Editor’s note: I thought about having ChatGPT create image for this piece… but this screenshot somehow seems better than what it eventually came up with.
The potential for AI to transform a business is immense. Today, design organizations are actively experimenting with new generative tools, researching methods for using their data with AI, and speculating on future impacts to their businesses. Like the many digital transformations discussed on this blog, AI’s ability to impact an organization hinges on a variety of factors associated with adoption and implementation.
However, unlike other digital transformations that have driven change within design, the AI trend has several notable characteristics to be considered that should shape any implementation roadmap. This article will discuss several characteristics that makes AI different from past digital transformations affecting design businesses and outlines tactics for realizing meaningful results.
AI is already part of everyday life.
Unlike digital transformations that have taken hold in the design world, such as CAD and BIM, AI-powered tools have already become very familiar to users in personal phone apps, e-mail, search engines, and entertainment services. It is not uncommon to hear professional colleagues discuss how they used ChatGPT for suggestions on food recipes, gym workout routines, or discover tips on gardening. Moreover, AI-powered features have fueled our digital user experience in consumer goods and services for decades: Netflix movie lists, Amazon product recommendations, and Spotify playlists are ubiquitous in our daily lives and habits with AI-based technologies as the backbone.
This distinction is important because the implementation of AI in a business setting comes along with a number of prior stakeholder experiences and expectations – both good and bad.
It is important for businesses to communicate why they are adopting certain AI-powered capabilities and how they expect stakeholders to play a part in the implementation.
AI strategies should endeavor to establish clear governance and guardrails on their AI implementations. It is important for businesses to communicate why they are adopting certain AI-powered capabilities and how they expect stakeholders to play a part in the implementation. AI adoption can also risk becoming an unmanaged ‘free-for-all’ of individual users wanting to install various new applications. Governance can put clear guidelines in place to mitigate risks to privacy, security, and intellectual property.
You do not need to be an expert to use AI.
When we think about implementing new digital processes in design businesses, we often think about slow changes to process, challenging learning curves, and the need to develop expertise. Computational design workflows exemplify much of this: becoming competent in design tools like Grasshopper requires users to think about an entirely new type of abstract graphical interface to control geometry. This workflow is then further abstracted as the need for custom scripts and software development become necessary to tackle even more complex problems. Today, the computational designer is a niche of specialization in many design firms. Industry roles like ‘BIM manager’ continue to have digital experts supporting implementations of popular design and construction platforms.
AI-powered tools can be observed to have a very different adoption trajectory. AI workflows are appealing to non-experts in ways that BIM and computational design could never claim. Whereas CAD’s command-line required memorization of specific commands, the inputs that enable a user to interact with an AI system like ChatGPT can include natural language and sketch images. Time-consuming image editing operations are being taken up with features like Photoshop’s ‘generative fill’. Complex content curation and management is greatly simplified with tools like NotebookLM that provide interfaces for turning conventional libraries of unstructured PDFs, documents, and images into interactive conversations for search and summarization.
Organizations should aim to cast a ‘wide net’ and involve a range of different stakeholders in the discovery phases of adoption.
The aforementioned topic of AI governance should also be balanced against encouraging users to participate in discovery of use cases. As an emerging technology, AI will have a variety of use cases for different business sectors and be used in different tools. Organizations should aim to cast a ‘wide net’ and involve a range of different stakeholders in the discovery phases of adoption. ‘User groups’ and ‘knowledge network’ models for stakeholder engagement are good tactics for bringing participants with a wide range of experiences to the table to discover where AI is best suited to have impact on the business.
AI’s wider impact on businesses has not been measured.
The conceit of many of today’s AI narratives is that the adoption in business will naturally lead to dramatic changes in businesses – including increases in efficiency and productivity while also disrupting certain status-quo skillsets, team structures, and job roles. We are starting to see anecdotal claims by individual design firms point towards a sense that AI has increased creative output in the form of design iteration and ideation. It has certainly become fashionable to see job requisitions from design firms that indicate AI-based skills and experience.
At this stage of adoption, it is incumbent upon business leaders and stakeholders to calibrate their AI investments with consideration to how they will measure the impact on their operations.
While there have been numerous surveys looking at AI adoption rates and expectations, the actual impact by AI on businesses has not been quantifiably measured at the time of this writing. Predictions on how AI technology will shape the future of design business are just that: predictions. At this stage of adoption, it is incumbent upon business leaders and stakeholders to calibrate their AI investments with consideration to how they will measure the impact on their operations.
Business leaders should establish why they are pursuing the adoption of AI in various business sectors: What will be the measurable outcomes to justify the change? What will be the method for continual evaluation of process improvement? This means observing and capturing the outcomes that are a direct result of AI-based implementation so that future investments and expectations can be calibrated accordingly. Even if it is a given that AI is an inevitable component of today’s design technology, the value delivered by AI is not self-evident and self-realizing.
Even if it is a given that AI is an inevitable component of today’s design technology, the value delivered by AI is not self-evident and self-realizing.
Mobilizing strategic transformation with AI
The availability and uses of AI are already widespread and continue along an early-stage adoption trajectory by businesses. The commentary is this article asserts a few key reasons why the AI trends are different from prior digital transformations. Unlike CAD or BIM, professionals are using AI for purposes inside and outside of their daily work making it important to set up process guardrails and governance within a business. Furthermore, the effective uses of AI is not incumbent on designated AI experts which implies the need for tactics that involve participation from stakeholders from a wider variety of technical and non-technical backgrounds. Finally, while expectations are high for AI, the actual impact of the technology has yet to be measured and it is largely up to businesses to assess why the adoption is worthwhile and how to measure its impact.
Within this context, a common thread is in how to mobilize people – yes, human people like designers, architects, engineers, and managers – to orchestrate implementations that can lead to meaningful change. Digital transformations of the past – as exemplified by CAD, BIM, and computational design – all required deliberate investment that occurred largely through conventional top-down, expert-led strategies. With AI, the roadmap for change will focus heavily on strategies for cultivating bottom-up, user-led strategies will drive many of the outcomes that business leaders are expecting.
A tactical TL;DR…
- Guardrails and governance – Define guardrails and governance strategies to guide stakeholders towards acceptable and meaningful uses of AI.
- Stakeholder engagement – Engage a wide range of stakeholders – including non-experts – (e.g. user groups) to discover uses of AI that will deliver value for the organization.
- Measuring impact – Define methods for measuring the success of AI adoption and evaluate impacts on work. Be prepared to adapt as AI tools continue to evolve.
- Adapting for continual change – Define strategies that support managing continual change through bottom-up, user-led tactics.
How Proving Ground can help…
- We work with architects, engineers, builders, and owners to define strategies and roadmaps for emerging technology like AI.
- We develop frameworks, data standards, and software tools to support novel implementations of AI for design.
- Contact us!