning as these firms transition from traditional labor-intensive models to AI-augmented service delivery.

Global Investment Overview

Aggregate Investment Scale

hwchase17

class AgentState(TypedDict):
    code: str
    tests: str
    errors: Optional[str]

def initial_writer(state):
    ...
    return {"code": ..., "tests": ...}

hwchase17

from langchain.vectorstores import Pinecone
from langchain.embeddings.openai import OpenAIEmbeddings
import pinecone

# The environment should be the one specified next to the API key
# in your Pinecone console
pinecone.init(
    api_key="...", environment="..."
)
index = pinecone.Index("test123")

hwchase17

from langchain.chains.openai_functions import create_structured_output_runnable
from langchain.chat_models import ChatOpenAI
from langchain.prompts import ChatPromptTemplate
from langchain.pydantic_v1 import BaseModel, Field
 
class Insight(BaseModel):
    insight: str = Field(description="""insight""")

chat_model = ChatOpenAI(model_name="gpt-4-1106-preview")

hwchase17

from langchain.chat_models import ChatOpenAI
from langchain.prompts import ChatPromptTemplate
from langchain.schema.output_parser import StrOutputParser
import requests
from bs4 import BeautifulSoup
from langchain.schema.runnable import RunnablePassthrough, RunnableLambda
from langchain.utilities import DuckDuckGoSearchAPIWrapper
import json

RESULTS_PER_QUESTION = 3

hwchase17

from langchain.prompts import PromptTemplate
from langchain.chat_models import ChatAnthropic
from langchain.schema.output_parser import StrOutputParser


#### ROUTER
# This is the router - responsible for chosing what to do
chain = PromptTemplate.from_template("""Given the user question below, classify it as either being about `weather` or `other`.
                                     
Do not respond with more than one word.

hwchase17

from langchain.agents import load_tools
from langchain.agents import initialize_agent
from langchain.agents import AgentType
from langchain.llms import OpenAI

llm = OpenAI(temperature=0, model="gpt-3.5-turbo-instruct")

from metaphor_python import Metaphor

client = Metaphor("")

hwchase17

from langchain import PromptTemplate, OpenAI, LLMChain

prompt_template = "What is a good name for a company that makes {product}?"

llm1 = OpenAI(model="foo", temperature=0)
llm_chain1 = LLMChain(
    llm=llm1,
    prompt=PromptTemplate.from_template(prompt_template)
)

hwchase17

from langchain.prompts import ChatPromptTemplate
from langchain.chat_models import ChatOpenAI
from langchain.schema.output_parser import StrOutputParser
from langchain.vectorstores import Chroma
from langchain.embeddings import OpenAIEmbeddings
from langchain.schema.runnable import RunnablePassthrough
from langchain.schema.runnable import RunnableMap
from langchain.schema import format_document
from typing import AsyncGenerator