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Jun 20, 2023

What We Can Learn About AI from Other Modern Technological Advances


We have already experienced numerous technological revolutions.

Over the past 40 years, a series of major technological advances impacted management consulting, including the widespread adoption of personal computers, exponential expansion in computing power and storage, and the introduction of the internet, mobile computing and cloud computing. These technological advances affected:

Research and Information Gathering
The internet and increased storage capabilities have made information gathering easier and more efficient. We can access a wealth of online information, conduct surveys through the web, and stage virtual group meetings

Data Analysis
We can process large amounts of data more efficiently. Statistical models and simulations that were previously impossible or very time-consuming can now be executed swiftly.

Where and How We Work
The introduction of the internet, advancements in mobile devices, and cloud computing have made remote work possible. We can now collaborate on projects from different locations across the globe and tap into a geographically diverse talent pool. We can easily track progress, manage resources, and coordinate activities across our team.

Client Engagement
The use of digital platforms for communication and presentations has revolutionized client engagement and facilitated far greater client involvement in projects than in the past. That being said, we rarely travel and meet clients in person.

Our Markets
Geographically, we can service a much wider range of markets. We can also expand our service lines to include a broader range of practice areas, including advice on technology and innovation itself. As collecting and analyzing data became easier, firms have had to shift farther along the value-added curve to remain competitive and differentiate their services.

Scalability and Cost Efficiency Cloud computing and a distributed network of associates have made it much easier to scale operations and make them more cost-efficient.

Security and Compliance:
The increased use of technology has also meant that we must pay more attention to cybersecurity and ensure our data management processes comply with requirements.

Required Skill Sets and Our Staffing Composition
Our research group, which once consisted mostly of telephone interviewers, now consists primarily of data analysts and developers. Word processors and proofreaders have disappeared to be replaced by graphic designers and experts in particular software. We have been able to automate some repetitive tasks, such as harvesting RFPs from bidding sites and compiling proposals.

Much of what we do has not changed fundamentally.

Similar Projects
Many of our projects are surprisingly similar to what we did ten, twenty and thirty years ago: evaluations and impact assessments; strategy and marketing studies; industry and economic development studies; and organizational reviews. The report is still the primary output of many projects.

Similar Business Model
Many of our projects are surprisingly similar to what we did ten, twenty and thirty years ago: evaluations and impact assessments; strategy and marketing studies; industry and economic development studies; and organizational reviews. The report is still the primary output of many projects.

Similar Role
While our practice is more solutions-focused than in the past, our broadly defined role in many of these projects has not changed materially. We:

  1. Meet with client to better understand their needs in terms of opportunities or issues they want us to review;
  2. Convert those issues into research questions that we will work to address;
  3. Determine what information is needed to address those issues and how we will collect that data (e.g., literature review, document review, surveys, interviews, focus groups) and develop a work plan that outlines how we will do that;
  4. Implement the work plan and compile the data;
  5. Analyze the data;
  6. Add value to it and prepare an output, typically as a report;
  7. Present the results to them and outline what next steps should be taken.

Key elements are still very time intensive:Certain elements of the research have remained very time intensive such as coding of interviews and open-ended survey questions, literature reviews, summaries of group sessions, and report writing. Reports are shorter in length but may take longer to write (as Mark Twain once said, “I didn't have time to write a short letter, so I wrote a long one instead”). While tools have greatly streamlined the analytical process, we have responded by conducting more detailed analysis.

Margins temporarily improved but soon returned to the same levels: While technology has improved productivity and raised margins for early adopters, most of the benefits of that improvement eventually passed on to consumers. Margins in the consulting industry have not changed materially with the advent of new technology. This could be attributed to increased price sensitivity from clients, demand for more value-based pricing models, and growing competition from boutique consulting firms and in-house resources.

What predictions can we make about AI from reviewing the adoptions of these recent technologies?

There will be a significant learning curve.
ChaptGPT has made AI accessible and easy to use. However, it is not easy to use well. Becoming an expert in prompt engineering will be like how we had to learn how to get Ask Jeeves or AltaVista to give us what we wanted in the days before Google Search. This struggle will soon become trivial. But adapting our business processes and practices to effectively integrate AI will take longer.

Significant adoption will happen more slowly than many of us expect.
To the extent that AI continues to be seamlessly built into the core features of our existing software (e.g., Office 365), adoption will happen relatively quickly. However, adoption will happen more slowly when it requires the adoption of new software or major new features and even more slowly if it requires changes to our business processes and practices (i.e. how we do things). The highly decentralized nature of the consulting industry will slow adoption. Individual consultants and small firms tend to have less time, resources, and motivation to adopt new software, practices, and processes.

At some point (perhaps soon), there will be a series of articles about how AI was overhyped.
Given high levels of investment into AI, both by existing and new companies, it is almost certain there will be articles about how investors are disappointed in the financial returns and how the benefits didn’t meet expectations. Slow adopters will feel justified that they did not move more quickly.

However, firms that do integrate AI into their processes will gain a competitive advantage.
Over time, those benefits may erode as AI becomes more widely adopted by competing firms and clients. However, more so than any previous technologies, AI may offer strategic opportunities to build a moat around these competitive advantages. Consulting has always been difficult to scale because of the reliance on individual expertise; AI offers an opportunity to scale that expertise.

Only a portion of the potential gains in productivity from AI will be realized.
One of the lessons associated with previous technologies is that they introduce significant new benefits and costs. For example, the internet significantly improved productivity in areas such as data collection, analysis, collaboration, and meetings. However, it also facilitated email and social media, which absorbed much of the time that was freed up.

AI will play out in ways different than we expect.
Our first thoughts focus on how AI will accelerate and strengthen the business models, services and processes we already have in place. However, the major impacts will come from models, services and processes that do not exist yet today.

The term AI will largely disappear because it will become so ubiquitous that it’s not worth mentioning.
As AI technology becomes more embedded in everything we use, its explicit reference will gradually fade, just like electricity in the previous century. We no longer marvel at the electric bulb, for it has become a ubiquitous part of our lives, essential but rarely mentioned. The same is anticipated for AI, which will quietly, but inexorably, transform into a taken-for-granted backdrop.

In our article next week, we will dive further into what we can learn from older technological revolutions such as the industrial revolution and the advent of the combustion engine.

All images in this article were produced by Midjourney’s generative AI.

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