I am thinking of starting a company that employs generative AI but I am not sure whether to do it. It seems so easy to get off the ground. But if it is so easy for me, won’t it be easy for others too? 

This year, more entrepreneurs have asked me this question than any other. Part of what is so exciting about generative AI is that the upsides seem limitless. For instance, if you have managed to create an AI model that has some kind of general language reasoning ability, you have a piece of intelligence that can potentially be adapted toward various new products that could also leverage this ability — like screen writing, marketing materials, teaching software, customer service, and more.

For example, the software company Luka built an AI companion called Replika that enables customers to have open-ended conversations with an “AI friend.” Because the technology was so powerful, managers at Luka began receiving inbound requests to provide a white label enterprise solution for businesses wishing to improve their chatbot customer service. In the end, Luka’s managers used the same underlying technology to spin off both an enterprise solution and a direct-to-consumer AI dating app (think Tinder, but for “dating” AI characters).

In deciding whether a generative AI company is for you, I recommend establishing answers to the following two big questions: 1) Will your company compete on foundational models, or on top-layer applications that leverage these foundational models? And 2) Where along the continuum between a highly scripted solution and a highly generative solution will your company be located? Depending on your answers to these two questions, there will be long-lasting implications for your ability to defend yourself against the competition.

Foundational Models or Apps?

Tech giants are now renting out their most generalizable proprietary models — i.e., “foundational models” — and companies like Eluether.ai and Stability AI are providing open-source versions of these foundational models at a fraction of the cost. Foundational models are becoming commoditized, and only a few startups can afford to compete in this space.

You may think that foundational models are the most attractive, because they will be widely used and their many applications will provide lucrative opportunities for growth. What is more, we are living in exciting times where some of the most sophisticated AI is already available “off the shelf” to get started with.

Entrepreneurs who want to base their company on foundational models are in for a challenge, though. As in any commoditized market, the companies that will survive are those that offer unbundled offerings for cheap or that deliver increasingly enhanced capabilities. For example, speech-to-text APIs like Deepgram and Assembly AI compete not only with each other but with the likes of Amazon and Google in part by offering cheaper, unbundled solutions. Even so, these firms are in a fierce war on price, speed, model accuracy, and other features. In contrast, tech giants like Amazon, Meta, and Google make significant R&D investments that enable them to relentlessly deliver cutting-edge advances in image, language, and (increasingly) audio and video reasoning. For instance, it is estimated that OpenAI spent anywhere between $2 and $12 million to computationally train ChatGPT — and this is just one of several APIs that they offer, with more on the way.

Instead of competing on increasingly commoditized foundational models, most startups should differentiate themselves by offering “top layer” software applications that leverage other companies’ foundational models. They can do this by fine-tuning foundational models on their own high quality, proprietary datasets that are unique to their customer solution, to provide high value to customers.

For instance, the marketing content creator, Jasper AI, grew to unicorn status largely by leveraging foundational models from OpenAI. To this day, the firm uses OpenAI to help customers generate content for blogs, social media posts, website copy and more. At the same time, the app is tailored for their marketer and copywriter customers, providing specialized marketing content. The company also provides other specialized tools, like an editor that multiple team members can work on in tandem. Now that the company has gained traction, going forward it can afford to spend more of its resources on reducing its dependency on the foundational models that enabled it to grow in the first place.

Since the top-layer apps are where these companies find their competitive advantage, they lie in a delicate balance between protecting the privacy of their datasets from large tech players even as they rely on these players for foundational models. Given this, some startups may be tempted to build their own in-house foundational models. Yet, this is unlikely to be a good use of precious startup funds, given the challenges noted above. Most startups are better off leveraging foundational models to grow fast, instead of reinventing the wheel.

From Scripted to Generative

Your company will need to live somewhere along a continuum from a purely scripted solution to a purely generative one. Scripted solutions involve selecting an appropriate response from a dataset of predefined, scripted responses, whereas generative ones involve generating new, unique responses from scratch.

Scripted solutions are safer and constrained, but also less creative and human-like, whereas generative solutions are riskier and unconstrained, but also more creative and human-like. More scripted approaches are necessary for certain use-cases and industries, like medical and educational applications, where there need to be clear guardrails on what the app can do. Yet, when the script reaches its limit, users may lose their engagement and customer retention may suffer. Moreover, it is more challenging to grow a scripted solution because you constrain yourself right from the start, limiting your options down the road.

On the other hand, more generative solutions carry their own challenges. Because AI-based offerings include intelligence, there are more degrees of freedom in how consumers can interact with them, increasing the risks. For example, one married father tragically committed suicide following a conversation with an AI chatbot app, Chai, that encouraged him to sacrifice himself to save the planet. The app leveraged a foundational language model (a bespoke version of GPT-4) from EluetherAI. The founders of Chai have since modified the app to so that mentions of suicidal ideation are served with helpful text. Interestingly, one of the founders of Chai, Thomas Rianlan, took the blame, saying: “It wouldn’t be accurate to blame EleutherAI’s model for this tragic story, as all the optimization towards being more emotional, fun and engaging are the result of our efforts.”

It is challenging for managers to anticipate all the ways in which things can go wrong with a highly generative app, given the “black box” nature of the underlying AI. Doing so involves anticipating risky scenarios that may be highly rare. One way of anticipating such cases is to pay human annotators to screen content for potentially harmful categories, such as sex, hate speech, violence, self-harm, and harassment, then use these labels to train models that automatically flag such content. Yet, it is still difficult to come up with an exhaustive taxonomy. Thus, managers who deploy highly generative solutions must be prepared to proactively anticipate the risks, which can be both difficult and expensive. The same goes for if later you decide to offer your solution as a service to other companies.

Because a fully generative solution is closer to natural, human-like intelligence, it is more attractive from the standpoint of retention and growth, because it is more engaging and can be applied to more new use cases.

• • •

Many entrepreneurs are considering starting companies that leverage the latest generative AI technology, but they must ask themselves whether they have what it takes to compete on increasingly commoditized foundational models, or whether they should instead differentiate on an app that leverages these models.

They must also consider what type of app they want to offer on the continuum from a highly scripted to a highly generative solution, given the different pros and cons accompanying each. Offering a more scripted solution may be safer but limit their retention and growth options, whereas offering a more generative solution is fraught with risk but is more engaging and flexible.

We hope that entrepreneurs will ask these questions before diving into their first generative AI venture, so that they can make informed decisions about what kind of company they want to be, scale fast, and maintain long-term defensibility.

In 2018, Rick Smith, founder and CEO of Axon, the Scottsdale, Arizona-based manufacturer of Taser weapons and body cameras, became concerned that advances in technology were creating new and challenging ethical issues. So, he set up an independent AI ethics board made up of ethicists, AI experts, public policy specialists, and representatives of law enforcement to provide recommendations to Axon’s management. In 2019, the board recommended against adding facial recognition technology to the company’s line of body cameras, and in 2020, it provided guidelines regarding the use of automated license plate recognition technology. Axon’s management followed both recommendations.

In 2022, the board recommended against a management proposal to produce a drone-mounted Taser designed to address mass shootings. After initially accepting the board’s recommendation, the company changed its mind and, in June 2022, in the wake of the Uvalde school shootings, announced it was launching the taser drone program anyway. The board’s response was dramatic: Nine of the 13 members resigned, and they released a letter that outlined their concerns. In response, the company announced a freeze on the project.

As societal expectations grow for the responsible use of digital technologies, firms that promote better practices will have a distinct advantage. According to a 2022 study, 58% of consumers, 60% of employees, and 64% of investors make key decisions based on their beliefs and values. Strengthening your organization’s digital responsibility can drive value creation, and brands regarded as more responsible will enjoy higher levels of stakeholder trust and loyalty. These businesses will sell more products and services, find it easier to recruit staff, and enjoy fruitful relationships with shareholders.

However, many organizations struggle to balance the legitimate but competing stakeholder interests. Key tensions arise between business objectives and responsible digital practices. For example, data localization requirements often contradict with the efficiency ambitions of globally distributed value chains. Ethical and responsible checks and balances that need to be introduced during AI/algorithm development tend to slow down development speed, which can be a problem when time-to-market is of utmost importance. Better data and analytics may enhance service personalization, but at the cost of customer privacy. Risks related to transparency and discrimination issues may dissuade organizations from using algorithms that could help drive cost reductions.

If managed effectively, digital responsibility can protect organizations from threats and open them up to new opportunities. Drawing from our ongoing research into digital transformations and in-depth studies of 12 large European firms across the consumer goods, financial services, information and communication technology, and pharmaceutical sectors who are active in digital responsibility, we derived four best practices to maximize business value and minimize resistance.

1. Anchor digital responsibility within your organizational values.

Digital responsibility commitments can be formulated into a charter that outlines key principles and benchmarks that your organization will adhere to. Start with a basic question: How do you define your digital responsibility objectives? The answer can often be found in your organization’s values, which is articulated in your mission statement or CSR commitments.

According to Jakob Woessner, manager of organizational development and digital transformation at cosmetics and personal care company Weleda, “our values framed what we wanted to do in the digital world, where we set our own limits, where we would go or not go.” The company’s core values are fair treatment, sustainability, integrity, and diversity. So when it came to establishing a robotics process automation program, Weleda executives were careful to ensure that it wasn’t associated with job losses, which would have violated the core value of fair treatment.

2. Extend digital responsibility beyond compliance.

While corporate values provide a useful anchor point for digital responsibility principles, relevant regulations on data privacy, IP rights, and AI cannot be overlooked. Forward-thinking organizations are taking steps to go beyond compliance and improve their behavior in areas such as cybersecurity, data protection, and privacy.

For example, UBS Banking Group’s efforts on data protection were kickstarted by GDPR compliance but have since evolved to focus more broadly on data-management practices, AI ethics, and climate-related financial disclosures. “It’s like puzzle blocks. We started with GDPR and then you just start building upon these blocks and the level moves up constantly,” said Christophe Tummers, head of service line data at the bank.

The key, we have found, is to establish a clear link between digital responsibility and value creation. One way this can be achieved is by complementing compliance efforts with a forward-looking risk-management mindset, especially in areas lacking technical implementation standards or where the law is not yet enforced. For example, Deutsche Telekom (DT) developed its own risk classification system for AI-related projects. The use of AI can expose organizations to risks associated with biased data, unsuitable modeling techniques, or inaccurate decision-making. Understanding the risks and building practices to reduce them are important steps in digital responsibility. DT includes these risks in scorecards used to evaluate technology projects.

Making digital responsibility a shared outcome also helps organizations move beyond compliance. Swiss insurance company Die Mobiliar built an interdisciplinary team consisting of representatives from compliance, business security, data science, and IT architecture.  “We structured our efforts around a common vision where business strategy and personal data work together on proactive value creation,” explains Matthias Brändle, product owner of data science and AI.

3. Set up clear governance.

Getting digital responsibility governance right is not easy. Axon had the right idea when it set up an independent AI ethics board. However, the governance was not properly thought through, so when the company disagreed with the board’s recommendation, it fell into a governance grey area marked by competing interests between the board and management.

Setting up a clear governance structure can minimize such tensions. There is an ongoing debate about whether to create a distinct team for digital responsibility or to weave responsibility throughout the organization.

Pharmaceutical company Merck took the first approach, setting up a digital ethics board to provide guidance on complex matters related to data usage, algorithms, and new digital innovations. It decided to act due to an increasing focus on AI-based approaches in drug discovery and big data applications in human resources and cancer research. The board provides recommendations for action, and any decision going against the board’s recommendation needs to be formally justified and documented.

Global insurance company Swiss Re adopted the second approach, based on the belief that digital responsibility should be part of all of the organization’s activities. “Whenever there is a digital angle, the initiative owner who normally resides in the business is responsible. The business initiative owners are supported by experts in central teams, but the business lines are accountable for its implementation,” explained Lutz Wilhelmy, SwissRe risk and regulation advisor.

Another option we’ve seen is a hybrid model, consisting of a small team of internal and external experts, who guide and support managers within the business lines to operationalize digital responsibility. The benefits of this approach includes raised awareness and distributed accountability throughout the organization.

4. Ensure employees understand digital responsibility.

Today’s employees need to not only appreciate the opportunities and risks of working with different types of technology and data, they must also be able to raise the right questions and have constructive discussions with colleagues.

Educating the workforce on digital responsibility was one of the key priorities of the Otto Group, a German e-commerce enterprise. “Lifelong learning is becoming a success factor for each and every individual, but also for the future viability of the company,” explained Petra Scharner-Wolff, member of the executive board for finance, controlling, and human resources. To kickstart its efforts, Otto developed an organization-wide digital education initiative leveraging a central platform that included scores of videos on topics related to digital ethics, responsible data practices, and how to resolve conflicts.

Learning about digital responsibility presents both a short-term challenge of upskilling the workforce, and a longer-term challenge to create a self-directed learning culture that adapts to the evolving nature of technology. As issues related to digital responsibility rarely happen in a vacuum, we recommend embedding aspects of digital responsibility into ongoing ESG skilling programs,that also focus on promoting ethical behavior considering a broader set of stakeholders. This type of contextual learning can help employees navigate the complex facets of digital responsibility in a more applied and meaningful way.

Your organization’s needs and resources will determine whether you choose to upskill your entire workforce or rely on a few specialists. A balance of both can be ideal providing a strong foundation of digital ethics knowledge and understanding across the organization, while also having experts on hand to provide specialized guidance when needed.

Digital responsibility is fast becoming an imperative for today’s organizations. Success is by no means guaranteed. Yet, by taking a proactive approach, forward-looking organizations can build and maintain responsible practices linked to their use of digital technologies. These practices not only improve digital performance, but also enhance organizational objectives.