AI Deployment For CTOs: An Interview With Petr Baudis
According to a 2023 PwC report, by 2030, artificial intelligence is projected to add as much as $15.7 trillion to the global economy (yes, that’s trillion with a T), with $6.6 trillion of this coming from increased productivity and $9.1 trillion from consumption-side effects.
The future of business hinges on AI, but not as a siloed technology. The CTOs who seamlessly incorporate artificial intelligence platforms into their organizations, maintaining transparency and promoting ethical usage, will lead the most innovative and sustainable enterprises of the future.
In a candid Q&A session, Petr Baudis, CTO at Rossum, shares his insights into the world of AI deployment and the long-term value of sustained investment. Get ready to explore the limitations, challenges, and transformative potential of AI integration – straight from the expert's perspective.
1. What should CTOs consider about their AI deployment strategy?
"When it comes to deploying artificial intelligence, there are several crucial factors that CTOs need to take into account. Let's break it down into a few key considerations:
Data Security and Privacy – One critical aspect is the security and privacy of the data being processed. CTOs should assess whether the data can be stored in the cloud or if it needs to remain on-premises. Additionally, they need to decide whether they can allow others, such as vendors or users, access to this data or if it needs to be kept confidential. The nature of the data plays a pivotal role in this decision.
Ethical Implications – CTOs must understand the types of decisions AI will make, whether they will impact individuals, and whether there is a mechanism for human review. Ensuring that AI systems don't discriminate against individuals or groups is a fundamental ethical concern.
Purpose and Use Cases – CTOs should consider what specific business metrics the AI will help improve and whether any potential roadblocks might hinder its effectiveness.
Learning and Adaptation – Depending on the AI's role, CTOs need to determine whether the AI should have the capability to learn and adapt over time. For instance, a chatbot for a website visitor might not require learning, but a co-pilot AI assisting employees in their daily tasks should be capable of learning and improving its performance. Understanding the mechanics of how the AI learns is critical.
CTOs face the crucial task of outlining the mechanisms governing the learning process. At Rossum, we place a strong emphasis on building our platform so that it’s continuously learning. This ensures it doesn’t repeat the same mistakes it has before, but more importantly, it delivers more accuracy to the end customer.
CTOs should carefully consider and understand these factors to ensure a successful and responsible deployment of AI within their organizations."
2. What emerging AI technologies and applications should businesses be aware of to stay ahead in the future?
"For businesses that are not AI vendors but are looking to leverage artificial intelligence in their daily operations or products, it's essential to strike a balance between staying ahead and not diving too quickly into rapidly evolving technologies.
AI is a dynamic field, and while there are exciting breakthroughs and scientific publications, adopting these technologies prematurely can be risky, especially if you're not directly involved in AI research or development.
An example would be when companies use reinforcement learning in their business, but they are not in the business of developing robots; it can take significant time for these technologies to transition from the lab to practical applications in the industry.
My suggestion is to exercise caution and let the AI vendors do some of the pioneering work for you. Building simple demos or experimenting with AI in a limited scope won't solve real business problems.
Be on the cutting edge of AI trends but remain pragmatic, keeping a close eye on business metrics and ensuring that your AI deployment is secure, ethical, and reliable. This can be a substantial differentiator for your business.
Rather than chasing the latest fads, focus on technologies that have already demonstrated practical applications in real-world scenarios. Be an early adopter, but only when these technologies become genuinely practical and proven, not just experimental. The key is to be on the edge of innovation without venturing too far into the unknown."
3. What long-term value and transformation can businesses expect from sustained AI investment and integration?
"Survival is at stake. Failing to embrace AI is not an option, as your competitors are already moving in this direction. Moreover, entirely new entrants in your industry are leveraging AI, and while they may initially struggle with solving all the business challenges you've encountered, AI can significantly enhance their speed and efficiency, posing a direct threat to your business.
To remain relevant, you must commit to a continuous cycle of innovation. Running at your current pace won't cut it; you'll need to accelerate your efforts and be open to radical transformations within your business.
At Rossum, we envision a future where tasks currently carried out by hundreds or thousands of employees, such as handling business transactions, managing invoices, exchanging emails, and tracking shipments, can be accomplished by a single individual overseeing one million transactions annually.
This vision implies a fundamental shift in the way businesses operate. Those who fail to adapt will face escalating operational costs, making it nearly impossible to maintain competitiveness. In essence, embracing artificial intelligence and transforming your operations is not just a choice but a necessity for long-term survival, and that’s an ROI we’re willing to invest in."
4. What are the current limitations of AI technology, and how can they be overcome?
"One significant limitation of current AI technology is its reliability, which manifests in several ways. For instance, when working with large language models, the issue of reliability is prominent due to their occasional capacity to generate incorrect or inconsistent information, essentially 'hallucinating' or inventing details. This inconsistency hampers their dependability.
Another noteworthy limitation is the dominance of a few major players in the AI ecosystem. When a single entity like OpenAI monopolizes the field, it can engage in monopolistic practices over time, potentially leading to a lack of competition and control issues.
To counter this, the democratization of AI and open-sourcing various AI components, including language models, becomes crucial for a balanced and competitive AI landscape.
Reliability in AI also entails the development of trust between AI systems and their human users. To build this trust, AI must be perceived as a dependable partner for employees and individual workers who rely on it to complete tasks. The key here is establishing trust even when the AI occasionally makes mistakes. Users should be able to understand why these errors occur, empathize with the limitations of artificial intelligence, and observe a continuous learning process to rectify those mistakes.
Furthermore, the aspect of learning within AI is relatively underdeveloped at present, suggesting that AI systems could benefit from more robust and sophisticated learning mechanisms to enhance their reliability and performance."
5. What are the common challenges businesses face when integrating AI, and how can these be addressed?
"The challenges businesses encounter when integrating AI can vary depending on the specific AI technology in question. For the latest advancements, such as generative AI, the absence of established business playbooks poses a significant hurdle. This means that businesses need to be innovative and adaptable, as there's no one-size-fits-all solution available. To navigate this landscape successfully, having the right talent is crucial.
Incorporating generative AI, for instance, demands a different set of skills compared to replicating established playbooks. It necessitates having individuals who can invent and apply this cutting-edge technology to address unique business needs. Hence, fostering a team of inventors within your organization becomes essential.
For more specialized and less universal AI models, certain business playbooks may already exist, but it's vital to focus on vendors who can demonstrate their ability to solve your specific problem effectively.
To address these challenges, businesses should first define the exact business problems they aim to solve with artificial intelligence. Once identified, they should seek AI-centric tools that offer end-to-end solutions for these problems. Relying on a patchwork of various components and attempting to integrate them can result in complex, expensive, and time-consuming systems."
Where Do We Go From Here?
The journey into AI is not a distant future – it's a pressing present. In an era where AI's contribution to the global economy is measured in trillions, and its integration is a hallmark of industry leadership, the role of CTOs becomes ever more pivotal. Decisions will ripple across the economic landscape, influencing how humanity harnesses this transformative power.
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