Dr. Priya Ponnapalli引用了哈佛商業評論分析服務(Harvard Business Review Analytic Services)和哈佛商學院教授Karim R. Lakhani所撰寫的《在AI時代競爭》一書中的觀點。他們在2020年發表了同名文章，指出AI/ML轉型始於一個明確而周全的策略，以解決特定的商業問題，然後獲得關鍵利益相關者的支持，並實現數據民主化，讓每個人都能獲得訪問和受益於其使用。從那裡開始，公司可以實施AI來解決最初確定的商業問題。
Dr. Priya Ponnapalli還探討了如何克服文化和商業上的障礙，以實現大規模採用AI。需要重新思考公司文化，將AI納入公司的核心價值觀和戰略中。同時，還需要建立一個跨部門的團隊，包括數據科學家、工程師和業務人員等，以確保項目的成功實施。此外，還需要投資於培訓和發展員工的技能和知識，以確保他們能夠適應新技術和工具。
總之，此文提供了有關如何通過人工智慧實現成功創新的有用信息。它強調了成功實施AI/ML所需的策略、技術、數據民主化、文化轉變和跨部門合作等方面。如果您正在尋找有關如何在您的企業中使用AI來創新的建議和指南，這篇文章可能會對您有所幫助。
關鍵字
1. 人工智慧 (Artificial Intelligence)2. 機器學習 (Machine Learning)
3. 創新 (Innovation)
4. 成功 (Success)
5. 策略 (Strategy)
6. 數據民主化 (Data Democratization)
7. 文化轉變 (Cultural Transformation)
8. 跨部門合作 (Cross-Functional Collaboration)
9. 實施合作夥伴 (Implementation Partner)
10. 商業問題 (Business Problems)
數據民主化 (Data Democratization)
數據民主化對於公司治理和企業成長都非常重要。在公司治理方面，數據民主化可以幫助企業更好地了解其內部運作和市場趨勢，從而更好地制定商業策略和做出決策。它還可以促進更好的合作和知識共享，從而提高員工的數字素養和決策能力。
在企業成長方面，數據民主化可以幫助企業更好地了解其客戶、市場趨勢和競爭對手等方面的信息。這些資訊可以幫助企業制定更好的產品和服務，並開發更有效的營銷策略。此外，數據民主化還可以促進創新和探索新的商業模式，從而推動企業成長。
Keys to Successful Innovation through Artificial Intelligence 利用人工智慧成功創新的關鍵
Dr. Priya Ponnapalli
Senior Manager, Applied Science
Amazon Machine Learning Solutions Lab
Artificial intelligence (AI) and machine learning (ML) have the potential to transform nearly every industry, but many organizations struggle to adopt and implement AI/ML at scale. Recent Gartner research shows that only 53% of ML projects make it from prototype to production. Chief information officers and IT leaders find it hard to scale AI/ML projects because they lack the tools and talent to create and manage a production-grade AI pipeline.
Data is often cited as the number one challenge. The other common barriers we see today are business- and culture-related. For instance, organizations often struggle to identify the right use cases to start their ML journey, which is often exacerbated by a shortage of skilled talent to execute on an organization’s ML ambitions. Business and technical leaders play a critical role in addressing these challenges by driving a culture of continuous learning and innovation; however, many lack the resources to develop their own knowledge of ML and its use cases. According to “The State of AI in 2021,” McKinsey & Co.’s global survey, there are certain best practices that differentiate AI/ML high performers from those that struggle to see the full value of AI/ML. On the technical front, the companies seeing the biggest bottom-line impact from AI adoption are more likely to follow both core and advanced AI best practices, including ML operations, move their AI work to the cloud, and spend on AI more efficiently.
人工智慧（AI）和機器學習（ML）有潛力改變幾乎所有產業，但許多組織在採用和實施大規模AI/ML時仍感困難。最近Gartner的研究顯示，只有53％的ML項目從原型成功推展到生產環境。資訊長官和IT領袖發現，他們缺乏創建和管理生產級AI流水線所需的工具和人才，因此很難擴展AI/ML項目。
數據常常被認為是最大的挑戰。今天我們看到的其他常見障礙是與業務和文化相關的。例如，組織通常很難識別開始其ML之旅的正確用例，這往往會因缺乏實現組織ML雄心的熟練人才而惡化。商業和技術領袖在通過推動持續學習和創新的文化來解決這些挑戰方面扮演著關鍵角色; 然而，許多人缺乏發展自己有關ML及其用例知識的資源。根據麥肯錫公司的全球調查報告“2021年AI的現狀”，AI/ML高級執行者和那些難以看到AI/ML的全部價值之間存在某些最佳實踐區別。在技術方面，從AI採用中看到最大底線影響的公司更有可能遵循基本和先進的AI最佳實踐，包括機器學習運營、將其AI工作移至雲端以及更有效地花費在AI上。
In my role as leader of the Amazon Machine Learning Solutions Lab, I head a global team that helps AWS customers identify and implement their most important ML opportunities. I’ve been fortunate to work with some of the most innovative organizations in the world, such as the National Football League, Formula 1, Intel, and United Airlines, as they transformed their businesses through ML. I’ve seen the challenges of ML-led transformation firsthand and helped our customers overcome them. That’s why I’m very excited to present this research from Harvard Business Review Analytic Services that not only uncovers some of the common challenges to AI/ML implementation but also offers guidance to overcome them.
作為亞馬遜機器學習解決方案實驗室的領導者，我帶領一個全球團隊，幫助AWS客戶識別和實施最重要的ML機會。我很幸運能夠與世界上一些最具創新性的組織合作，例如美國國家橄欖球聯盟、一級方程式賽車、英特爾和聯合航空公司，幫助他們通過ML轉型。我親眼見證了ML引領的轉型所面臨的挑戰，並幫助客戶克服它們。這就是為什麼我很興奮能夠呈現哈佛商業評論分析服務的這項研究，不僅揭示了AI/ML實施中一些常見挑戰，還提供了克服這些挑戰的指導。
Artificial intelligence (AI) and machine learning (ML) are playing an increasingly outsized role in strengthening and transforming industries around the world. The global AI market value is expected to reach $267 billion by 2027,1 and the technology is expected to contribute $15.7 trillion to the global economy by 2030.2 While the technology is still in its infancy in some industries, other sectors are testing the waters, and still others are already reaping the benefits from their AI/ML transformation and charting a path for their industries.
More and more companies are using AI/ML, for everything from boosting productivity to enhancing customer experiences and satisfaction to making better decisions faster. These technologies are also used to generate new revenue opportunities and improve operational efficiencies.
As the world economy moves from a time when every industry operates from a different set of core competencies to one shaped by data and analytics, everyone from C-suite executives to IT teams must understand how to use AI/ ML strategically and efficiently to achieve their organizations’ transformational and strategic goals.
But the road to a successful AI/ML implementation is not always a straight or smooth one. The journey may come with detours and require constant reiteration and reevaluation to stay on track and produce the intended outcome. Defining business goals is the first step to figuring out what sort of strategy is needed. “In every large corporation, you have somebody screaming, ‘You need to do more AI!’ The question from smart people is, ‘For what reason?,’ says Mark Maenner, head of data transformation for the BMW Group.
人工智慧（AI）和機器學習（ML）在全球各行各業中發揮著越來越大的作用，加強並改變產業。預計到2027年，全球AI市場價值將達到2670億美元，而這項技術預計到2030年將為全球經濟做出157萬億美元的貢獻。雖然在某些行業中這項技術仍然處於起步階段，但其他行業正在嘗試探索，還有些已經從他們的AI/ML轉型中獲益，為他們的產業開拓了道路。
越來越多的公司正在使用AI/ML，從提高生產力到增強客戶體驗和滿意度，再到更快速做出更好的決策。這些技術也用於生成新的收入機會和提高運營效率。
隨著世界經濟從每個行業都擁有不同核心能力的時代轉向由數據和分析塑造的時代，無論是高管還是IT團隊，每個人都必須了解如何戰略性地和有效地使用AI/ML來實現他們組織的轉型和戰略目標。
但是，成功實施AI/ML的道路並不總是一條直線或平滑的路。這段旅程可能會有很多彎路，需要不斷重申和重新評估，以保持在軌道上並產生預期的結果。明確定義業務目標是找出需要的戰略所需的第一步。“在每家大型公司中，你總有人大聲喊著‘你需要做更多的AI！’而聰明人的問題是‘為了什麼目的？’”BMW集團的數據轉換負責人Mark Maenner如是說。
Implementing AI/ML successfully requires more than just great technology and mountains of data. It also requires rethinking silos and fragmented legacy systems, adding capabilities, and retooling the company culture, Marco Iansiti and Karim R. Lakhani, Harvard Business School professors and coauthors of Competing in the Age of AI, wrote in a 2020 Harvard Business Review article of the same name.
An AI/ML transformation starts with a well-conceived strategy to address specific business problems, then moves to getting buy-in from key stakeholders and democratizing the data so that everybody has access and can benefit from its use. From there, companies can implement AI to address the business problem initially identified.
AI/ML Transformation, Fueled by Covid-19
As Covid-19 has shown, sometimes long-range planning simply isn’t possible. The pandemic forced businesses in every sector to revamp their operations and invest in AI/ML, something that may have been unthinkable for many just a year before. Many enterprises had to create systems on the fly or were forced to scale projects they’d delayed for one reason or another, with great results in many cases.
“AI-driven processes can be scaled up much more rapidly than traditional processes can, allow for much greater scope because they can easily be connected with other digitized businesses, and create incredibly powerful opportunities for learning and improvement,” wrote Lakhani and Iansiti in the “Competing in the Age of AI” article.
要成功實施AI/ML，需要的不僅僅是優秀的技術和大量的數據。哈佛商學院教授、《Competing in the Age of AI》的共同作者Marco Iansiti和Karim R. Lakhani在2020年同名的哈佛商業評論文章中寫道，還需要重新思考孤立的部門和分散的遺留系統，增加能力，重新調整公司文化。
AI/ML轉型始於一個周密的戰略，以解決特定的業務問題，然後轉向獲得關鍵利益相關者的支持，並實現數據民主化，以便每個人都可以訪問並受益於其使用。從那裡開始，公司可以實施AI來解決最初確定的業務問題。
AI/ML轉型受Covid-19推動
正如Covid-19所顯示的那樣，有時候長期規劃根本是不可能的。這場大流行迫使每個行業的企業重新改進其營運，並投資於AI/ML，這在一年前對許多人來說可能是不可想像的。許多企業不得不即時創建系統，或者被迫擴大他們因某種原因推遲的項目，這在很多情況下取得了很好的效果。
“AI驅動的流程可以比傳統流程更快地擴展，因為它們可以輕鬆地與其他數字化企業相連接，並創造令人難以置信的學習和改進機會，” Lakhani和Iansiti在《Competing in the Age of AI》的文章中寫道。
AI/ML was central to the fight against Covid-19. Scientists used these technologies to aid in everything from diagnosis and drug development to forecasting the spread of the disease as well as to monitoring and surveilling the population.3
The benefits of these technologies during Covid-19 extend beyond advances in the health care industry, too. Jack Berkowitz, chief data officer at ADP Inc., says the HR technology company had already moved its people analytics and workforce benchmarks to the cloud, but the pandemic was a force multiplier for its use of AI/ML. “We were able to build new capabilities we never thought possible,” says Berkowitz. “In addition to day-to-day operations and analytics for our clients, we were deeply involved in the PPP [Paycheck Protection Program] loans because we pay a large portion of the workforce, particularly for the small and midsize businesses. To do that processing and build those reports they needed to file the loans, [and] we had to do that all in the cloud.” Within 20 days, all relevant information clients needed for their PPP applications was available and accessible in the cloud.
AI/ML在抗擊Covid-19方面發揮了重要作用。科學家們使用這些技術幫助診斷和藥物開發，預測疾病傳播，以及監測人口。這些技術在Covid-19期間的好處不僅限於醫療保健行業的進步。ADP公司的首席數據官Jack Berkowitz表示，這家人力資源科技公司已經將其人力資源分析和員工基準轉移到了雲端，但疫情卻成為了其使用AI/ML的乘數。“我們能夠建立以前從未想過的新能力，”Berkowitz說道。“除了日常運營和分析外，我們還深入參與了PPP（發薪保護計畫）貸款，因為我們支付了大部分的勞動力，特別是小型和中型企業。為了處理這些貸款並編制需要提交的報告，[我們必須全部在雲端中進行]。在20天內，客戶提交PPP申請所需的所有相關信息都可以在雲端中輕鬆地獲取和使用。”
Berkowitz was pleasantly surprised by clients’ readiness to accept these technologies and capabilities. “In the HR space, people have been classically a bit more resistant to taking on new things, but they’ve really embraced those capabilities. When they’re thought through and packaged in a way that’s consumable, the uptake is through the roof.”
The BMW Group was also well into its AI/ML journey prior to the pandemic, but Covid-19 created a sense of urgency to take all its AI capabilities remote. “[The pandemic] was an accelerator, not only for AI and ML but also for gaining insight from all the data we had, as well,” says Josef Viehhauser, platform lead and enterprise analytics at the BMW Group. “We already had use cases running on our supply chains, most of which are quite simple events. The pandemic brought an understanding of the influence of catastrophes on the supply chain in specific regions. We were able to boost our existing learnings to move us forward and get better in those areas.”
Berkowitz對客戶接受這些技術和能力的準備度感到愉快驚訝。“在人力資源領域中，人們通常對接受新事物有些抗拒，但他們真正地擁抱了這些能力。當它們經過思考並以易於消費的方式打包時，吸收率非常高。”
BMW集團在疫情爆發前就已經深入探索AI/ML，但是Covid-19創造了一種緊迫感，迫使他們將所有的AI能力轉移到遠程。BMW集團平台主管和企業分析師Josef Viehhauser表示：“（疫情）不僅是AI和ML的加速器，還有利用我們所擁有的所有數據進行洞察分析。我們已經有一些供應鏈的應用案例在運行，大多數是相當簡單的事件。疫情使我們更加了解災難對特定地區供應鏈的影響。我們能夠加強現有的學習，提高在這些領域的能力，並向前邁進。”
Successfully Innovating with AI/ML
Long before the pandemic, companies around the globe were realizing the transformative effect of these essential, strategic technologies. A Harris poll found that 55% of companies reported that they accelerated their AI strategy in 2020 due to Covid-19, and 67% expect to further accelerate their AI strategy in 2021.4
Still, according to a 2020 NewVantage Partners survey, nine out of 10 leading businesses have investments in AI technologies but less than 15% deploy AI capabilities in their work. This huge gap may be in large part because many companies don’t fully understand the technology’s full potential—or they don’t have a clear strategy to use AI/ML to meet their goals. Organizations that do are able to innovate, develop intuitive products, and deliver better service.
在疫情爆發之前，全球各地的公司已經意識到這些基礎和戰略技術的轉型作用。哈里斯民調發現，55％的公司報告稱，由於Covid-19，他們在2020年加速了AI戰略，67％的公司預計在2021年進一步加速其AI戰略。
然而，根據2020年NewVantage Partners的一項調查，九成領先企業都在AI技術上投資，但不到15％的企業在其工作中使用了AI能力。這種巨大的差距可能很大程度上是因為許多公司沒有充分理解技術的全部潛力，或者他們沒有明確的策略來使用AI/ML來實現他們的目標。那些有策略的組織能夠創新，開發直觀的產品，提供更好的服務。
The BMW Group is one such company that leverages technology effectively to increase innovation. It employs AI/ML for everything from product development to forecasting demands for its goods and services, explain the BMW Group’s Viehhauser and Maenner. The company developed a proprietary translation solution to help its multilingual workforce better communicate. And it processes energy-relevant data at all its locations to establish energy consumption patterns and heat and cool its buildings more efficiently. “BMW Intelligent Personal Assistant,” a voice- activated virtual assistant, provides information about the vehicle, plays entertainment, and helps customers park and drive more safely. In addition, the company has a new operating system that offers an all-encompassing, intelligent, multi-sensory experience tailored to the user at hand. The company is able to fulfill its overall mission to make better products and enhance the customer experience through the effective use of technology.
Similarly, ADP uses AI/ML to aggregate and anonymize massive amounts of data to help its customers maximize employee retention, attract new talent, compare key people metrics to their competition’s, and manage risk, compliance, and labor costs, notes ADP’s Berkowitz. The benefits extend beyond HR. “So who are we building these systems for? Primarily, people think about the HR practitioner—the person who makes sure that you get paid or that your benefits are there or that your promotions are taken care of. We also build them for CXOs [chief experience officers], the CHRO [chief human resources officer], or the CFO, somebody who has an operational responsibility, or it could be a first-line manager.”
Technology helped ADP reduce 21 million job titles down to several thousand, which allowed the company to develop compensation benchmarks across industries and positions. “We process that information to come up with, for example, what does a software developer make in Seattle versus a software developer in Austin versus one in New York or Sioux City, Iowa? That [assessment] takes a massive amount of machine learning and a massive amount of computing capability,” he explains. “We also provide tools companies can use to move the needle on their diversity and inclusion by measurement, because once you can measure what’s going on in your company, you can improve it.”
In the retail industry, an effective use of AI/ML can help enhance the customer experience through predictive behavior analysis and hyper-personalization techniques. These technologies can be programmed to call the customer by name and track customer preferences. Chatbots, for instance, are now a staple for responding to FAQs and providing specific customer information such as account balances and order status.
Contact centers are deploying AI/ML services to handle a variety of customer requests and inquiries, such as through intelligent chatbots, intelligent voicebots, smart routing, real-time voice analytics with sentiment analysis, agent assist with next best action, and post-call analytics. Enhancing the overall customer experience can boost repurchase odds and improve long-term loyalty.
Keys to a Successful AI Implementation
These innovations didn’t happen by accident. The BMW Group, ADP, and other successful companies have worked diligently and deliberately to develop a strategy that would help them achieve success by starting with their big-picture goals and working backward. While the details of implementing a successful strategy for AI/ML vary depending on each business and the specific project, the steps to get there are universal.
Define your why.
Adopting AI/ML is never the goal; AI exists to support the overall business objective developed by considering the larger- picture goal. As AI evangelist Andrew Ng asserts in his AI Transformation Playbook, strategy is key to harnessing the power of AI.
Maenner says every project at the BMW Group begins with one question: What is the total impact of the project versus the investment you need to make? “BMW is a product company. No one is delivering solely AI to a customer,” he says. “AI and ML play an integral role in the digital transformation of the BMW Group and help us improve the product experience for our customers, the way we develop our products, or even understanding processes.”
ADP began its AI transformation with the goal of corralling the data from more than 90 million employee records into robust models that could be employed to serve its customers under a variety of different scenarios. The first step was making sure the data was accurate and coherent. “We treated data as a product to enable our AI scientists and engineers to get the information they need quickly and efficiently,” says Berkowitz. “In our first discussion, we determined the components and budget we needed to do that. When some of the things they thought would pan out didn’t work, we pivoted. We were constantly iterating.”
In the end, that iterative trial-and-error process eventually led to a 30% decrease in downtime and a 60% increase in mobile app deployments, explains Berkowitz. When the pandemic hit, the company was ready.
Don’t try to boil the ocean.
There is always the temptation to tackle everything on a company’s AI/ML wish list at once, but business leaders should start small by breaking down the ultimate goal into its component parts. Identify specific use cases in which AI/ ML could solve business problems or add value to existing products and services.
Simple projects can pay huge dividends and spark even greater successes, says Kirk Borne, chief science officer at Leesburg, Va.-based DataPrime, a data science, analytics, and AI/ML products and solutions company. “A recommender engine saying people who bought this product also bought this other product is a pretty simple thing that generates significant revenue. I think 70% of Netflix’s revenue comes from a recommender.”
Move from control to consensus.
Admiral Grace Hopper, the founder of modern computing, was quoted in a 1976 Computerworld article as saying the most dangerous phrase in the language is, “We’ve always done it this way.” A successful implementation needs executive buy-in to get everyone ready for change and on board with a new plan.
AI/ML implementation doesn’t need to come just from the top; it is a team sport, and the whole business is the team. “When you expect a data team to produce things that benefit everybody without bringing in the different parts of the business, that project is going to fail,” says Borne.
Creating engaged, high-performing teams that share data, communicate openly, and learn from feedback keeps everyone invested in the mission and in the loop about what the company wants to achieve. “There’s value in bringing all those other voices into the discussion, whether it’s diversity of race or age. [It’s] not a top-down or bottom-up approach; it’s an ‘everyone should have a seat at the table’ approach,” says Borne.
Creating team cohesion through effective communication and shared goals in service of a higher cause is important for success. When employees and teams truly understand the why, they quickly get on board with the how and the what. Whether it’s through videoconference technology, messaging systems, email, or meet-ups, communication is essential to stay on top of what’s working and not working and to remain alert and ready for course corrections.
Break down the silos.
Large companies have traditionally separated divisions such as marketing, operations, and HR into different entities that operate like church and state. But today’s most efficient organizations eliminate silos and integrate data analytics across divisions to power as many processes as possible. This development represents a big but necessary shift.
“We discovered that it’s really hard to share best practices across many teams,” says Viehhauser. “So, [we] build up [our] use cases, and after having executed many use cases, then we take a step back, reflect, and ask [ourselves], ‘What can we harmonize across all these use cases? What can I share in terms of know-how?’”
According to Viehhauser and Maenner, the BMW Group centralized its resources and created cross-functional analytical teams to better evaluate its needs and to discover new use cases that could be addressed using AI/ML principles. It also made information more accessible and available throughout the company. Even sharing code repositories and data catalogs proved to be a big hit with its employees, all of whom have an opportunity to learn data science through a specified and tailor-made training program.
“To make the transformation work, you must take people where they are and train them on the competencies,” says Maenner. “They do not need to be data scientists, but they need to understand what data or what AI is. It’s important to give tech teams the insight and purpose to help them understand how and when they’re making the difference. Having a lot of people trained in the technology and its possibilities is the power in what we have today.”
ADP’s adaptation began with one small team working directly with Berkowitz. Today, the company operates on a hub-and-spoke model, with 11 different teams led by experts in specific areas such as tax, sales, or marketing who are tasked with spreading the capabilities throughout the company.
Recognize a failing project.
There’s a common denominator among companies that fail in their efforts to adopt AI: They lack the vision, discipline, talent, alignment, right information, and right use case.
“AI often starts with the sandbox. It’s no surprise that moving from a sandbox to production is super hard to do,” says Viehhauser. “You have to come up with templates that people can reuse to make the time moving from [the] sandbox to production really short. Then you’re able to ultimately create value and leverage benefits.”
So, what separates successful projects from unsuccessful ones? Many projects fail because they have a data problem. They may have the wrong kind of, not enough, or biased data. Unsurprisingly, AI models need a massive amount of good- quality data. While no company sets out to create a biased AI model, it can happen if diverse perspectives are omitted in the design process. “In the early days of the big data revolution, companies bragged about how much data they had. And I’m thinking to myself, ‘Everyone’s got a lot of data. That’s not a competitive differentiator. Tell me about the productive value!’” says Borne.
Viehhauser says that the BMW Group established a centralized, on-premises data lake in 2015 that collects and combines anonymized data from sensors in vehicles, operational systems, and data warehouses to derive historical, real-time, and predictive insights. However, the company needed to more easily scale its platform to support the growing demands of internal and external stakeholders.
Hence, it developed a cloud-native solution to both support the data needs of all the various internal business units and to allow the company to move quickly to address the array of emerging use cases.
Find the right partner.
The chances of a successful strategy fueled by AI/ML greatly improve, says Borne, if business leaders remember that data is a science and its implementation requires an interactive process of testing, validating, and refining the hypothesis, then testing and refining the next iteration.
An experienced AI vendor can help organizations succeed during this phase, says Maenner. “We say, ‘If you swim in your soup all night and day, then you probably just know your soup.’ Working with a good implementation partner can broaden your understanding of the possible and create a quicker return on investment.”
Use data responsibly and ethically.
As AI becomes more prevalent in daily life, there are increased concerns about whether that data is used responsibly, which means everything from enhancing data privacy to reducing bias in the modeling.
To assure customers that their data would be dealt with ethically, ADP rolled out an AI and data ethics board, announced its principles for using and applying the technology, and built its own monitoring setup. “Included in that are things about transparency, ‘explainability,’ and bias. We’ve implemented some things that are purely automated around how to measure those things. We continue to push the outer edges,” says Berkowitz.
Similarly, the BMW Group designed its platform around principles set out in its Code of Ethics for AI guidebook while outlining the company’s commitment to human agency and oversight, nondiscrimination, environmental and social well- being, and data transparency and accountability.
Building in the ethical component goes a long way toward building trust with employees, customers, and the broader population, which is essential for the long-term growth of AI/ML.
Conclusion
Maenner believes the transformational aspect of these technologies comes down to finding the right balance between the revolution and the evolution. “You really need to push people on one side and ask them to take ownership of their responsibilities on the other side,” he says. “They have to understand that if they are investing [in] AI/ML, it will also harvest success in terms of budgets or improved processes.”
While there are many challenges in adopting AI/ML or finding the right strategy to employ these technologies, enterprise success stories like those of ADP and the BMW Group show that any hurdles are clearly outweighed by the benefits. Further, adopting AI is no longer optional for businesses that want to remain competitive, and it is even more critical for those that want to stand out as industry leaders.
In essence, it’s a tug-of-war to see which prevails—AI or the status quo. Close observers such as DataPrime’s Borne are betting on AI. “Every company needs to have a competitive advantage and will keep pushing against the headwinds to succeed,” he says.
"Implementing AI/ML successfully requires more than just great technology and mountains of data. It also requires rethinking silos and fragmented legacy systems, adding capabilities, and retooling the company culture." - 透過人工智慧和機器學習實現成功的創新需要不僅有優秀的技術和大量數據，還需要重新思考孤立的系統、添加能力並改變公司文化。
"The journey may come with detours and require constant reiteration and reevaluation to stay on track and produce the intended outcome." - 這個旅程可能會有很多彎路，需要不斷重複和重新評估才能保持正確方向並產生預期的結果。
"Working with a good implementation partner can broaden your understanding of the possible and create a quicker return on investment." - 與一個好的實施合作夥伴合作可以擴大您對可能性的理解，並創造更快的投資回報。