We’re excited to announce that McKinsey has been named a Leader, the highest ranking we can receive, in The Forrester Wave™ AI Consultancies Q1, 2021 report.
From developing AI-infused solutions and helping organizations recruit the right data-science talent, to transforming companies end-to-end through AI, McKinsey “sets a high bar for business impact and ROI on engagements and drives to create sustainable and growing competency with clients,” wrote Forrester.
Forrester evaluated 13 firms based on 26 criteria that were grouped into the categories of current offering, strategy, and market presence. McKinsey received the highest possible scores in 22 of the 26 criteria, and ranked highest across all three categories. “This recognition is a testament to the high-impact work our teams are doing. Partnering with clients to deliver significant business impact through end-to-end AI transformations is the most important aspect of our work while empowering them to run their own AI solutions and scale them,” says Alex Singla, a McKinsey senior partner who co-leads McKinsey Analytics globally with senior partner Alex Sukharevsky.
In its report, Forrester states that McKinsey brings “‘its secret weapons’…world-leading data-science talent, a factory model for AI development and ModelOps, and a comprehensive playbook to partner and lead the C-suite and boards into the AI age.” They also said that we deliver impact and innovate “with end-to-end, AI infused business solutions that reshape what clients do” in every sector, ranging from manufacturing and retail to mining and financial services—and most recently in sailing: for the defending champions of the America's Cup, Emirates Team New Zealand, we used deep reinforcement learning to develop an AI bot that could sail as well as professional sailors.
Flying across the sea, propelled by AI
“Just a few years ago, people may have been happy with the impact from scattered, siloed pilots, but we’re now helping organizations run hundreds of use-cases using a higher level of programming and pre-built, re-usable code and data libraries which enable true scaling of solutions,” explains Helen Mayhew, a McKinsey partner and leader of QuantumBlack in Europe. “The time for new use cases from invention to production is coming down from years to weeks.”
As an example, we helped a B2B company reinvent its way of working across a global network of 15,000 sales reps: refining messages, channels and cadence, and tracking relevant metrics using AI-based decision making. We helped the client establish a product engineering team within an analytics center of excellence. They built and maintained technical components, such as end-to-end data pipelines and AI models, which smaller data-science teams around the world could customize to improve local customer engagement. They were able to quickly assemble solutions to test live with sales reps frequently, with releases made every two weeks.
Continually refining their management of customers improved the quality of their relationships—resulting in 10 to 15 percent top-line growth—as well as enabled them to run 101 promotional campaigns in a year, compared to being able to run only two campaigns the previous year.
The technical assets we use to accelerate AI development are built in our QuantumBlack Labs. For example, Kedro, an award-winning open-source tool, standardizes data pipelines to speed up building models; Brix captures and shares re-usable code; and PerformanceAI tracks and visualizes how well data models are performing.
In addition, we have devised a playbook of strategies, techniques, and best practices, based on experience leading numerous client projects, that operationalizes each phase of an AI transformation, making the entire process reliable and repeatable across organizations.
This recognition is a testament to the high-impact work our teams are doing.
“A core part of our value proposition is empowering our clients and building lasting capabilities,” says Alex Sukharevsky. “Our horizontal assets like Kedro, and the industry-specific assets built on top of them, solve a lot of the technical basics in an AI transformation. So we can hand them off and train clients to run and update them on their own.”
This handoff means ensuring that the right people and processes are in place. “Assembling the right mix of talent in client tech teams and establishing an engineering culture of continuous learning is a priority, followed by the technologies, tools, and processes to support them,” explains Nayur Khan, an associate partner.
One hallmark of an engineering culture is the organizing of communities of practice so participants can undertake research, share learning, and grow skills. “In most organizations, the tech talent is scattered throughout so the first step is to unite them,” explains Nayur. Other organizational elements include a strong on-boarding program, defined career paths, opportunities to “toggle” between cutting-edge research and development, and on-the-ground client work. To build technical skills and community, we help clients organize hackathons, create skills academies, and, when needed, can establish a free-standing AI/analytics unit.
“The bare minimum to be in this space is no longer just to understand and offer analytics. You have to be at the cutting edge,” says Nicolaus Henke, a senior partner of McKinsey Analytics. “We are constantly scanning the landscape to identify technologies with significant business potential and continually refreshing our own expertise on machine learning, quantum computing and other emerging capabilities.”