Arbolus | Generative AI Unpacked - Key insights from the webinar

Check out some key insights from our recent webinar on generative AI for Businesses and Investors

At our recent webinar "Generative AI Unpacked: Navigating the Hype for Businesses and Investors" our panelists Carol S. Scott (Senior Director at Microsoft), Barak Turovsky (EiR Scale Venture Partners, former Head of Product at Google) and Johnathan Tran (Managing Director at PwC Strategy&) explored practical examples and use cases of Generative AI for businesses, identified industries that are likely to benefit from its adoption and highlighted key indicators that a business is well-positioned to take advantage of Generative AI.

We summarized key insights from the webinar below, to quickly equip you with valuable perspectives on how to navigate the world of Generative AI effectively.

You can access and watch the full recording here.

‍What’s real and what’s hype in terms of the impact of Generative AI on businesses?

Real aspects of Generative AI's impact on businesses include rapid innovation, personalization, and acceleration of industry transformation in areas like healthcare, gaming, finance, marketing, and advertising. It also impacts how people work across industries and changes day-to-day work processes.

Meanwhile, the hype surrounding Generative AI is the belief that it is a silver bullet that can solve all problems or challenges, unrealistic expectation of immediate ROI, and the oversimplification of guaranteed success.

It is important for businesses to focus on specific use cases and problems they are solving, rather than relying solely on the hype.

"So one thing that is real is rapid innovation, like we have not seen in a long time, and a level of personalization in the industry as well. And so you're starting to see the acceleration of industry transformation in healthcare, gaming, finance, and a lot of use cases coming up."
>Carol S. Scott - Senior Director at Microsoft

What are some practical examples and use cases of how generative AI is currently being used by businesses?

• Customer service: AI-powered chatbots and virtual assistants help answer customer queries, provide support, and improve customer satisfaction. They can handle multiple tasks simultaneously and are available 24/7.
• Content creation: Generative AI can assist in drafting emails, documents, and presentations, saving time and effort for employees while maintaining fluency and eloquence in writing.
• Marketing: AI can generate stock images, create personalized marketing campaigns, and analyze customer data to improve targeting and engagement.
• Workflow and productivity improvement: Generative AI can help automate repetitive tasks, streamline processes, and optimize workflows, leading to increased efficiency and productivity.
• Education and training: AI can generate educational content, provide personalized learning experiences, and assist in training employees on new skills or technologies.
• Decision support: While not yet fully reliable for critical business decisions, generative AI can provide insights and suggestions based on available data, helping users make more informed choices.

"Customer service use case, it's like talking to a walking encyclopedia with empathy who's available 24/7, right? You can’t beat that level of quality."
Johnathan Tran - Managing Director at PwC Strategy&

What industries are most likely to benefit from adopting generative AI, and why? How can businesses that fall behind catch up with early adopters?

Industries most likely to benefit from adopting generative AI include healthcare, gaming, finance, creative industries, marketing and advertising. These industries are seeing rapid innovation, personalization, and industry transformation.

To catch up with early adopters, businesses need to focus on their specific use cases, invest in the right tools and skills, and foster collaboration between generative AI systems and human employees. Additionally, they should be open to adopting new processes and adjusting their strategies to leverage generative AI effectively.

"Companies that will not embrace it, I think they'll face a significant risk of being disrupted by either competitors or newcomers that will be leveraging those Generative AI language models."
Barak Turovsky - EiR at Scale Venture Partners, ex-Google

What challenges should businesses be prepared to face when implementing generative AI, and how can they mitigate those challenges?

1. Adoption and change management: Encourage employees to use generative AI tools and integrate them into their workflows. Provide training and support to help them understand the benefits and capabilities of the technology.
2. Ensuring data quality and accuracy: Invest in data cleaning, pre-processing, and fact-checking tools to reduce inaccuracies and hallucinations in AI-generated content.
3. Balancing human and AI collaboration: Foster collaboration between generative AI systems and human employees by challenging teams to use AI tools for innovation and problem-solving.
4. Protecting intellectual property and privacy: Be cautious about sharing sensitive information with AI systems and ensure compliance with data protection regulations.
5. Evaluating and selecting the right AI solutions: Focus on the problem you're solving, the value proposition of the technology, and the quality of the internal R&D and data science team.
6. Adjusting processes, skills, and tools: Be prepared to invest in new skills and tools, such as highly skilled escalation agents, data scientists, and fact-checking tools, to adapt to the paradigm shift brought about by generative AI.

"You probably will need to invest much more into your data. So you might need to have a much more sophisticated data science or data cleaning, pre-processing department, that looks at the data to make sure we reduce inaccuracy or hallucinations, etc. So it will be a part of that shift, not in terms of “let’s take existing labor and just make it cheaper”, it will be new skills and new tools."
Barak Turovsky - EiR at Scale Venture Partners, ex - Google

"So I wanna build on that related to the tools because you can have the best tool in the world, but we know that change management and getting people to do something different is always a barrier. I always say it's never the technology, it's always the psychology. So I do think when we think of adoption, what will it take to adopt in that industry?” Carol S. Scott - Senior Director at Microsoft

What are some of the key indicators that a business is well-positioned to take advantage of generative AI?

1. A clear value proposition of the technology and a well-defined problem it solves.

2. Customers' understanding of the solution and willingness to pay for it.

3. A well-architected technology with a balance of proprietary and open-source components.

4. Protection through patents and intellectual property.

5. A high-quality internal R&D and data science team.

6. Strong leadership and a clear product roadmap for the next three to five years.

“It’s important for startups to find what’s the value proposition, but the value proposition doesn’t have to be AI.” Barak Turovsky - EiR at Scale Venture Partners, ex-Google

What are some of the potential risks or challenges investors need to be aware of when considering investing in companies that are implementing generative AI?

1. Determining the use case and potential industry impact of the AI solution.
2. Considering the potential for novelty and vaporware in the AI startup space.
3. Understanding the value proposition of the technology and the problem it solves.
4. Assessing customer understanding of the solution and their willingness to pay for it.
5. Evaluating the technology's architecture, including proprietary versus open-source components.
6. Analyzing the protection provided by patents and intellectual property.
7. Assessing the quality of the internal R&D and data science team.
8. Evaluating the company's leadership and product roadmap for the next three to five years.

"Sometimes if things are good enough - yes, we could do this but there is a lot out there already. So is this truly disruptive or is this a nice to have? And then getting into - do you have early adopters? Do you have an interest? Do you have people that are really interested in what you’re doing"
Carol Scott - Senior Director at Microsoft

What are some of the key metrics or KPIs that investors should be tracking when evaluating the success of a generative AI implementation within a business?

1. Cost reduction or productivity gains: Assess if the implementation of generative AI has led to reduced costs or increased productivity in the targeted areas.‍
2. Customer satisfaction: Measure improvements in customer satisfaction, which could be reflected in metrics like Net Promoter Score (NPS).
3. Adoption rate: Evaluate the rate at which the generative AI solution is being adopted within the industry or by the end-users.
4. Quality of technology and team: Assess the quality of the internal R&D and data science team, as well as the proprietary technology, patents, and IP protection.
5. Impact on specific use cases: Identify the relevant metrics for each use case, such as employee satisfaction, employee retention, or time to full productivity for new employees.

"There’s obviously two dimensions that I mentioned, one is - are you able to reduce cost? Meaning productivity gains. And the second one - can you measure, and a lot of companies do measure, better customer satisfaction? This could be Net Promoter Score, etc." - Barak Turovsky - EiR at Scale Venture Partners, ex-Google

"If we’re talking about human-machine interface use case for internal employees, you might be thinking about employee satisfaction, employee retention. How many months does it take for a new salesperson to hit full productivity? Am I cutting that from three months to one? Because I’m helping them get the information they need and helping them customize their outbound emails etc. So I think we have to design and sort of identify the relevant metrics by use case to evaluate whether any given company is doing well and for that need."
Johnathan Tran - Managing Director at PwC Strategy&

How do you see the landscape of generative AI evolving in the next few years, and what implications might that have for investors?

The landscape of generative AI is expected to evolve rapidly in the next few years, with a focus on democratization, industry transformation, and increased adoption across various sectors. This will lead to better customer experiences, cost reductions, and productivity gains.

For investors, this implies the need to identify companies with a clear value proposition, strong technology architecture, and the ability to execute on their vision. Additionally, investors should consider the potential for disruption in industries that are slow to adopt generative AI and the need for new skills and tools to support this paradigm shift.

Be prepared to ChatGPT your competition or your competition will ChatGPT you. - Barak Turovsky - EiR at Scale Venture Partners, ex-Google

Watch the full webinar recording here.

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