Disruption has been a fearful term that is used more and more often when talking about the transformative force of Generative artificial intelligence (GenAI) in Professional Service Firms (PSFs), under which Consultancy firms. Why fearful? The popularity of natural language processing (NPL) technologies – which is a type of Generative AI – is growing increasingly. It has the astonishing ability to generate novel content (images, text, etc.) to answer complicated questions and give solutions with such a speed that the human mind cannot match. GenAI systems make use of models that have been trained on extensive datasets to comprehend the patterns and relationships found in the data. This enables the tool to only produce solutions and answers that are similar to the training data. As a result of this fearful disruption, consultancy firms are increasingly adopting this state-of-the-art technology to assist them in their daily work. Certainly, this will have an impact on the status quo of consultancy firms and their corporate hierarchy. However, is this new technology so advanced that it will replace the trust in human expertise completely? Today, public and in-house language tools are progressively used in consultancy, but not all ethical and practical implications are yet overcome. To analyze this in more detail, a Senior Consultant operative in the Netherlands was asked to share her insights and point out what exactly is fearful using the two types of GenAI.

Public/Open Language Tools

Changing the status quo, is there a transformative paradigm?

The use of publicly owned and openly used language models such as ChatGPT, Bard, Quillbot or Agros Multilingual are growing for consultants’ daily tasks, such as paraphrasing, enhancing sentence structure, translations and the writing of introductions. Indeed, these technologies greatly help consultants do the same amount of work, in a shorter amount of time. You need less and less people to complete the tasks: The status quo of consulting is bound to change. The interview with a senior consultant confirmed this transformative paradigm: “Consultants will likely lose their jobs; as a result, layers of the corporate hierarchy will get thinner, but they won’t disappear.” Your level in the corporate hierarchy is a representation of your responsibility, based on your expertise and build-up trust, in the firm and by your clients.

The operational gap

Clearly, an operational gap should be identified between the rather simple and efficient tasks that GenAI is allowed to perform, and the tasks for which the human eye remains essential. Ethical considerations are the reason that this gap still exists. The use of GenAI remains an ethical uncertainty, as its trustworthiness cannot be determined or guaranteed. This is the reason that the public GenAI operates with a confidentiality risk. At the firm of an interviewed expert: “Open language tools, such as ChatGPT and Google Translate are not allowed to be used. Because it can give too much client information, even sharing the smallest bits, is not allowed.”

All data you enter, you are giving consent to become available training data, on which other answers and possible solutions generated by that tool will be based. In addition, opacity is the lack of transparency that AI algorithms are able to provide, regarding generating operations and reasoning to their users. This contradicts directly a core responsibility of the consultant, being able to explain your sources and information at all stages of their consulting research. A connecting hazard to this is the mixture of factual and non-factual data that makes the credibility of information generated through language tools questionable. Evidently, the use of open GenAI is still quite limited in presumably taking over all tasks of a consultant. The human eye is required to be the controlling factor ensuring the trustworthiness and accountability of the firm towards clients.

Private/in-house Language Tools

In-house language tools:

“Bullshit in, is bullshit out…” as was quoted by the interviewed expert. She reflected upon the tendency of PSFs to make large investments in GenAI in-house tools. For instance, PwC has invested 1 billion dollars in their own (pilot) ChatGPT and EY 1.4 billion dollars in an AI platform and announced future spending in their language model tools. Manifestly, creating a more contained, secured, transparent, and controllable GenAI tool that can solve most security and confidentiality issues, while generating solutions on valid data. As confirmed by the interviewed expert, this disruption would hypothetically have a great impact on the transformative paradigm as it could close the operational gap. Noteworthy, the ability to have this great impact will be limited to the individual large (international) PSFs that have abundant sets of data and the funds to create, manage and improve these language models continuously. But again, not without (ethical) risks.

A limited investment

All language models will be as big or limited as their dataset itself is. This means that the quality of your in-house generated solutions will be based and trained on the companies’ data only. Hence, the idea of bullshit in is bullshit out. To add, external factors and awareness are excluded to keep the data contained, limiting the ability to research or find undiscovered information, a quality for which a public language tool like ChatGPT is explicitly valued. Most disappointingly, it will depend on client confidentiality agreements whether their data can be shared (anonymously) with the in-house tool. “With current strict security and confidentiality agreements, sometimes team members are not allowed to view all data available” let alone all employees of a large (international) consultancy firm. Despite the promising efforts by large PSFs, overcoming a predominant confidentiality problem with an in-house trustworthy GenAI tool is not yet guaranteed.

In conclusion, the application of Generative Artificial Intelligence (GenAI) in consulting raises both the prospect of a paradigm shift and ethical issues. Public language tools improve productivity and effectiveness but pose concerns about confidentiality and transparency. Large corporations are investing a lot of money in in-house technologies to address the operational gap and offer solutions that are transparently and securely regulated. The phrase “bullshit in, bullshit out” perfectly captures the problem of only using data that is internally confined. In-house GenAI technologies have a lot of work to do before they can be fully trusted and accounted for, despite their potential. To maintain its credibility while negotiating the changing context of human-machine collaboration, the consultant industry must still strike a balance between productivity and ethical considerations, to ensure its trustworthiness.

Acknowledgement Statement: This blog is part of the student competition in Management Consulting Master Program at the School of Business and Economics.

AI is changing the world

AI is changing the world. Maybe, experiencing these changes from the inside, as citizens of the game changer superpowers, we don’t really realize the violent impact of disruption on our daily life. The violent impact will come when, while cooking dinner for our beloved family we realize that an Artificial Intelligence, linked to the watch of our children, through monitoring pulse rate, body heat and oxygen in the blood, will be able to recommend the best cartoon for them to watch or the best vegetable to eat. And just at that point we will really think “ok, what’s happening here?”. But for now, seeing these changes in “rallenty”, reading about one “small” innovation at a time (just because we can’t read simultaneously more than one word at a time), doesn’t really seem to shake us. Well, long story short, the world (and the business world firstly) is changing and it’s changing fast.

When speaking about innovative practices, people can lag behind, Businesses can’t

We, as people, can surely remain back a few steps without risking bankruptcy. Businesses can’t. Nevertheless, when the businesses on which we focus are consultancy societies that, in order to survive, formulate strategies to make other societies stand out, within a sea of competitive societies backed up by other societies. In this mess,it is inevitable that consultancy firms must not only keep up with innovation, but even direct and guide it. Regarding this last statement I’m pretty sure they will. How? Well for two main reasons, they are constantly immersed in a network of stakeholders (clients and partners) who are informed or need to be informed regarding cutting-edge technologies and trends and because of their human-centered nature (with relatively low fixed assets) that allows them to implement new methodologies or sell new products in a fast and nimble way. Now, when we speak of implementing new methodologies and selling new products, what are we referring to? Well, in this article, we are referring to implementing and selling Artificial Intelligence languages.

How can new languages be created? A chat with Carlo

In order to understand how new languages are created, I interviewed my long-time friend Carlo, Master of Science in Machine Learning and Research Engineer at CISPA Helmholtz Center for Information Security. In simple words Carlos’s daily job involves adjusting leaks and improving the state of the art of AI and deep learning models. I interviewed Carlo in order to understand what it means for Consultancy Firms to create their own AI language and what are the variables involved. Speaking with Carlo I understood that there are two main protagonists in this process, the data (the oil) and the model (the engine). While the parameters of the models can be obtained and used by all, since societies such as Meta make them public and useable (we are speaking of pre-trained, large and generalized language models), the data needed to make the model speak the “consultancy language” (through the “fine-tuning” process) is more difficult to be found. For this reason it is more likely that big companies such as PwC, EY, KPMG, that already have tons of terabytes of in-house data can more easily get the engine to work. Small companies instead, are obligated to download it from public online sources such as Kaggle or Wiki English that still provide great quantities of good quality data. This doesn’t mean that small companies can’t do it, it just means that for them it’s probably going to be more costly. So once the consultancy company in object has on the one hand Meta’s pre-trained AI model and, on the other hand, the data, Carlo takes the field. His role is to use the data collected making the model speak the strategic consulting or marketing consulting or HR consulting language (depending on the data collected), delivering a product that can increase the efficiency of work within these companies. And how does he do this? Through the fine-tuning; a process that consists in training the pre-trained, large and generalized language model and making it better suited for a particular application.

Deep diving the apparently impossible process

This process involves the use of more than 20 GPUs (Graphic Processing Units, the billions of operations needed to train these models are performed on specialized computers built for quick and parallel processing). Once the model has been trained to serve a particular mansion or “speak” a particular language, the model can be prompted and used to generate new text or voice. What happens in this second step (called inference or prediction) is often referred to as a black box, since the human mind can’t really understand what is going on under the hood and why exactly the model is generating a single specific output (but this is another story). So once the fine tuned model is created, it needs to be put in production. Putting a fine-tuned model into production is a complex process. Through CI/CD pipelines, which means Continuous Improvement and Continuous Development, the model is updated and trained with data and questions, and every time a new output is generated, feedback on that output is feeded back to the model, to ensure the full lifecycle of the CI/CD pipeline. This helps ensure the model works effectively and doesn’t give unwanted answers (such as racist answers or information on how to conduct illegal activities). Finally, after completing this phase, the model is ready to be used by the consultancy firms (while needing continuous training with new and updated data).

The outputs of the impossible process, the case of Lilli

This process, which seems long and expensive, is actually doable, thanks to the work of specialized engineers and consultants, such as Carlo, that concludes the interview making his own prediction: “I think the major consultancy companies, in five years, will all have ad hoc fine tuned AI models integrated into their systems. Their employees will use them to boost their performance working in a more efficient and data driven environment.” And someone already started! McKinsey, this year launched “Lilli,” an own generative AI solution that aggregates all Mckinsey’s knowledge and capabilities in one place. The data used to train Lilli relies on more than 100.000 documents and interview transcripts that the firm collected during years of hard work. For Adi Pradhan, an associate partner who specializes in technology strategy and transformations, Lilli is “a thought-sparring partner” ahead of meetings and presentations. He uses Lilli to look for weaknesses in arguments and anticipate questions that may arise, to tutor himself on new topics and make connections between different areas on the projects.

Acknowledgement Statement

This blog is part of the student writing competition in Management Consulting Master Program at the School of Business and Economics.

References

• How Professionals Adapt to Artificial Intelligence: The Role of Intertwined Boundary Work, Journal of Management, 2023
  [https://onlinelibrary.wiley.com/doi/epdf/10.1111/joms.12936]
• How professional services firms dodged disruption, MIT Sloan Management Review, 2023
  [https://sloanreview.mit.edu/article/how-professional-services-firms-dodged-disruption/]
• Consultants Emerge as Early Winners in Generative AI Boom, The Wall Street Journal, 2023
  [https://www.wsj.com/articles/consultants-emerge-as-early-winners-in-generative-ai-boom-8df71d38]
• Killing Strategy: The Disruption Of Management Consulting, CBINSIGHTS, 2020
  [https://www.cbinsights.com/research/disrupting-management-consulting/ ]
• Revolutionizing Decision-Making: How AI and Data Analytics are Transforming Management Consulting Practices, Medium.com, 2023
  [https://medium.com/accelerated-analyst/revolutionizing-decision-making-how-ai-and-data-analytics-are-transforming-management-consulting-94b98a8b1e1b]
• Meet Lilli, our generative AI tool that’s a researcher, a time saver, and an inspiration – McKinsey Blog, 2023
  [https://www.mckinsey.com/about-us/new-at-mckinsey-blog/meet-lilli-our-generative-ai-tool]