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What Makes Enterprise AI Different From Any Other AI? – ReadWrite



What Makes Enterprise AI Different From Any Other AI? - ReadWrite

Has enterprise artificial intelligence (AI) lived up to the hype generated at a decade’s worth of industry conferences? Or is it coming up short? Maybe putting the word “enterprise” in front of AI just adds up to a marketing spin. It depends on how individual businesses deploy AI.

When companies adopt AI wisely, they do more than shift repeatable tasks and processes from humans to more efficient computers. They bring humans and machines together to build more intelligent workflow — transformational workflows.

What Makes Enterprise AI Different From Any Other AI?

The private equity firm Graham Allen has been leaning on AI to revitalize and grow midwestern industrial and mid-sized businesses with a pragmatic approach that’s gaining attention.

The enterprise AI-focused operating company SymphonyAI has been earning headlines for its strategy. Its portfolio companies have been making inroads in the industry verticals they each address, including Symphony IndustrialAI. With the recent acquisition of Savigent, Symphony AyasdiAI in banking, and Symphony MediaAI in the business of subscription and media distribution revenue, including gaming.

In data ops for private capital, Harmonate has been leading a quiet revolution in how private equity and funds-of-funds middle and back offices operate with machine learning.

Humans and machines together can achieve more, in a more repeatable and reliable fashion, and with better insight. But apart from some funds and companies, is that actually happening throughout the economy?

Where is the money going?

No, and yes. Money is being poured into AI, and it’s making a difference. It’s just that the difference being made is not necessarily visible. This lack of visibility fuels skeptics. And the progress is not fast, given that the availability of huge amounts of data is both a blessing and a curse. Copious data delivers the raw material AI needs. But AI is still learning how to cope with the complexity and needs help from human domain experts.

The smart companies are the ones that are not tinkering and failing to make big moves. And the smart companies also aren’t trying to leap too far ahead with moonshots that skip steps.

What the smart companies are doing is putting together point solutions into products that solve real business enterprise solutions. They are developing the right loop between domain experts and machines. The result is real AI product suites that capture the knowledge capital of enterprises and can transform industries.


We all know AI investments have been increasing in recent years. Skeptics would say the trend derives from big promises and false expectations. But I’m compelled to think many companies are deploying AI more wisely than we understand. They are discovering value and growing the potential of AI.

It’s just happening in quieter corners of business enterprises. It’s happening in places where domain experts and the right technologists are solving small problems, then connecting those breakthroughs to others, until there’s an inflection point. There’s a germination period underway right now.

We are moving from a diffuse cloud of point solutions to product suites in industry verticals powered by business leaders who’ve embraced the new reality of their markets.

When do I get my flying car?

AI skeptics, however, persist in believing that artificial intelligence advances are like flying cars – a sci-fi fantasy that has failed to materialize despite years of hopes and promises. It’s true that optimistic predictions have sometimes outstripped the reality of AI.

By one estimate, AI has been through seven false starts since the 1950s. Impressive multimillion-dollar AI efforts have faltered. Some ostensible “AI startups” aren’t even really using AI but rather are selling automation with elements of machine learning. This poor performance and confusion fuels skepticism, inhibits innovation, wastes money and reduces returns.

Most investor enthusiasm for AI is based on sound logic, however. AI tools have evolved from defeating humans at chess. Machines are good at recognizing patterns, a powerful and important cognitive function.

And, in fact, processing patterns are humanity’s intellectual edge over other species. It also accounts for many daily business tasks that AI-driven machines can now frequently do better than humans across a range of sectors. The results are driving enhanced AI chips that reduce costs and dramatically improve performance.

But those chips are also being driven by the fact that repeatable tasks can be deceiving. When multiple choices of what to do lead to many more multiples of options. Even AI can start to lose track of where it’s going. Experience with humans, and more chip power can bridge that gap.

More to work with

There is a lot more data to process today, too, which means more potential value. Thanks to the internet, social media, connected devices and the Internet of Things, total extant data exceeds 40 zetabytes, a ten-fold increase since 2013.

There are now “40 times more bytes than there are stars in the observable universe,” according to the World Economic Forum. Cloud computing has facilitated elastic consumption of storage and network demands to handle that data. Digital transformations have resulted.

A growing number of companies are recognizing the benefits. AI adoption tripled in the 12 months leading up to March 2019, perhaps “the fastest paradigm shift in technology history” according to a major study. PWC forecasts that AI could add $15.7 trillion to the global economy by 2030.

AI is not a fad. It is a key differentiator. Like the internet, it has the potential to completely transform the economy. Companies that deploy it effectively will make changes.

How to Transform a Business with Enterprise AI

Of course, companies can possess all the ingredients necessary to conduct top-performing AI analysis but still fail to achieve results, particularly if they lack a robust understanding of their industry’s business processes. Human perspective and insight are more art than science. Inspiring the former while developing the latter is the challenge we all face in the new AI age we’re now in the middle of.

Companies sometimes tinker, improving obsolete systems rather than rethinking and reinventing their operations to capitalize on enterprise AI.

Tinkering is good. But tinkering too long leads to a flawed approach that may help a company reduce its costs or streamline processes in the short run. But such gains are unlikely to justify the investment needed to gain significant market share.

Worse yet, the company will have missed an opportunity to achieve a transformational advantage, one that competitors may be exploiting.

Adding to the problems with tinkering are startups seeking to harness AI for individual point solutions. Their value proposition is harder to figure out. The potential for differentiation is typically diminished, and their survivability is less certain. A task and a point solution are not a business enterprise.

The middle way

Companies don’t face a choice of incremental change or narrow focus, however. Instead, established and new ventures need to harness enterprise AI’s capacity to capture and profit from the knowledge capital in their given sectors.

In 1998, Paul Strassmann argued that the proper function of the software is to serve as the business’s “prefrontal cortex,” storing and exploiting the working knowledge that has traditionally remained stuck in employees’ heads. When applied correctly, enterprise AI is the ideal technology for this work.

The goal of enterprise AI is not only to empower humans but also to program and institutionalize stronger, smarter, more efficient organizations.

Enterprise AI can expedite those changes because, unlike traditional software, which follows the static instructions of a programmer, AI can evolve to capture a wider variety of tasks and learns through practice.

Furthermore, enterprise AI is undaunted by the many terabytes of data that companies gather. It quickly observes complex and obscure patterns that humans miss.

That’s why forward-looking companies are using it to build next-generation platforms – systems of actionable intelligence that capture siloed data from existing systems of record. The enterprise AI solution makes this data available in a holistic way, through a set of AI models, applications and solutions.

These platforms also acquire and integrate data from external sources, providing intelligence for further revenue growth.


Businesses will need a vision for “AI-ification” if they want to rethink their operations, transform their technology stacks, overhaul existing solutions and win in the future. And we’re fast approaching the point where it’s not a question of wanting to rethink, but needing to rethink.

Image Credit:

Chris Gale


Chris is the founder of enterprise technology advisory and communications firm Gale Strategies. He’s an integrated communications marketer helping growing businesses and multinationals manage critical issues and tell their story to investors, customers, and consumers.


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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