smartCIO Vol. 1 - Less Artificial, More Intelligence
SmartCIO magazine delivers valuable industry insights, strategies, and best practices to IT leaders.
A message from
Rani Johnson.
Welcome tosmartCIO我们今天的全球季刊杂志,“s modern IT leaders. Our mission for this magazine is to share the latest technology and leadership insights and build a trusted community of CIOs and leading IT experts. Each quarter, you’ll hear from a variety of thought leaders about the latest technology trends, business challenges, and strategic approaches for maximizing value.
This edition dives into a topic that’s top-of-mind for organizations everywhere: AI and machine learning (ML). CIOs are being tasked with adopting AI and ML technologies to maximize organizational efficiency and productivity; however, many are still trying to determine just how to do that.
This issue provides insight into what AI and ML mean for organizations and the future of work, as well as best practices for successful AI and ML adoption. As these technologies continue to evolve, it is crucial for CIOs to understand their potential and stay ahead of the curve by incorporating AI and ML into their digital transformation strategy. We promise to help guide you along the way.
A message from Rani Johnson.
Welcome tosmartCIO我们今天的全球季刊杂志,“s modern IT leaders.
Our mission for this magazine is to share the latest technology and leadership insights and build a trusted community
of CIOs and leading IT experts. Each quarter, you’ll hear from a variety of thought leaders about the latest technology
trends, business challenges, and strategic approaches for maximizing value.
This edition dives into a topic that’s top-of-mind for organizations everywhere: AI and machine learning (ML).
CIOs are being tasked with adopting AI and ML technologies to maximize organizational efficiency and productivity;
however, many are still trying to determine just how to do that.
This issue provides insight into what AI and ML mean for organizations and the future of work,
as well as best practices for successful AI and ML adoption. As these technologies continue
to evolve, it is crucial for CIOs to understand their potential and stay ahead of the curve by
incorporating AI and ML into their digital transformation strategy. We promise to help
guide you along the way.
Rani Johnson
Chief Information Officer, Workday
Global Study Reveals Barriers to AI Adoption for Business Leaders
Read Now
How AI and ML Are Powering the Future
of Work
Read Now
如何突出技能的组织可以使用人工智能创造e the Jobs of Tomorrow
Read Now
Why Do AI and ML
Matter to Your
Business?
Read Now
How CIOs Can Use AI and ML to Cultivate a Future-Ready Organization
Read Now
Workday’s Continued Diligence to Ethical
AI and ML Trust
Read Now
Accenture Report: Generative AI Is Powering a New Reality for Business
Read Now
ByJosh Krist,
Staff Writer, Workday
AI is having its moment.
Something that’s always been in the medium- to long‑term future has suddenly become very real, and very now. Our “AI IQ: Insights on Artificial Intelligence in the Enterprise” report on a survey of 1,000 business decision-makers from around the globe found that many business leaders, to varying degrees ranging from small pilots to full-blown functionality, are currently utilizing some form of AI and machine learning (ML) to further their business.
But, they need to ask themselves: Are they deploying AI and ML right? Or just right now?
With “AI IQ,” we wanted to cut through the hype and speculation and find out what people are really doing, thinking, and feeling when it comes to what might be the most transformative technology of our lifetime. If you believe the experts, the changes AI will bring are so profound that we can barely imagine them given our current frame of reference.
Leaders want less artificial, more intelligence.
Overall, we found that business leaders are fairly certain that their AI investments will increase over time, and that AI will definitely bring tangible business benefits. But, they’re not sure they can trust the data they use to power AI and ML, or that they are deploying AI and ML in the right places, in the right way, and at speed. Lastly, they are unsure the people within their organizations have the necessary skills to get the most value out of these technologies.
We also found that, unlike previous transformative technologies, most people are in agreement that it will indeed be a game changer, or at least a game enhancer. A whopping 80% of respondents agree that AI and ML are required to keep their business competitive, and two-thirds say that AI and ML have already increased productivity and operational efficiencies.
Another thing we found is that 73% of the decision‑makers surveyed are under pressure to increase the adoption of investments in AI and ML. Although almost everyone feels pressure to move quickly with AI, and that pressure mostly comes from the top, the motivation for the pressure varies. Leaders in IT feel the pressure to be more competitive. In HR, the pressure is about improving the employee experience; finance decision-makers say they’re being asked to address a skills gap.
The bottom line is that leaders know they need to execute on AI and ML now, but quickly learn how to do so in a lasting way built on a solid foundation that will deliver value quickly in a fast-changing space. “Hopeful but a little fearful too,” might be a good summation of how business leaders are feeling about AI.
Other top findings from the report that reflect business decision-makers’ sentiments include:
Under Pressure
73% feel pressure to increase the adoption of investments in AI and ML
83% intend on either keeping investments in AI and ML the same or increasing it next year
88% say AI and ML are influencing technology purchasing decisions
Risks and Barriers
75% say many hindrances are preventing their organization from fully implementing AI and ML
Top risks to implementing include data and security (48%), concerns about accountability (47%), and decision-making errors (46%)
77% say uptake would increase within their organization if there were fewer risks involved
Data Difficulties
77% are concerned about the timeliness or reliability of data their organization will use for AI and ML
29% currently using AI believe insufficient data volume or quality is to blame for AI falling short of expectations
Rare Consensus
99% agree there are business benefits from investing in AI
Reasons range from improving decision-making (41%), automating business processes (35%), and improving employee retention/experience (32%)
80% agree that AI and ML are required to keep their business competitive
Looking ahead.
We conducted this survey because we know that the future of many companies and industries, and business itself, after all, is being made right now. As you read these lines, an even larger and more ambitious follow-up survey on AI and ML is being conducted in collaboration with Longitude Research, aFinancial Timescompany. Although we look forward to sharing this roadmap to the future, we know that with such transformative and fast-moving technology, fresh insights at this very moment are extremely valuable.
Our Chief Technology Officer Jim Stratton, who along with other Workday leaders has been vocal and clear about the importance of AI within the enterprise, lent his executive perspective to this research, and his words are useful here.
“In my conversations with customer and partner CIOs and business and technology leaders,” Stratton writes in the foreword, “the biggest challenge I’m hearing with AI and ML is that people aren’t sure where to start, and they wish they had a better idea of where others are finding success, and encountering roadblocks, on their journey. That’s exactly what this report is for, and I hope that after reading it and absorbing the insights it offers, leaders will be able to tell a better story about AI and their own organization’s goals.”
Find out more about the“AI IQ: Insights on Artificial Intelligence in the Enterprise”report, talk with our AI chatbot about it, ordownload the report.
BySayan Chakraborty,
Co-President, Workday
Workday has long believed that artificial intelligence (AI) and machine learning (ML) will power the future of work.
While recent advancements in AI and ML—mainly with generative AI, and specifically OpenAI and ChatGPT—are causing everyone to jump on the AI and ML bandwagon, Workday has been building and delivering AI and ML capabilities to our customers for nearly a decade.
Workday’s unique approach to AI and ML.
Workday thinks about and implements AI and ML differently than any other enterprise software company in the world. From a capabilities perspective, Workday takes a platform-first approach that embeds AI and ML into the very core of our technology platform. Why does this matter? It matters because it allows us to rapidly deliver and sustain new ML-infused capabilities into our applications. ML gets better the more you use it, and by having millions of users constantly using dozens of applications on the same platform, it improves at a faster rate.
我们工作的另一个区别是数据and the special care we take of it. The sheer quantity of customer data we have access to is enormous—over 60 million users representing about 442 billion transactions a year. But quantity means nothing without quality, which we enforce with our comprehensive single data model. This data model allows us to maintain clean and coherent data in a way our competitors—who rely on multiple integrations of different data repositories—cannot. We also use a tenanted model to structure our data, which uniquely allows us to build tailored models for customers in a specific region or industry through federated learning, all while maintaining the necessary privacy and regulatory rules. And lastly, we can bring in third-party data withWorkday Prism Analyticsand merge it with Workday’s unparalleled data set to create unique models no one else can.
The sheer quantity of customer data we have access to is enormous—
over 60 million users representing about 442 billion transactions a year.
在ML,从业者讨论数据的“三对”needed to drive positive outcomes: sufficient volume, velocity, and variety. Workday has all three. The combination of Workday’s unique data and technology capabilities allows us to deploy AI and ML solutions with high performance and better tailored use cases, quickly delivering rapid and differentiated outcomes for our customers.
Enabling the future of work with
AI and ML.
A great example of how our unique approach comes to life isWorkday Skills Cloud,our ML capability for enabling the future of work. As we reach the limits of traditional career trajectories, credentials, degrees, and formal resumes, the future economy must be much more dynamic, flexible, and capable of allowing people with nontraditional backgrounds to participate effectively. Workday Skills Cloud uses AI and ML to analyze the way skills are used in human language, understanding their relationship to each other, and mapping that to a skills-centric workforce at scale.
Workday Skills Cloud, and the ML engines that power it, are essential to enabling our customers to live in this new world. So much so that over half of our coreWorkday Human Capital Management (HCM)customers are using it. Workday Skills Cloud has processed over 5 billion uses of skills since its launch five years ago. There’s simply no way for companies to adopt skills at scale without ML.
Applying AI and ML is equally essential to the future of finance. With AI and ML, finance teams can get help managing risk and eliminating inefficiencies by reducing what used to take months or weeks down to just hours or minutes.
For example, finance teams spend an inordinate amount of time gathering information and reconciling transactions throughout the month and at quarter close. Workday AI and ML help teams quickly identify financial patterns, trends, and anomalies—enabling them to complete the financial close process more quickly and efficiently.
By embedding AI and ML natively into our platform,Workday Financial Managementenables intelligent automation to process high-volume transactions faster—further improving accuracy while delivering measurable business impact.
Unlimited possibilities for Generative AI.
有无限的可能性如何AI和毫升will impact the future of work, especially now with Generative AI. Workday was an early adopter of large language models (LLMs)—the technology that has enabled Generative AI—and we use them in production today. We have started adopting Generative AI at Workday to solve a host of additional customer challenges. A canonical case for LLMs is content creation, and we can see how drafting performance reviews, job descriptions, and a host of other documents will be transformed by this approach. We’re going to continue to identify key use cases where Generative AI can add value to our customers and develop unique models that leverage both Workday data and external data sets.
Delivering confident decisions with trustworthy AI.
We believe that for AI and ML to really deliver on the possibilities it offers, it must be trustworthy and it must augment humans, not displace them. In order for AI and ML to be trustworthy, trust must be designed into the very foundation. As one of theworld’s most ethical companies,we’re committed to responsible AI. We provide our customers with a clear understanding of how our ML products are developed and assessed in order to help mitigate any risks associated with their use. Ourkey ethical AI and ML principlesserve as the cornerstone of our work in this space, and guide us in the development of AI and ML technologies that drive positive societal outcomes and expand growth opportunities for our customers and their employees.
With a guiding principle to keep humans at the center, no decision is fully automated by Workday’s AI and ML technology, and our practices ensure that people are the final decision-makers. We commit to maintaining our human-in-the-center approach, using AI and ML to make people more productive, better informed, and enabling them to solve problems they didn't think they could solve before. This is the promise of AI and ML, and we’re just getting started with imagining how it will shape the future of how we work.
In order for AI and ML to be trustworthy, trust must be designed into the very foundation.
ByAneel Bhusri,
Co-Founder, Co-CEO, and Chair, Workday
The world of work as we once knew it no longer exists.
For years, work was a static concept and narrowly defined. That notion has given way to a far more dynamic and rapidly evolving model fueled by the rise in hybrid and remote work, emerging technologies, and increasing economical and societal factors.
At the center of this shift is today’s workforce. Companies used to manage their talent with a focus on traditional degrees and linear career progression. But that approach no longer works, given the heightened focus on the employee experience combined with how fast the nature of jobs is evolving. In fact, Dell Technologies predicts that 85% of the jobs in 2030 haven’t been invented yet.
To navigate the speed and scale of this new reality and help prepare for the jobs of tomorrow, it’s imperative for organizations to adopt a skills-based mindset driven by the power of AI and machine learning (ML).
Here’s what today’s leaders need to focus on, as the changing world of work won’t wait.
Shift the way you think about talent.
Skills-based organizations will lead the future of work. Shifting to a skills-based approach helps organizations hire and retain the right talent and enables them to upskill their existing talent to meet the needs of today’s digital world. According to a recent study by Amazon and Workplace Intelligence, almost 80% of employees in the U.S. today are concerned that they lack the skills needed for the jobs of tomorrow, while 58% of employees believe their skills have gone stale since the pandemic.
Organizations must evolve how they think about the concept of work, moving away from the rigid idea that work is done through structured job roles and responsibilities, instead viewing work as a more fluid compilation of skills to be leveraged as the world around us changes. The resulting impact, as noted by Deloitte, is that skills-based organizations are more agile and more competitive, vital to succeeding in this new landscape.
Forward-looking companies recognize the benefits of these technologies in driving a skills-based workforce.
Lean in to innovation.
Fundamental to delivering on this shift to a skills-based approach are technologies such as AI and ML that can understand key attributes to help drive automation and provide insights and predictions that help to identify and align skills with jobs, quickly turning employee data into a strategic advantage, while helping businesses adapt to change.
Forward-looking companies recognize the benefits of these technologies in driving a skills-based workforce. Consulting firm Booz Allen Hamilton, for example, shared its take on the power of AI to support this shift and its capacity to unlock more access to opportunities for internal candidates, uncover previously unseen skills matches, and tap into a more diverse talent pool.
At Workday, we developed Career Hub. This helps our employees share skills and interests and receive relevant connections, curated learning content, and recommended jobs to help them on their career journeys. Using ML, Career Hub provides workers with suggestions to grow their skills and capabilities and encourages them to build a plan as they explore opportunities for continued career development. At a time when the employee experience is becoming a business imperative, offering employees an opportunity for internal mobility and advancement while also listening to what employees have to say is mission-critical to retaining talent and driving overall business success.
Shifting to a skills-based approach helps organizations hire and retain the right talent and enables them to upskill their existing talent to meet the needs of today’s digital world.
Support smart policy.
In a fragmented world, technology can be the great equalizer. But progress toward a skills-based workforce cannot be sustained without meaningful policies that embrace responsible approaches to AI and ML. Those policy discussions must include all stakeholders, including the companies developing these technologies, to ensure businesses are prepared for the road ahead.
AI and ML technologies can fundamentally improve the way we work and foster greater equality in accessing opportunity, but in the face of such a profound technological and societal change, it’s vital that we commit to an ethical compass. At Workday, we follow four key principles that guide how we develop and utilize AI and ML technologies responsibly and work to address their broader societal impact. One of those principles is putting people first.
Together, the public and private sectors can work to establish standards and policies that ensure new technologies, such as AI and ML, drive human progress, create job opportunities for our future workforce, and grow our economies. The time to go all in is now.
BySpiros Margaris,
Top Global Influencer in AI and Fintech
There’s no question that AI and machine learning (ML) are starting to impact nearly every corner of the business world.
However, many business leaders are still unclear on what they should be considering now as they prepare to best capitalize on AI and ML technologies.
Over a series of podcast episodes, I’ve had discussions about the opportunities and challenges of AI and ML with Workday leaders. Check out the first duo of episodes to learn more about AI and ML policies, global regulations, intelligent automation, and more.
In order to get organizations to lean in to using AI, the key is to establish trust, recognize that the stakes are higher for financial users, and focus on the transition from tactical to strategic finance.
In this episode, Jenn Dearth, senior manager of Product Management for machine learning for financials at Workday, joins Spiros Margaris to discuss how Workday currently uses AI in managing risk, generating rules, and providing preemptive insights for finance functions.
Listen Now
In the AI and ML landscape, does legislation impede innovation, or is there a balance to be found between the two?
In this episode, Spiros Margaris is joined by Jens-Henrik Jeppesen, senior director of Public Policy at Workday; and Chandler C. Morse, vice president of Corporate Affairs at Workday, to talk through AI policies, GDPR, and the EU’s proposedAI Act,as well as how Workday is embracing the balancing act of innovation and legislation when it comes to employee development and skills-based talent acquisition.
Listen Now
ByJim Stratton,
Chief Technology Officer, Workday
CIOs have an opportunity to guide their organizations into building a skills-based workforce with the help of AI and machine learning (ML) technologies, which are revolutionizing the way work
gets done.
CIOs play a crucial role in this transformation by aligning technology with the future needs of the business and the capabilities of its employees.
In this video, I talk with Aashna Kircher, Workday general manager of Talent, about how IT leaders can leverage AI and ML advancements to support HR in enhancing employee engagement and retention, while also cultivating a workforce that prioritizes skills.
ByKelly Trindel,
Head of Machine Learning Trust, Workday
Over the past few years,
we’ve seen a major shift in
the evolution of organizations’ understanding of AI and the benefits it can provide.
With that more mature understanding, organizations have also evolved their thinking around balancing these benefits with the potential risks associated with AI.
Harnessing the power of AI can lead to significant improvements in the applications many of us use every day. Just think of your own experiences with your favorite navigation service, streaming service, or online shopping site. AI greatly improves results and provides you with a more meaningful, personalized experience.
更广泛地说,组织选择leverage AI have the potential to experience exponential benefits to their business. However, in doing so, it’s of paramount importance that these businesses develop rigorous policies and safeguards to manage the associated risks of unintended consequences. That’s why Workday has put in place a machine learning (ML) trust program that specifically addresses how Workday delivers ML and AI technologies with a laser focus on ethics and customer trust, and an eye toward global emerging AI regulation.
In 2019, we committed to an ethical AI approach. As the market’s and our customers’ understanding of AI has matured, so too have the details of our approach. Our commitment to ethical AI is a reflection of our core values, including a focus on our employees, customer service, innovation, and integrity. In our effort to develop responsible and trustworthy AI, we aspire to achieve the following goals.
Organizations that choose
to leverage AI have the potential to experience exponential benefits
to their business.
Amplify human potential.
At Workday, we put people at the center of everything we do and always respect fundamental human rights. We design AI to help our customers and their employees unlock opportunities and focus on meaningful work. Our solutions support human decision-making, improve experiences, and put users in control to decide whether to accept the recommendations provided by our AI-based solutions.
Our technologies are designed to help reduce error and tedious work while improving user productivity and decision-making. We focus on improving and developing people’s capabilities and experiences, and leverage a “human-in-the-loop” approach to enable end-user control over ultimate decisions.
Positively impact society.
AI has the potential to empower people to work smarter and more efficiently, and even change the nature of work itself. We care about the impact of our AI solutions and work to maximize their potential to do good. We focus on delivering AI solutions that tackle real-world challenges and drive positive outcomes for our customers, their employees, and our shared communities.
We’re thoughtful and deliberate in our approach at Workday, and we only develop AI solutions that align with our values. In the human capital management space, for example, we avoid the development of technologies that enable intrusive productivity monitoring. We choose instead to focus on job-relevant skills solutions that enable more personalized experiences, nurture career development and growth, and promote internal mobility. We apply a comprehensive governance program to facilitate the delivery of innovative and trustworthy AI solutions that are secure, robust, and reliable.
Champion transparency and fairness.
Workday values inclusion, belonging, and equity, as well as open communication and honesty. We act responsibly and ethically in our design and delivery of AI solutions to support equitable recommendations. Because we understand the multitude of ways that bias can be introduced into the AI lifecycle, we leverage a risk-based review process to evaluate whether our AI solutions are susceptible to unintended consequences and how any such risks can be managed.
Workday values inclusion, belonging, and equity, as well as open communication and honesty.
We provide transparency into our AI models through clear documentation to customers that describes how our AI solutions are built, how they work, how they are trained and tested, and how they are monitored through our ongoing testing and evaluation practices. We continue to learn and improve by exploring innovative research in algorithmic fairness and explainability.
Deliver on our commitment to data privacy and protection.
Data privacy and protection is foundational to trustworthy AI. Workday Privacy Principles apply to all of our products and services, including our AI technologies. We embrace good data stewardship, governance processes, and privacy-by-design principles. We respect user agency and give our customers control over whether their data is used in our AI solutions. Data access to build our AI solutions is controlled and audited. We remain focused on our customers and the evolving privacy needs of their employees as we work to determine how we can best serve them.
We continue to work with diverse and cross-functional experts to fully operationalize these principles through clear policies and documented practices. In alignment with our core value of integrity, we say what we mean, mean what we say, and stick to our commitments. These ethical AI principles guide us as we address our customers’ evolving needs through innovating the business of enterprise cloud applications in the face of a rapidly changing and complex regulatory environment.
We engage U.S. federal, state, and local governments; the European Union; and other governments around the world to advocate for workable, risk-based regulatory approaches that build trust in AI technology and enable innovation. As our development process continues to evolve to account for new best practices and emerging regulatory frameworks, we remain committed to supporting the delivery of trustworthy AI solutions that provide value to our customers, the workforce, and society.
ByGhadeer Redler,
Staff Writer, Workday
To say the past year has been a momentous one for AI is certainly an understatement.
The release of OpenAI’s ChatGPT in late 2022 lit up the internet with astonishing AI-generated responses to a range of complicated questions and prompts. One especially colorful and memorable example? Instructions written in the style of theKing James Biblefor how to remove a peanut butter sandwich from a VCR.
Toss in AI-produced art using text-to-image tools and suddenly, the future of generative AI—and its potential value—feels very tangible. Not surprisingly, the AI race between the big tech firms has heated up in response.
But this new era of AI is about much more than improved search engines and word-processing software. Dig beneath the breathless headlines about ChatGPT and other new tools and one of the biggest step changes in AI history becomes clear: the availability of pretrained models that can be adapted to just about any task.
The recently unveiled content-generating AI tools are all based on “foundation models”—trained on massive quantities of raw data, such as language and images, that can be customized. These foundation models will revolutionize how and where businesses across industries use AI.
In fact, 96% of business executives are “either very or extremely inspired” by the new capabilities offered by AI foundation models, and 95% say it will usher in a new era of enterprise intelligence, according to Accenture’s report“When Atoms Meet Bits: The Foundations of Our New Reality.”But first, business leaders must marshal the right resources to realize AI’s potential. Below are some of the key findings from Accenture regarding the future of AI.
Foundation models 101.
To seize the AI advantage, businesses must understand the distinctive qualities of foundation models that allow them to create novel capabilities and business value.
So, first things first: What exactly is a “foundation model”?Stanford Institute for Human-Centered Artificial Intelligenceresearchers coined the term in 2021 to describe large AI models trained on a vast quantity of data with significant downstream task adaptability.
A foundation model can be trained on one data modality (such as text) or several—such as text and images (such as ChatGPT), or even sound and video. Two prominent types of foundation models pushing generative AI forward are transformer machine learning (ML) models and large language models. Both are neural networks involving hundreds of millions, even trillions, of prediction-related parameters.
What makes these models uniquely powerful—and perhaps endlessly adaptable—is that their capabilities are not task specific. Because they are broadly trained across one or more data modalities, foundation models can learn new tasks with little to no additional training. As long as the task is within its domain, the AI can handle it.
What we’re seeing is this learning ability in action—and the race to harness its capabilities is on. Google, Microsoft, Baidu, and Meta have all created their own large language models while other companies, such as OpenAI, have created large multimodal models. DeepMind’s Gato may be the most advanced multimodal AI model yet: it can complete more than 600 tasks including chatting, captioning images, playing Atari video games, and stacking blocks with a robotic arm.
For now, most foundation models are fairly limited in the amount of data they’re trained on—mostly just natural language (text) and images. As models involve more and more varied data—video, 3D spatial data, protein structures, industrial sensor data—their potential uses and value will skyrocket. In fact, 97% of global executives in the Accenture report agree that AI foundation models will enable connections across data types, revolutionizing where and how AI is used, per our report.
The new competitive differentiator.
Not surprisingly, the recent advances in AI foundation models have grabbed the attention of business leaders across the globe.
Companies are now experimenting with these models, adapting them for tasks that range from powering customer service bots to automated coding. And just as quickly as the models advance, organizations are discovering new ways to use them.
Take CarMax, for example. The company recently used OpenAI’s GPT-3 model to read and synthesize more than 100,000 customer reviews for every vehicle the company sells. The model then produced 5,000 summaries—a task the company says would have taken its editorial team 11 years to complete.
这个例子强调了一个重要的一点how and why generative AI is poised to impact so many industries. Most companies won’t need to build their own foundation models. Instead, they can access existing models as platforms via open source channels or paid access. Just as companies lean on public cloud data centers, they will increasingly tap AI models created and offered by other companies. Thousands of applications have already been powered by OpenAI’s GPT-3, including copywriting, website building and, of course, chatbots.
97% of global executives in the Accenture report agree that AI foundation models will enable connections across data types.
There are two major benefits businesses should keep in mind. First, these models will completely transform human-AI interaction, whether that’s through natural language communications or coding. Google, for example, has already developed an AI code completion tool that has boosted software engineers’ productivity. Second, foundation models are making possible new AI applications and services that were too difficult—or even impossible—to build before. Again, this is because most businesses will be able to ramp up AI capabilities using pretrained models—no need to create their own model from scratch.
No surprise, then, that 98% of global executives agree AI foundation models will play a key role in their company strategies in the next three to five years, theresearch finds.
Because the frontier of AI models and their capabilities is moving so fast, companies will need to tread carefully to anticipate and avoid pitfalls. For example, a foundation model is only as good as the data it’s trained on—and many datasets are biased due to the historic exclusion of certain populations and demographics. In other words, there are real risks that all businesses should consider when deciding whether to leverage the power of a foundation model.
That said, a paradigm shift is now underway in the business of AI. There’s no question that these models will impact every industry, but what will set businesses apart is how they use AI and what problems they attempt to solve with it.
A paradigm shift is now underway in the business of AI.
