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Where AI Meets BI: The State of Artificial Intelligence at Top Management Consulting Firms

Since 2017, management consulting firm Deloitte has been tracking the advancement of AI across industries through surveys of global business leaders.
Cody Samuels

In 2023’s fifth edition of its State of AI in the Enterprise, the Deloitte dossier reported that 94% of industry leading companies believe AI is critical to their success strategy over the next five years.

The generative AI market is expected to double every other year for the next 10 years, and investment is surging. The global Generative AI market is valued at $13.7 billion as of 2023 and expected to increase to $165 billion by 2032. Venture capital (VC) investment in Generative AI was up 425% from 2020 through 2022 and continues to soar.

On the one hand, AI has significantly expanded the scope of human creativity, and on the other, the technology has ignited deep philosophical debate concerning truth, consciousness, and humanity in the workplace. Just recently, Worky examined OpenAI’s astronomical valuation from $29 billion in 2023 to $80 billion in 2024. Gen AI has been compared by EY to the debut of the iPhone.

Much like Deloitte, every year competing consulting firm McKinsey & Company releases its annual Global Survey of over 1,000 respondents. Conveyed within its most recent report, McKinsey & Company tracks AI’s usage across a host of industries, finding Gen AI adoption (though widely touted) is only moderately rising, though that lift is characterized as steady.

Although not a drastic increase from previous years, one third of respondents say their companies use Gen AI regularly in at least one business function, indicating the new technology is moving beyond public hype to practical applications. However, innovation is central to survival, and companies are using Gen AI to beat the odds  with 78 percent of respondents stating that their current business model would need to change “moderately to completely” in order to remain economically viable by 2025.

Company leadership awareness and utilization on a day to day basis is exponentially growing with nearly a quarter of C-suite executives using Gen AI for work and over a quarter of companies using AI say Gen AI is already on their board’s agenda. This highlights a growing understanding and acceptance of Gen AI’s potential outside of the tech space. Exposure is widespread among employees with 79% of respondents having had some familiarity  with Gen AI, personally or professionally. Meanwhile, 22% of employees at a global scale regularly use Gen AI in their own work.

While generative AI will likely affect most business functions over the longer term, research suggests that information technology, marketing and sales, customer service, and product development are most ripe for the first wave of applications.

  • Information technology. Generative AI can help teams write code and documentation. Already, automated coders on the market have improved developer productivity by more than 50 percent, helping to accelerate software development.
  • Marketing and sales. Teams can use generative AI applications to create content for customer outreach. Within two years, 30 percent of all outbound marketing messages are expected to be developed with the assistance of generative AI systems.
  • Customer service. Natural-sounding, personalized chatbots and virtual assistants can handle customer inquiries, recommend swift resolution, and guide customers to the information they need. Companies such as Salesforce, Dialpad, and Ada have already announced offerings in this area.
  • Product development. Generative AI may be used to rapidly prototype product designs. Life sciences companies, for instance, have already started to explore the use of generative AI to help generate sequences of amino acids and DNA nucleotides to shorten the drug design phase from months to weeks.

While both Deloitte and McKinsey’s reports track an acceleration of Gen AI into the present and future workplace across industries mentioned above, both of the consulting firms’ surveys fail to explore how the large consulting and management industry – raking in well over $300 billion annually of late – have begun to implement state of the art Gen AI technologies into their business practices.

McKinsey’s Gen AI Adjustment for a Digital Future

Founded in 2016 QuantumBlack, McKinsey’s AI wing, has since grown to become a leading provider of AI-powered solutions for businesses, aiming to unlock the power of artificial intelligence (AI) to help organizations hoping to reinvent themselves from the ground up and accelerate sustainable and inclusive growth.

Harnessing the foresight and precision of data and technology with the creativity and understanding of people, McKinsey has deemed their Gen AI work as Hybrid intelligence, a source of competitive advantage that transforms how companies think, operate, and disrupt. Relentlessly focused on real-world impact, McKinsey’s cutting edge solutions paired with deep strategic thinking and domain expertise helped Wayne Brown, the operations manager at the Vistra-owned Martin Lake Power Plant, build and deploy a heat rate optimizer (HRO).

A suite of modular tools that brings software engineering learning to AI, QuantumBlack Horizon is built to be flexible, interoperable, and compatible with all key technology platforms and modern tech-stack components clients already have in place. The suite helps tech leaders achieve four key objectives in their AI initiatives: clean, organized, and accurate data across internal and external sources; the AI models are intended to be scalable, repeatable that build on each other in a factory-like approach to model development and performance transparency that enables quick, reliable decision making.

The tools of proprietary systems are listed below:

  • Iguazio: a holistic machine learning operations platform which includes machine learning operations (MLOps) orchestration, serverless automation, continuous integration and delivery (CI/CD) for machine learning, an online- and offline-feature store, and model monitoring. It extends our open-source tools, including MLRun—Iguazio’s MLOps orchestration framework—and Nuclio, a serverless functions framework.
  • Brix: an internal code-sharing platform for analytics assets, ranging in size from small code snippets to end-to-end data pipelines, ensuring nobody on the QuantumBlack team aims to reinvent the wheel when attempting to solve similar problems.
  • Alloy: a framework for ensuring QuantumBlack’s expanding ecosystem of reusable assets and components are interoperable, maintainable and sustainable.
  • Vizro: provides an extensive library of visualizations and intuitive dashboards to allow frontline business leaders to make decisions and take actions based on model outputs.

Powering Gen AI: QuantumBlack Takes Its Tools into the Energy Sector

In Tatum, Texas circa 2020 during the heart of the pandemic, Wayne and his group worked together with a McKinsey team that included data scientists and machine learning engineers from QuantumBlack AI by McKinsey, to build a multilayered neural-network model—essentially an algorithm powered by AI that learns about the effects of complex nonlinear relationships.

Parsing through two years’ worth of data at the plant, the QuantumBlack team learned which combination of external factors—such as temperature and humidity—and internal decisions, like set points that operators control, would optimize the algorithm and attain the best heat-rate efficiency at any point in time. “Heat rate” is a measure of the thermal efficiency of the plant—the amount of fuel required for each unit of electricity produced. To reach optimal heat rate, plant operators continuously monitor and adjust hundreds of variables or “set points” on controls like steam temperatures, pressures, oxygen levels, and fan speeds. Previously, this was all handled and overseen manually, a herculean task for any operator to get right 100 percent of the time. Vistra thought AI could help.

Through this training process and by introducing “better” data, QuantumBlack’s AI empowered models “learned” to make ever more accurate predictions. When the models were accurate to 99 percent or higher and ran through a rigorous battery of real-world tests, a McKinsey team of machine learning engineers converted said models into a singular AI-powered engine. This ‘engine’ or code generated recommendations every 30 minutes for operators to improve the plant’s heat-rate efficiency.

At a meeting with all of Vistra’s leaders to review the HRO, Lloyd Hughes, a seasoned operations manager at the company’s Odessa plant, said, “There are things that took me 20 years to learn about these power plants. This model learned them in an afternoon.”

With newly implemented power at their fingertips, Wayne and his team could make better, more informed decisions. Acting on the HRO recommendations helped the Martin Lake plant run more than two percent more efficiently after just three months in operation, resulting in $4.5 million per year in savings and 340,000 tons of carbon abated. This carbon reduction was the equivalent of taking 66,000 cars off the road.

If that doesn’t sound like significant improvement and cost management, consider this: companies that build gas-fueled power plants invest millions of dollars in research and development over four to five years to achieve a one-percent improvement in power-generation efficiency. Vistra hit that improvement level in only one-twentieth the amount of time using the data and equipment it already had.

Vistra has since rolled the HRO out to another 67 power-generation units across 26 plants, for an average one-percent improvement in efficiency, and more than $23 million in savings. Along with the other AI initiatives, QuantumBlack’s AI “heat rate: – using McKinsey’s Hybrid intelligence method– have helped Vistra abate about 1.6 million tons of carbon per year, which is ten percent of its remaining 2030 carbon-reduction commitment. That’s equivalent to offsetting about 50 percent of what a 500-megawatt coal plant emits.

What happened at Martin Lake has happened at dozens of Vistra’s other power plants, with more than 400 AI models (and counting) deployed across the company’s fleet to help operators make even better decisions. It also reflects a core trait of Vistra’s AI transformation, which is that it isn’t a story of one massive hit, but rather the story of dozens of meaningful improvements snowballing to deliver significant value in terms of accelerating sustainable and inclusive growth.

It’s also the story of how an organization architected an approach to rapidly scale every successful AI solution across the entire business. A continuous improvement culture combined with a powerful AI modeling capability helped leaders and plant operators do their jobs better than ever before.

To help sustain Vistra’s AI ambitions – nixing costs and generating profits – the company is building up its talent bench, acting as a paradigmatic model for how to retrain its current workforce steeped in Gen AI knowledge. In addition to hiring a small team of data scientists and engineers, Vistra’s Vice President Rachit Gupta has his eye toward the future, partnering with the University of Texas at Dallas to offer basic, intermediate, and advanced courses in AI and analytics for Vistra employees. These courses include reskilling from statistics to machine learning. Gupta has also built relationships with local colleges and universities to develop internship programs and work with students in capstone projects to identify top technical talent, creating a funnel for engaged students’ future employment.

Key Hires in Management Consulting for Gen AI

In an attempt to stay at the forefront of consulting’s Gen AI surging demand for specialists, companies like McKinsey and Deloitte will need to continue to bolster their AI and machine learning hires in a highly competitive market.

  • Machine learning engineer and MLOps: Bridging the gap between computer science and data science, playing a crucial role in developing and deploying effective machine learning (ML) systems that can learn and make predictions from data. They analyze the problem at hand, understand the available data, and choose the most suitable algorithms for building effective models. Familiarity with programming languages (Python, Java, C++, R) as well as machine learning frameworks (TensorFlow, PyTorch, Scikit-learn) and Cloud platforms (AWS, Azure, GCP); Data analysis and visualization tools (Pandas, Matplotlib).
  • Front-end software engineer: focuses on the user interface (UI) and user experience (UX) of websites and applications that users can directly see and interact with, like buttons, menus, text, images, and animations. Strong coding skills are needed in languages like HTML, CSS, and JavaScript as well as other frameworks like React, Angular, or Vue.js. Front-end software engineers should have a comprehensive understanding of design principles and UX best practices and be adept at interfacing with designers and product managers to translate designs into functional code. Engineers may also write and test code, debug issues, and optimize websites for performance, building a wide variety of things, from simple static websites to complex single-page applications (SPAs) and web applications
  • Project managers: While the role is still evolving with regards to Gen AI, project managers handle tasks related to planning, executing, and monitoring projects that leverage generative AI (GenAI) technology, including data acquisition and preparation by ensuring high-quality data collection, cleaning, and pre-processing for effective GenAI training and usage. Additionally, they oversee GenAI model development and training as well as collaborating with data scientists and engineers to build, train, and fine-tune GenAI models for their clients.

Here at Worky we understand that the future of the management consulting business rests at the intersection of Gen AI and human deduction. AI is merely an additional – albeit very powerful – tool to apply to problem areas within any company seeking to move their business forward faster. While Gen AI won’t be replacing mid level to higher level positions within firms, the new technology will likely aid and reduce the need for entry and mid-level analysts to collect and synthesize data. Nevertheless, these same junior roles will also be positively affected by the gains in Gen AI technology, and furthermore, we expect QuantumBlack and similar arms of McKinsey and its competitors to build out their Gen AI capabilities with nothing short of alacrity in a race to service their clients through their proprietary BI and decision models and systems.

If you and your team are looking to connect with topflight AI talent, perhaps without the full heft of a tier one management consulting firm, please don’t hesitate to reach out to us directly. We have assembled a full team of management consulting caliber AI talent to help clients move forward with all things AI at a cost that is more inline with 2024 budgets.

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