Has enterprise artificial intelligence (AI) lived up to the hype generated during a decade of industry conferences? Where does it happen short? Maybe putting the word “business” in front of artificial intelligence just adds to a marketing rotation. It depends on how individual companies deploy AI.
When businesses embrace AI wisely, they do more than move repeatable tasks and processes from humans to more efficient computers. They bring humans and machines together to create a smarter workflow – transformational workflows.
What makes enterprise AI different from any other AI?
The enterprise AI-driven operating company SymphonyAI won securities for its strategy. The companies in its portfolio have made inroads in the verticals they each deal with, including Symphony IndustrialAI. With the recent acquisition of Savigent, Symphony AyasdiAI in banking and Symphony MediaAI in subscription and media distribution revenues, including games.
In private equity data operations, Harmonate has led a quiet revolution in the way private equity and fund of funds middle and back offices work with machine learning.
Humans and machines together can accomplish more, more reproducibly and reliably, and with better understanding. But other than some funds and companies, is this actually happening across the economy?
Where is the money going?
No and yes. Money is invested in AI and it makes a difference. It’s just that the difference is not necessarily visible. This lack of visibility stirs up skeptics. And progress is not fast, as the availability of huge amounts of data is both a blessing and a curse. Abundant data provides the raw materials AI needs. But AI is still learning to deal with complexity and needs the help of experts in the human realm.
Smart businesses are the ones that don’t tinker and fail to take big steps. And smart companies aren’t trying to jump too far with moon bursts skipping stages, either.
What smart businesses do is bundle point solutions into products that solve real business solutions. They develop the right loop between the experts in the field and the machines. The result is true suites of AI products that capture the knowledge capital of companies and can transform industries.
We all know that investments in AI have increased in recent years. Skeptics would say the trend stems from big promises and false expectations. But I have to think that many companies are deploying AI more wisely than we think. They discover value and develop the potential of AI.
It just happens in the quieter corners of businesses. It happens in places where the experts in the field and the right technologists solve small problems, then connect those breakthroughs to others, until there is an inflection point. There is a germination period going on right now.
We are moving from a diffuse cloud of point solutions to product suites in verticals, powered by business leaders who have embraced the new reality of their markets.
When do I receive my flying car?
AI skeptics, however, persist in believing that advances in artificial intelligence are like flying cars – a sci-fi fantasy that has failed to materialize despite years of hope and promise. . It is true that optimistic predictions have sometimes overtaken the reality of AI.
According to one estimate, AI has had seven false starts since the 1950s. Impressive multi-million dollar AI efforts have wavered. Some ostensible “AI startups” are not even really use AI, but instead sell automation with elements of machine learning. This poor performance and confusion fuels skepticism, inhibits innovation, wastes money and reduces returns.
Much of investor enthusiasm for AI, however, is based on solid logic. AI tools evolved after defeating humans at chess. Machines are good at recognizing patterns, a powerful and important cognitive function.
And, in fact, the treatment regimens are those of humanity. intellectual advantage compared to other species. It also represents many day-to-day business tasks that AI-driven machines can now often do better than humans in various industries. The results lead to improved AI chips that lower the costs and dramatically improve performance.
But these bullets are also motivated by the fact that repeatable tasks can be deceptive. When multiple choices of what to do lead to many more multiple options. Even AI can start to lose sight of its destination. Experience with humans and greater chip power can bridge this gap.
More to work with
There’s also a lot more data to process today, which means more potential value. Thanks to the Internet, social networks, connected devices and the Internet of Things, the total existing data exceeds 40 zetabytes, ten times more since 2013.
There are now “40 times more bytes than stars in the observable universe,” according to the World Economic Forum. Cloud computing has facilitated the elastic consumption of storage and the network demands to manage that data. Digital transformations have resulted.
A growing number of companies are recognizing the benefits. AI adoption tripled in the 12 months to March 2019, possibly “the fastest paradigm shift in technology history” according to one major study. PWC predicts 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 may have all the ingredients necessary to conduct the most successful artificial intelligence analyzes, but still fail to deliver results, especially if they do not have a solid understanding of the business processes in their industry. . Human perspective and insight is more of an art than a science. Inspiring the first while developing the second is the challenge we all face in the new age of AI we find ourselves in now.
Companies sometimes tinker around, improving outdated systems rather than rethinking and reinventing their operations to capitalize on enterprise AI.
DIY is good. But tinkering around for too long leads to the wrong approach that can help a business cut costs or streamline processes in the short term. 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 gain a transformational advantage, one that its competitors could exploit.
Startups looking to harness AI for individual point solutions add to the DIY problems. Their value proposition is harder to understand. The potential for differentiation is generally reduced and their ability to survive is less certain. A task and a point solution is not a business enterprise.
The middle lane
However, companies do not face a choice of incremental change or restricted concentration. Instead, established and new businesses must harness the ability of enterprise AI to capture and leverage knowledge capital in their given industries.
In 1998, Paul Strassmann argued that the proper function of software is to serve as the company’s “prefrontal cortex”, storing and exploiting the practical knowledge that has traditionally been stuck in the minds of employees. When applied correctly, enterprise AI is the ideal technology for this job.
The goal of enterprise AI is not only to empower humans, but also to program and institutionalize stronger, smarter, and more efficient organizations.
Enterprise AI can accelerate these changes because, unlike traditional software, which follows static instructions from a programmer, AI can evolve to capture a wider variety of tasks and learn by doing.
Additionally, enterprise artificial intelligence is not deterred by the many terabytes of data companies collect. He quickly observes complex and obscure patterns that humans miss.
That’s why forward-looking companies are using it to create next-generation platforms – actionable intelligence systems that capture siled data from existing recording systems. The enterprise AI solution makes this data available holistically, through a set of AI models, applications and solutions.
These platforms also acquire and integrate data from external sources, providing insights for future revenue growth.
Businesses will need an “AI-ification” vision if they are to rethink their operations, transform their technology stacks, revise existing solutions, and win in the future. And we’re quickly approaching the point where it’s not about wanting to rethink, but about needing to rethink.