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Investment in artificial intelligence (AI) has been booming for years now, and it’s not slowing down. Some researchers expect overall AI investment to push $500 billion by the end of the decade. That is reasonable when viewed from an investor perspective. Venture Capital firm Sequoia Capital, for example, has stated that generative AI alone has the potential to generate trillions of dollars of economic value.
Generative AI — which includes buzzy projects like OpenAI’s ChatGPT — is based on AI technology that recently matured and became available to the public. But we’re reaching an inflection point as its potential starts to blossom and money begins to pour in.
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In fact, while generative AI currently accounts for only about 1% of the AI-based data being produced, it’s expected to reach 10% by 2025, according to Gartner. This estimate could prove to be conservative. Nina Schick, an AI thought leader, recently shared her view with Yahoo Finance that 90% of online content could be generated by AI by 2025.
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This data can be used for countless business purposes, and it’s poised to entirely change the way that we think about work.
In other words, we are standing right at the edge of a revolution.
How AI is changing
So, what is different about today’s AI developments?
With tools like ChatGPT, AI is now generating a new type of conversation-like content that can entirely redefine the way we use and interact with data. This clearly has radical implications for creative professionals in fields like education, marketing and business analytics, and it could portend a monumental shift in how their work gets done.
However, what it means for those of us on the technology side of the house — and, more precisely, what it means for the optimization of business processes and operations — is not yet settled. Right now, there is no powerful enterprise use case in scale for generative AI that will directly impact the top and bottom lines of today’s leading businesses. But make no mistake, there will be, and it will likely appear within a year.
So enterprises must be studying this technology right now. Because what will separate the winners from the losers is knowing how to use it. And I believe the key to success at using generative AI lies in understanding the primal and foundational importance of data quality.
Why data is the skeleton key
Think about it like this: Generative AI is, quite literally, data-driven. To be able to output anything at all requires a wealth of data primed for analysis. That’s why investing in the building and maintenance of a clear data corpus will be the most important piece of a successful future in generative AI. It can massively accelerate the “learning” capabilities of Generative AI-based solutions.
When data is as valid, accurate, complete, consistent and uniform as possible across the entire enterprise, an intelligent generative AI tool can serve as the de facto digital assistant we always dreamt of, serving teams across all departments and functions. Any question may finally be answerable.
Three actionable insights
So, how can you prepare today for the yet-to-be-determined future? Here are three actionable insights.
1. Invest in high-quality, ‘machine-learning-ready’ data
With generative AI, you won’t need an abundance of data scientists on hand to build relevant intelligence and insights. Instead, you’ll need a few experts who understand the underlying technologies of generative AI, such as large language models, and a full team focused on making sure the data being input is the right data and in the right format. AI can do all the analysis, leaving leaders to focus on making the right decisions for the business.
In other words, it’s less about spending on AI and more about spending on stellar data quality and data management.
2. Prepare employees to embrace a new co-pilot
Generative AI also has the potential to shift the paradigm for employees. With it, a new reality emerges in which employees are working alongside a “co-pilot” that can answer any question and has a long-term memory of every topic ever discussed.
Encouraging employees to embrace AI as part of their day-to-day working lives will help workers optimize the technology to fit their specific roles.
3. Establish clear governance to limit risk
Technology is not always perfect, and new innovations require a full assessment of potential outcomes and ramifications. This isn’t just a matter of ethics; there can be real negative business consequences. What if your generative AI tool, for instance, starts spitting out offensive content during your shiny new marketing campaign? Are you prepared for that possibility?
That is why you must establish clear guardrails for supervising and governing your AI technology. This includes deeply evaluating what kind of data you would like to “expose” and give access to generative AI-based solutions. It’s not something that can run on autopilot, and we still don’t know how costly or challenging it will be to scale. So, we need to make sure we’re thinking through everything — and taking a measured, strategic approach to protecting your future.
Generative AI prime time is starting now, and it will dramatically change enterprise software. The specifics are still to be determined, but the change is coming soon. Enterprises should take this moment to prepare their data, policies and workforce for this emerging reality.
Yaad Oren is Managing Director of SAP Labs U.S. and Head of SAP Innovation Center Network.
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