The following is adapted from my new book, Real World AI.
You’ve found a way that AI can help your business. Maybe you want to automate the way help tickets are categorized, or improve search results, or increase efficiency in restocking.
You Want to Use AI: Do You Build, or Do You Buy?
Whatever you want to do, you face one big decision: Do you build the AI model yourself in-house, or do you purchase components from a third-party vendor and integrate them into your business?
It’s a critical choice to make, because it will have big impacts on the quality of the model, the cost, and the length of time to implementation.
It’s also a more complicated decision than you might think. Many people automatically assume that buying makes the most sense, but you can’t just buy a complete AI solution off the shelf. Many components go into a successful AI model, and there’s no one-stop-shop that can provide you with the entire system you need.
In order to implement AI successfully in your company, you must carefully strategize when (and what) to buy, and when to build yourself.
Is the AI Solution Related to Your Company’s Core Business Functions?
The first step in deciding whether to build or buy is to consider whether the AI solution is related to your company’s core business functions. If it is, then it often makes sense to build it in-house, as it would be a key competitive advantage. If not, it might make more sense to buy it, so that you’re not diverting valuable resources away from your core business functions.
For example, consider a fashion retailer that wants to use AI to create a better shopping experience. Specifically, they want to provide products relevant to a selected category—for example, when a consumer goes to the website and selects “blazer,” they want to show that consumer a bunch of blazers to choose from.
How do you determine the function?
The company’s core business function is fashion—that’s how they provide value to their customers. Based on that, it makes sense for them to create the training data for the AI model in-house, by taking lots of pictures of their inventory and annotating them by hand, deciding whether or not each picture was of something that could be considered a blazer.
A waste of time and money to always create technical infrastructure from scratch.
On the other hand, it would be a bad use of their time and money to create technical infrastructure from scratch. It has nothing to do with their core business functions, and it won’t provide them with an advantage over their competitors, nor enhance their brand. So they’re better off buying off-the-shelf solutions for most or all of the infrastructure.
How Quickly Do You Need a Solution?
Next, consider the role of time and urgency in your decision. There are many reasons that could drive a company to buy a component off the shelf, even if it does affect their core business, and time is one of the biggest.
The urgency options — building and buying, have timelines associated with them.
In general, it’s typically faster to buy and implement components than it is to build them from scratch. You might be able to buy and implement a component in half the time it would take you to build it yourself.
Shortening the time to market may be a priority. Often, companies will be tempted to take a shortcut to reduce their time to market if they see or anticipate competitors about to do the same. The opportunity costs of not solving the problem may be significant.
What Level of Quality Do You Need?
You’ll also have to examine the quality of buying versus building in-house.
Sometimes building in-house can result in a higher-quality model, because you can design everything for your very specific situation. However, this depends greatly on your company’s technical sophistication, resources, and expertise. Even if other considerations have you leaning toward building a component in-house, if you don’t have the capability to build it with sufficient quality, that option might be off the table.
Security is part of your company’s quality.
You should also consider security as you think about quality. You might think that buying a third-party product and integrating it deeply into your business has the potential to introduce security risks. But unless you have significant security expertise internally, you could just as easily introduce those risks by building insecure functionality.
How Much Do You Want to Spend?
Both building and buying require money and investment, so you’ll need to understand the budget you have in the context of the value your solution will provide to the company.
In some other cases, it may be prohibitively expensive to build a team to create infrastructure from scratch. When Yahoo! was making a similar decision, they were concerned that they wouldn’t be able to hire enough talent for a machine learning team to work on their core search functionality.
Costs must always be top of mind. What is your return on investment?
Facing pressure to stay competitive in the short term, they chose to stop investing in search as a core business. Of course, history has shown that Yahoo! lost that one to Google.
So while it’s often more costly to build something in-house, you have to think about the return on investment. If building in-house will drive greater strategic value down the line, it might be smarter to spend more now.
What Third-Party Components Are Available?
You also need to consider what third-party components are available — you can’t buy something if it doesn’t exist. There are many pieces of major infrastructure you’ll need to set up to enable your eventual success with AI, and you might be surprised by how many third-party solutions are available to help.
The first piece of infrastructure is your data.
You’ll need to have a lot of data to feed your model, as well as a way to clean it, move it, organize it, and store it. Unless you have extremely specific needs, there are many open source and commercial products that can handle the mechanics of moving data from here to there.
You’ll also need infrastructure that enables you to annotate all your data.
In some cases, your annotations will be the key differentiator that allows your model to provide business value (as in the fashion retailer example), which might convince you to build this infrastructure yourself to protect your IP. But there are also many commercial companies, such as Appen, who have security solutions in place to protect your data, as well as the processes and knowledge to help you annotate your data most effectively.
Next, you’ll need a platform to orchestrate training, testing, and hosting your models.
All of the major cloud platforms—Amazon, Google, Microsoft—provide machine learning platforms that can automatically train, test, tune, and deploy models. There are also full life cycle open source solutions, like Kubeflow, as well as point solutions that can be integrated together or with components you build yourself. And commercial vendors like Databricks can build more sophisticated custom solutions.
Mix and Match for the Perfect Solution
All of these considerations will play into the ultimate decision of whether you build or buy. Most projects end up being a combination of both, mixing and matching different components.
By considering your core business functions; the desired time frame, quality, and cost; and the available third-party components, you’ll be able to find the right solution for your company. Other things being equal, you should try to build components that are key to your company’s core business and buy the rest.
For more advice on building or buying an AI solution, you can find Real World AI on Amazon.
Wilson Pang joined Appen in November 2018 as CTO and is responsible for the company’s products and technology. Wilson has over nineteen years’ experience in software engineering and data science. Prior to joining Appen, Wilson was chief data officer of Ctrip in China, the second-largest online travel agency company in the world, where he led data engineers, analysts, data product managers, and scientists to improve user experience and increase operational efficiency that grew the business. Before that, he was senior director of engineering at eBay in California and provided leadership in various domains, including data service and solutions, search science, marketing technology, and billing systems. He worked as an architect at IBM prior to eBay, building technology solutions for various clients. Wilson obtained his master’s and bachelor’s degrees in electrical engineering from Zhejiang University in China.
Alyssa Rochwerger is a customer-driven product leader dedicated to building products that solve hard problems for real people. She delights in bringing products to market that make a positive impact for customers. Her experience in scaling products from concept to large-scale ROI has been proven at both startups and large enterprises alike. She has held numerous product leadership roles for machine learning organizations. She served as VP of product for Figure Eight (acquired by Appen), VP of AI and data at Appen, and director of product at IBM Watson. She recently left the space to pursue her dream of using technology to improve healthcare. Currently, she serves as director of product at Blue Shield of California, where she is happily surrounded by lots of data, many hard problems, and nothing but opportunities to make a positive impact. She is thrilled to pursue the mission of providing access to high-quality, affordable healthcare that is worthy of our families and friends. Alyssa was born and raised in San Francisco, California, and holds a BA in American studies from Trinity College. When she is not geeking out on data and technology, she can be found hiking, cooking, and dining at “off the beaten path” restaurants with her family.
Image Credit: markus spiske; pexels
Fintech Kennek raises $12.5M seed round to digitize lending
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!
Fortune 500’s race for generative AI breakthroughs
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!
UK seizes web3 opportunity simplifying crypto regulations
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!