Bracing for and Embracing Generative AI in your Business

Eva Schönleitner is a Senior Executive business leader in the B2B software and operational technology segments. Her expertise lies in the areas of commercialization, international expansion, and digitalization with focus on SAAS, cloud, and AI technologies. Throughout her 20-year career, Eva has held executive roles at global IT and OT companies and was CEO of a technology scale-up for real-time analytics and AI applications. Eva now collaborates with VC and growth equity firms to advise technology and industrial companies and serves on boards as non-executive director. She splits her time between Seattle, US and Zurich, Switzerland.

AI and especially Generative AI have been in the technology headlines seemingly daily this year and you are asking yourself whether this is “just” an over-hyped fad that will give way to the next new “shiny toy” or whether these new technologies are a true enabler for your business.

Yes, no, and yes! Yes, AI and generative AI have been over-hyped this year to the point where one might believe there are no other relevant technologies to consider. However, AI is definitely not a fad, and your business can materially benefit from AI enabled solutions in the coming years.

Market size, trends and importance to the business

The global AI market is already at $207.9 Billion this year¹ and predicted to reach $1.85 Trillion by 2030, growing on average by 33% every year.

AI has evolved out of analytics and machine learning applications, it is the cornerstone of digitalization, the utilization of the vast amounts of data we have at our fingertips, and the ability to gain insights rapidly (often in real-time).

Companies have started adopting AI as part of their digitalization strategy over the last few years to either enable new business models or to optimize operations. Industries leading the adoption include retail and consumer goods, financial services, and high-tech and telecom, but the industrial segments, pharma, healthcare, automotive, energy, transport & logistics are not far behind.

Looking forward just two years, IDC predicts that over 40% of core IT budgets will be spent on AI initiatives in G2000 companies, and that technology providers will allocate 50% of R&D, staffing and CAPEX investments to AI/automation through 2026².

Generative AI, which is a branch of artificial intelligence that involves the creation of new, original content or data using machine learning algorithms and large language models (LLM’s) is truly a groundbreaking technology that will improve customer interactions, significantly enhance operations, and also change the way of working for our workforce in the very near future. I would compare it to the level of innovation that we saw from mainframes to the client/server area in the 70’s and 80’s, and the wide availability of cloud technologies in the 00’s.

The generative artificial intelligence market is valued at $13 Billion this year and expected to reach $109.37 Billion by 2030³. McKinsey estimates that generative AI could enable automation of up to 70% of business activities, across almost all occupations, between now and 2030, adding trillions of dollars in value to the global economy⁴.

The growth will come from existing technology players such as Microsoft, OpenAI, Google, Amazon, or Meta and many new players which we can see evolving today in the startup scene. In Q2 2023 four out of the top 5 largest funding rounds went to generative AI companies⁵. The largest market is the US (>4600 startups and $249BN private investments in 2022) followed by China, the UK, and Israel⁶.

Business applications for generative AI

The top use cases for generative AI as of today are:

Customer service: Provide personalized customer service through chatbots and virtual assistants, automate repetitive customer service tasks like answering frequently asked questions, or transcriptions of audio and video calls. For instance, IKEA handles 47% of customer queries with AI bots already; note that they did not lay off their staff but retrained them as interior design advisors⁷.
Content creation: Create high-quality content such as articles, reports, speeches, draft email responses and even resumes.
Marketing: Create personalized marketing campaigns that target specific audiences and generate content for social media and other marketing channels.
Code and analysis generation: Remove the need for lower end coding skills, handle routine coding tasks, enhance quality and reliability of code, and provide code snippets, functions, or entire applications with genAI tools for developers.
Product design: Support new product design by generating ideas and prototypes based on user input and other data.
Data analytics: Analyze large amounts of data quickly and accurately, also identify patterns and trends in data that might not be immediately apparent.
Decision Support: Crunch large amounts of data and distill actionable pathways; this has become especially interesting in the healthcare sector. An example would be physicians receiving decision support on whether to use certain drugs for critical care and whether those medications likely would help their patients.

Emerging risks and concerns

It also becomes apparent, that along with the new possibilities, there are concerns and new risks to consider including privacy, intellectual property, ethical challenges, and bias.

Generative AI has the potential to process personal data and generate sensitive information, which can lead to privacy concerns. For instance, generative AI chatbots can inadvertently collect personal data such as names, addresses, and contact details of a customer.

Intellectual property concerns have already arisen including copyright violations of creative work, ownership of work created by an AI, and the use of confidential materials in the data for the learning model.

Ethical concerns arise particularly regarding the misuse of AI-generated content. The ability to create highly realistic deepfakes and manipulated media has significant implications for misinformation campaigns, identity theft, and damage to an individual’s reputation or privacy.

Bias refers to the case where the learning model has uneven representation or creates stereotypes. An example could be an AI program that reviews best candidates based on a job description and favors one gender, age range, etc. It is not doing this out of spite but based on the parameters it received to assess the candidates. As AI adopts more complex roles, bias will be harder to identify and fix.

Addressing these very real concerns requires a multi-faceted approach involving the technology providers, policy makers, and businesses using AI software. Technology companies are starting to focus on the topic through continuous enhancements and even legal assurance of their software. Many governments including the United Nations, the EU, and recently also the US have already or are introducing regulations, ethical guidelines, and responsible AI practices.

Where to begin?

Have you missed the train on Generative AI already? Absolutely not. Generative AI is in the early stages of the adoption cycle – from the availability of enhanced existing business applications to completely new products. However, considering the importance the market, technology providers, and investors place on the technology, it is critical that your company incorporate the topic and to start now.

You will want to start at multiple levels in your organization:

Provide a platform for generative AI at the board and CXO level: Incorporate AI into your regular board cadence from an innovation and risk perspective. This can be done in many ways from expanding an existing digital or technology sub-committee to creating a new sub-committee focused on AI. You will require experts from the technical (IT and R&D), legal, business, and cyber-security domains. Ensure that this leadership team develops a governance model for your company in which the organization can then freely innovate.

Provide board and leadership training on AI and generative AI, so this skillset becomes “native” to your senior leadership and is not just contained in a small expert group. Understanding AI is a new fundamental skillset for every board and senior executive team.

Enable and promote deep technical expertise for your teams in R&D, and the offices of the CIO and CDO (both digital and data officers). You will achieve this by combining key hires in the AI field and upskilling your existing technical workforce. The latter is especially important as there are (as of today) very few true AI and generative AI experts, and it is key for your developer community to gain AI skills for long-term retention and success in their careers.

Realize that the role of your developers will change significantly in the very near future and proactively support the change. Generative AI tools can write code, interpret existing code, test, and find bugs a magnitude faster than a human ever could. Forrester estimates that this year (2023) AI powered software tools will write 10% of the world’s code and tests⁸. Those tools will not eliminate the need for having developers, but the developer role will change to focus more on the design of code structures, complex applications, and tasks that require human creativity, intuition, or specific domain knowledge. In addition, developers will have to QA AI generated code to ensure the outcome meets the desired needs.

Foster even deeper collaboration between the business functions and IT teams. Expect to see AI enhancements throughout most of the existing applications you are using today in your business. Your business and IT leaders will want to work with the technology vendors to understand AI related product roadmaps, so innovations can be continuously implemented.

Allow experimentation, trials, and pilots. As with any digitalization effort, allowing the teams to test, try, then pilot at a small scale is key. Considering the existing technology providers will continuously enhance their solutions and the thousands of startups entering the market every year, it’s important to test out different tools/applications/solutions and to see which set of technologies work best for your company.

Increase responsibility of your CISO to include the AI and especially the generative AI topic to ensure the appropriate safeguards are in place from a governance and software architecture perspectives.

Measure/measure/measure. 65% of US executives surveyed by KPMG in March 2023 expect generative AI to have a significant impact on the business⁹, but as mentioned earlier we are in the very early stages of adoption. Building measurement systems into the initiatives is critical to deducing the true impact on the bottom line.

Try some of the generative AI tools out yourself privately. One of the easiest ways to get familiar with the topic is to try it out yourself. This is possible since generative AI has many consumer-focused use cases too. So download the “ChatGPT” app (by OpenAI) onto your smartphone, open up the Microsoft Edge browser and type in “BingChat”, or do the same in Google’s Chrome browser and “Bard”– just to name a few. Immediately ask the tools what the top 10 generative AI tools are and compare the answers – then try one or 2 other ones too. Draft out an upcoming birthday speech, a holiday letter, try a mock cover letter, or ask complex question and you’ll see what the hype is all about. You will likely also see some limitations, but I won’t spoil the fun for you to find out yourself.

A final note: This summary was not drafted or written by a GenAI tool. 😊

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