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3 Steps to Overcome Common AI Application Development Obstacles



Vatsal Ghiya

From life-changing implementations like medical diagnostics imaging and self-driving vehicles to humble use cases such as virtual assistants or robot vacuums — artificial intelligence is being put to use to solve an incredible range of problems.

Despite widespread AI implementation efforts, however, the development of effective AI tools is still far from easy. Teams can expect to encounter quite a few obstacles along the way.

Data is one of the most important elements in developing an AI algorithm. Remember that just because data is being generated faster than ever before doesn’t mean the right data is easy to come by.

Low-quality, biased, or incorrectly annotated data can (at best) add another step. These extra steps will slow you down because the data science and development teams must work through these on the way to a functional application.

At worst, faulty data can sabotage a solution to the point where it’s no longer salvageable. Don’t believe it? That’s exactly how Amazon spent years building a sexist hiring tool that the company would eventually scrap.

Just Getting Started

Once you have high-quality data, your work is far from over. Instead, you’ll need to convert it into a machine-readable format — a process that comes with numerous challenges.

In highly regulated industries like finance and healthcare, for instance, data will need to be carefully de-identified to ensure it meets privacy standards.

If you’re sourcing international data, you’ll also need to adhere to data-sharing laws that govern the countries where the data originates. The process sounds like dotting the i’s and crossing the t’s — but adherence to data will require in-depth knowledge of a complex regulatory landscape.

Crunching the Numbers

Of course, data is nothing without a team to turn it into insights that can inform an AI model.

If your organization lacks a trained data science team in-house, you might have to hire or outsource these capabilities.

Even if you do have a team of experienced engineers on your roster, the sheer time required to annotate raw data can get in the way of actual algorithm development.

Employees aren’t likely to take a pay cut just because you have them performing lower-value work.

These obstacles certainly add complexity to the development process, but they shouldn’t be deal-breakers. Instead, a well-constructed plan can help you avoid some of these hurdles while you clear others one at a time as they appear.

3 Steps to Overcome Common AI Application Development Obstacles

REMEMBER: Maximize Efficiency and Outcomes

The AI development process is iterative, with each iteration is aimed at improving the accuracy and scope of the model. As you begin to plan how your own development journey will unfold, focus on the following three steps.

1. Find the right partner for primary tasks

Data sourcing, annotation, and de-identification can consume more than 80% of a data scientist’s time.

Leveraging the expertise of the right partner can save a huge amount of your AI team’s time and energy. You want to allow your team to utilize the skills you pay them for instead of performing mundane data-cleaning functions.

Besides ensuring your team is free to put their best skills to good use, an experienced partner can help you track down the highest-quality content for training your AI data model.

Gartner Research predicts that 85% of AI implementations through 2022 will produce errors in output due to bias in input. With the right partner helping you source and annotate data, you can avoid a costly scenario where “garbage in yields garbage out.”

2. Align stakeholders with clear use cases and customer needs

Building an AI solution is a considerable investment that will require lots of participants with varying roles.

Having a diverse range of experiences and perspectives is critical to a successful AI implementation, but only if these stakeholders are aligned on the project’s goal.

Existing gaps between different perceptions of the ideal outcome only widen as the development process progresses, so it’s important to take the time to nip these misunderstandings in the bud early.

Spend time with all stakeholders and teams to establish clearly defined goals and criteria for success. This small upfront investment will cost you time and money, but it will save you both in the long run by keeping participants aligned for the project’s duration.

3. Get it right, one implementation at a time

AI is extremely powerful, but it’s not a silver bullet; there are still many business problems for which AI isn’t a suitable solution. Instead of throwing artificial intelligence at the wall and seeing what sticks, organizations should start by prioritizing the use cases that make the most sense.

Are you looking to filter through a vast amount of data? AI is an excellent option. If you’re trying to spot patterns, it’s equally capable, and software can scale to outperform millions of human analysts with ease.

Start with simple or proven AI implementations that offer the easiest and quickest path to a payoff, and take the experience gained through these ventures to more complicated future projects.


Creating an AI application isn’t easy, but the potential rewards are massive. Keep a clear understanding of the potential pitfalls your team could encounter throughout the process.

Your potential pitfalls include data sourcing and annotation issues, personnel shortages, skills gaps, and a lack of alignment toward a common goal.

Construct a plan that takes these obstacles into account. Start with the above three steps, and you’ll be well on your way to an effective AI implementation.

Image credit: scott graham; unsplash, thank you!

Vatsal Ghiya

CEO and co-founder of Shaip

Vatsal Ghiya is CEO and co-founder of Shaip, which enables the on-demand scaling of platforms, processes, and people for companies with demanding ML and AI initiatives.


Fintech Kennek raises $12.5M seed round to digitize lending



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 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; 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



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



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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