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3 Tips for Getting Returns from AI Investments – ReadWrite

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Andrey Koptelov


As we are long past the stage of AI hype, it’s becoming apparent that the technology’s biggest issues revolve around gaining profits rather than figuring out how to make it useful. With the growing number of AI experts and machine learning services, AI is capable of providing immense value for many organizations. However, when it comes to deploying AI, companies often fail to even cover their initial investments. This seems a bit contradictory, isn’t it? 

A recent IBM research reveals that only 21% of companies are able to integrate AI into their operations. This is where the root cause of the problem lies: it’s impossible to achieve economic returns on the technology that hasn’t been put into production. Moreover, even those AI projects that get deployed often don’t bring the expected value.

Let’s discuss the hurdles companies face on the way to AI profit-making and how they can be overcome. 

Prepare the workforce

Given that AI is always data-heavy, it’s paramount that the adopting organization’s culture is data-driven. Unsurprisingly, a lack of data culture is one of the most recurring problems that companies have to face on the way to realizing the full potential of AI.

If the company’s leaders and key employees have poor data expertise, AI initiatives will most likely fail. Even expertly built AI systems won’t realize their full potential if the staff doesn’t apply data-driven approaches to decision-making. A lack of change management is another widespread mistake in AI implementation.

More often than not, AI calls for significant changes in organizational structure and strategy as well as employees’ mindsets and skills. Therefore, consider change management as a core part of the AI implementation roadmap and ensure that your company’s leaders have the necessary knowledge and drive to foster the AI-centric culture.

Set tangible goals

While goals are basic success prerequisites for any project, when it comes to AI implementation, many companies still fail to clearly determine them. It’s essential to have clear expectations about the outcomes of an AI initiative. More often than not, end users don’t participate actively in AI projects, so when the technical team builds flawless AI systems, they provide little business value. This is why it’s critical to involve all the stakeholders from the beginning of the project.

Also, AI projects often bring value that cannot be measured. For example, enhanced employee satisfaction or better customer experience is much harder to keep track of than cost or time savings. Or, let’s say you build an AI system to decrease the time it takes for the IT department to categorize tickets. First, given that the system will have to make sense of free-form text using NLP, it won’t be 100% accurate, especially in the beginning. So your team will need to determine the permissible error rate and account for that in the ROI calculation.

Here is another example — let’s say there is a critical issue which needs immediate attention of IT staff and an AI system mistakenly identifies this ticket as low-priority. This significantly complicates ROI calculation as it’s hard to measure the negative outcomes of such a case.

This is why it’s critical to start with projects where ROI expectations can be properly calculated. For example, many manufacturing companies succeed in achieving economic returns on AI initiatives applied for quality control, as their ROI is comparatively easy to measure. 

Start small

While it’s tempting to build large-scale AI systems, aiming for low-hanging fruit is often a much more effective strategy, especially in the beginning. It might be a good idea to start with robotic process automation (RPA), which tends to be more affordable than AI and provides relatively fast ROI. RPA implementation is non-invasive, meaning that it doesn’t disrupt the flow of legacy systems like many AI solutions would do.

AI projects that turn out to be quick wins can also help to justify more ambitious AI investments and ensure stakeholder buy-in in the future.

AI calls for maturity

While it may sound trivial, companies that are more mature and experienced have a better shot at reaping the benefits from AI. Such companies tend to have established data governance practices, elaborate training programs, performance tracking systems, and clear project goals. These are critical differences between companies that succeed in AI implementation and those that don’t.

Given the volatility of project success rates, AI calls for a solid foundation in key management areas more than any other technology. The degree to which companies can track, measure, and organize processes often correlates to their probability of profiting from AI. 

Andrey Koptelov

Andrey Koptelov is an Innovation Analyst at Itransition, a custom software development company headquartered in Denver. With a profound experience in IT, he writes about new disruptive technologies and innovations.

Politics

Fintech Kennek raises $12.5M seed round to digitize lending

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Google eyed for $2 billion Anthropic deal after major Amazon play


London-based fintech startup Kennek has raised $12.5 million in seed funding to expand its lending operating system.

According to an Oct. 10 tech.eu report, the round was led by HV Capital and included participation from Dutch Founders Fund, AlbionVC, FFVC, Plug & Play Ventures, and Syndicate One. Kennek offers software-as-a-service tools to help non-bank lenders streamline their operations using open banking, open finance, and payments.

The platform aims to automate time-consuming manual tasks and consolidate fragmented data to simplify lending. Xavier De Pauw, founder of Kennek said:

“Until kennek, lenders had to devote countless hours to menial operational tasks and deal with jumbled and hard-coded data – which makes every other part of lending a headache. As former lenders ourselves, we lived and breathed these frustrations, and built kennek to make them a thing of the past.”

The company said the latest funding round was oversubscribed and closed quickly despite the challenging fundraising environment. The new capital will be used to expand Kennek’s engineering team and strengthen its market position in the UK while exploring expansion into other European markets. Barbod Namini, Partner at lead investor HV Capital, commented on the investment:

“Kennek has developed an ambitious and genuinely unique proposition which we think can be the foundation of the entire alternative lending space. […] It is a complicated market and a solution that brings together all information and stakeholders onto a single platform is highly compelling for both lenders & the ecosystem as a whole.”

The fintech lending space has grown rapidly in recent years, but many lenders still rely on legacy systems and manual processes that limit efficiency and scalability. Kennek aims to leverage open banking and data integration to provide lenders with a more streamlined, automated lending experience.

The seed funding will allow the London-based startup to continue developing its platform and expanding its team to meet demand from non-bank lenders looking to digitize operations. Kennek’s focus on the UK and Europe also comes amid rising adoption of open banking and open finance in the regions.

Featured Image Credit: Photo from Kennek.io; Thank you!

Radek Zielinski

Radek Zielinski is an experienced technology and financial journalist with a passion for cybersecurity and futurology.

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Fortune 500’s race for generative AI breakthroughs

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Deanna Ritchie


As excitement around generative AI grows, Fortune 500 companies, including Goldman Sachs, are carefully examining the possible applications of this technology. A recent survey of U.S. executives indicated that 60% believe generative AI will substantially impact their businesses in the long term. However, they anticipate a one to two-year timeframe before implementing their initial solutions. This optimism stems from the potential of generative AI to revolutionize various aspects of businesses, from enhancing customer experiences to optimizing internal processes. In the short term, companies will likely focus on pilot projects and experimentation, gradually integrating generative AI into their operations as they witness its positive influence on efficiency and profitability.

Goldman Sachs’ Cautious Approach to Implementing Generative AI

In a recent interview, Goldman Sachs CIO Marco Argenti revealed that the firm has not yet implemented any generative AI use cases. Instead, the company focuses on experimentation and setting high standards before adopting the technology. Argenti recognized the desire for outcomes in areas like developer and operational efficiency but emphasized ensuring precision before putting experimental AI use cases into production.

According to Argenti, striking the right balance between driving innovation and maintaining accuracy is crucial for successfully integrating generative AI within the firm. Goldman Sachs intends to continue exploring this emerging technology’s potential benefits and applications while diligently assessing risks to ensure it meets the company’s stringent quality standards.

One possible application for Goldman Sachs is in software development, where the company has observed a 20-40% productivity increase during its trials. The goal is for 1,000 developers to utilize generative AI tools by year’s end. However, Argenti emphasized that a well-defined expectation of return on investment is necessary before fully integrating generative AI into production.

To achieve this, the company plans to implement a systematic and strategic approach to adopting generative AI, ensuring that it complements and enhances the skills of its developers. Additionally, Goldman Sachs intends to evaluate the long-term impact of generative AI on their software development processes and the overall quality of the applications being developed.

Goldman Sachs’ approach to AI implementation goes beyond merely executing models. The firm has created a platform encompassing technical, legal, and compliance assessments to filter out improper content and keep track of all interactions. This comprehensive system ensures seamless integration of artificial intelligence in operations while adhering to regulatory standards and maintaining client confidentiality. Moreover, the platform continuously improves and adapts its algorithms, allowing Goldman Sachs to stay at the forefront of technology and offer its clients the most efficient and secure services.

Featured Image Credit: Photo by Google DeepMind; Pexels; Thank you!

Deanna Ritchie

Managing Editor at ReadWrite

Deanna is the Managing Editor at ReadWrite. Previously she worked as the Editor in Chief for Startup Grind and has over 20+ years of experience in content management and content development.

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UK seizes web3 opportunity simplifying crypto regulations

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Deanna Ritchie


As Web3 companies increasingly consider leaving the United States due to regulatory ambiguity, the United Kingdom must simplify its cryptocurrency regulations to attract these businesses. The conservative think tank Policy Exchange recently released a report detailing ten suggestions for improving Web3 regulation in the country. Among the recommendations are reducing liability for token holders in decentralized autonomous organizations (DAOs) and encouraging the Financial Conduct Authority (FCA) to adopt alternative Know Your Customer (KYC) methodologies, such as digital identities and blockchain analytics tools. These suggestions aim to position the UK as a hub for Web3 innovation and attract blockchain-based businesses looking for a more conducive regulatory environment.

Streamlining Cryptocurrency Regulations for Innovation

To make it easier for emerging Web3 companies to navigate existing legal frameworks and contribute to the UK’s digital economy growth, the government must streamline cryptocurrency regulations and adopt forward-looking approaches. By making the regulatory landscape clear and straightforward, the UK can create an environment that fosters innovation, growth, and competitiveness in the global fintech industry.

The Policy Exchange report also recommends not weakening self-hosted wallets or treating proof-of-stake (PoS) services as financial services. This approach aims to protect the fundamental principles of decentralization and user autonomy while strongly emphasizing security and regulatory compliance. By doing so, the UK can nurture an environment that encourages innovation and the continued growth of blockchain technology.

Despite recent strict measures by UK authorities, such as His Majesty’s Treasury and the FCA, toward the digital assets sector, the proposed changes in the Policy Exchange report strive to make the UK a more attractive location for Web3 enterprises. By adopting these suggestions, the UK can demonstrate its commitment to fostering innovation in the rapidly evolving blockchain and cryptocurrency industries while ensuring a robust and transparent regulatory environment.

The ongoing uncertainty surrounding cryptocurrency regulations in various countries has prompted Web3 companies to explore alternative jurisdictions with more precise legal frameworks. As the United States grapples with regulatory ambiguity, the United Kingdom can position itself as a hub for Web3 innovation by simplifying and streamlining its cryptocurrency regulations.

Featured Image Credit: Photo by Jonathan Borba; Pexels; Thank you!

Deanna Ritchie

Managing Editor at ReadWrite

Deanna is the Managing Editor at ReadWrite. Previously she worked as the Editor in Chief for Startup Grind and has over 20+ years of experience in content management and content development.

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