Introduction

  • Nike, Inc. is a major publicly traded sportswear and equipment supplier based in the US.
  • The company is headquartered near  Beaverton , Oregon .
  • It is the world's leading supplier of  Athletic shoes  and apparel and a major manufacturer of  sports Equipment
  • Revenue in excess of  US$ 18.6 billion in its fiscal year 2008 (ending May 31, 2008).
  • As of 2008, it employed more than 30,000 people worldwide..
  • The company was founded in January 1964 as  Blue Ribbon Sports  by  Bill Bowerman and Philip Knight.
  • officially became Nike, Inc. in 1978.
  • Nike markets its products under its own brand as well as Nike Golf, Nike Pro,  NIKE+ , Air Jordan , Nike Skateboarding.
  • In addition to manufacturing sportswear and equipment, the company operates retail stores under the Niketown name.
  • Nike sponsors many high profile athletes and sports teams around the world, with the highly recognized trademarks of "Just do it" and the  Swoosh  logo.
  • Nike, originally known as  Blue Ribbon Sports , was founded by  University of Oregon  track athlete Philip Knight and his coach Bill Bowerman in January 1964. The company initially operated as a distributor for Japanese shoe maker  Onitsuka Tiger
  • The company's profits grew quickly, and in 1966, BRS opened its first retail store
  • By 1971, the relationship between BRS and Onitsuka Tiger was nearing an end.
  • BRS prepared to launch its own line of footwear, which would bear the newly designed  Swoosh  by  Carolyn Davidson .
  • The Swoosh was first used by Nike in June 1971, and was registered  on January 22, 1974.
  • By 1980, Nike had reached a 50% market share in the U.S. athletic shoe market
  • Nike's first national television commercials ran in October 1982 during the broadcast of the  New York Marathon .
  • It was agency co-founder  Dan Weiden  who coined the now-famous slogan  "Just Do It"  for a 1988 Nike ad campaign, which was chosen by  Advertising Age  as one of the top five ad slogans of the 20 th century
  • Throughout the 1980s, Nike expanded its product line to include many other sports and regions throughout the world.
  • Nike produces a wide range of sports equipment.
  • Their first products were track running shoes. They currently also make shoes, jerseys, shorts, etc. for a wide range of sports including  track and field, baseball , ice hockey , tennis , etc.
  • The most recent additions to their line are the  Nike 6.0 ,  Nike NYX , and  Nike SB  shoes, designed for  Skateboarding .
  • Nike has recently introduced cricket shoes, called  Air Zoom Yorker , designed to be 30% lighter than their competitors'.
  •  In 2008, Nike introduced the  Air Jordan XX3 , a high-performance basketball shoe designed with the environment in mind.
  • Nike is well known and popular in  Youth Culture, chav  culture and  Hip Hop culture  as they supply  urban Fashion  clothing.
  • Nike recently teamed up with  Apple.inc  to produce the  Nike+  product which monitors a runner's performance  via  a radio device in the shoe which links to the iPod Nano
  • Some of Nike's newest shoes contain  FLywire  and Lunarlite Foam. These are materials used to reduce the weight of many types of shoes.
  • Nike shoes are carried by multi-brand stores and the exclusive Nike stores across the globe.
  • Nike sells its product to about 20,000 retail accounts in the U.S. and in almost 200 countries around the world.
  • In the international markets, Nike sells its products through independent distributors, licensees and subsidiaries. I
  • Independent distributors need not adapt to local pressures because the 4Ps of marketing are managed by distributors.
  • Nike’s pricing is designed to be competitive to the other fashion  Shoe Retailers .
  • The pricing is based on the basis of premium segment as target customers.
  • Nike as a brand commands high premiums. Nike’s pricing strategy makes use of vertical integration in pricing wherein they own participants at differing channel levels or take part in more than one channel level operations.
  • This can control costs and influence product pricing.�
  • Promotion is largely dependent on finding accessible store locations. It also avails of targeted advertising in the newspaper and creating strategic alliances.
  • Nike has a number of famous athletes that serve as brand ambassadors such as the Brazilian Soccer Team (especially Ronaldino, Renaldo, and Roberto Carlos), Lebron James and Jermane O’Neal for basketball, Lance Armstrong for cycling, and Tiger Woods for Golf.
  • Nike also sponsors events such as Hoop It Up and The Golden West Invitational. Nike’s brand images, the Nike name and the trademark swoosh, make it one of the most recognizable brands in the world. Nike’s brand power is one reason for its high revenues. Nike’s quality products, loyal customer base and its great marketing techniques all contribute to make the shoe empire a huge success
  • ADVERTISEMENTS:
  • In 1982, Nike aired its first national television ads, created by newly formed ad agency  Wieden+Kennedy , during the New York Marathon. This was the beginning of a successful partnership between Nike and W+K that remains intact today.
  • The Cannes Advertising Festival has named Nike its  Advertiser of the Year  on two separate occasions, the first and only company to receive that honor twice (1994, 2003).
  • Nike also has earned the  Emmy Award  for best commercial twice since the award was first created in the 1990s. The first was for "The Morning After," a satirical look at what a runner might face on the morning of January 1, 2000 if every dire prediction about Y2K came to fruition. The second Emmy for advertising earned by Nike was for a 2002 spot called "Move," which featured a series of famous and everyday athletes in a stream of athletic pursuits.
  • In addition to garnering awards, Nike advertising has generated its fair share of controversy:

Endorsements

  • Nike pays top athletes in many different sports to use their products and  Promote/Advertise  their technology and design.
  • Nike is the official kit sponsor for the  Indian Cricket Team  for 5 years, from 2006 till end of 2010. Nike beat  Adidas  and  Puma  by bidding highest (US$43 Million total).
  • LeBron James- $90 Million
  • Kobe Bryant- $40 Million
  • Christiano Ronaldo- $32.5 million
  • Maria Sharapova- $6 million
  • Roger Federer- $13 million
  • NIKEiD  is a service provided by  Nike  allowing customers to personalise and design their own Nike merchandise.
  • They offer online services as well as physical NIKEiD studios in different countries around the world, including: United Kingdom, France, Spain, Germany, China and the USA.
  • It provides customers the ability to choose from a limited range of different material and colours to develop their own style of tennis shoe.
  • It gave a total of 31 different customizable parts including the base, overlay, accent, lining, stitching, outsole, laces, etc.
  • In total this shoe gave a choice of 82 different materials and option types.
  • It has since expanded from a small web-based service to a large array of stores and new software applications to give consumers a greater range of personalization and uniqueness in a variety of Nike shoes and clothing.

Child Labour Controversy

  • Nike has been accused of using child labour in the production of its soccer balls in Pakistan.
  • While Pakistan has laws against child labour and slavery, the government has taken very little action to combat it.
  • Furthermore the U.S constitution states that child labour is an illegal and inhumane practice and any U.S. company found guilty practicing and encouraging it will be prosecuted.
  • GATT and WTO prohibits member nations, like the United States, from discriminating against the importation of goods made by children.
  • Are dolphins becoming more important than children? A question making WTO to reconsider the children's appeal of the third world.

Competitors

  • Reebok International Limited , a subsidiary of the German sportswear giant  adidas , is a producer of  athletic footwear , apparel, and accessories.
  • In 1890 in Holcombe Brook, a small village 6 miles from the town Bolton , England , Joseph William Foster was making a living producing regular running shoes when he came up with the idea to create a novelty spiked running shoe.
  • After his ideas progressed he joined with his sons, and founded a shoe company named  J.W. Foster and Sons  in 1895.
  • In 1960, two of the founder's grandsons Joe and Jeff Foster renamed the company  Reebok  in England, having found the name in a dictionary won in a race by Joe Foster as a boy; the dictionary was South African edition hence the spelling.
  • The company lived up to the J.W. Foster legacy, manufacturing first-class footwear for customers throughout the UK.
  • In 1979, Paul Fireman, a US sporting goods distributor, saw a pair of Reeboks at an international trade show and negotiated to sell them in North America
  • Adidas is a major German-based sports apparel manufacturer and  Parent Company  of the Adidas Group, which consists of the  Reebok  sportswear company, Taylor made adidas golf  company , and Rockport .
  • Besides sports footwear, the company also produces other products such as bags, shirts, watches, eyewear and other sports and clothing-related goods. The company is the largest  Sportswear  manufacturer in  Europe  and the second biggest sportswear manufacturer in the world, after its U.S. rival Nike.
  • Adidas was founded in 1948 by  Adolf Dassler .
  • Registered in 1949, Adidas is currently based in Herzogenaurach , Germany, along with Puma.
  • The company's clothing and shoe designs typically feature  Three parallel Bars , and the same motif is incorporated into Adidas's current official  logo .
  • The "Three Stripes" were bought from the Finnish sport company  Karhu Sports  in the 1950s.
  • The company  revenue  for 2009 was listed at  € 10.38 billion and the 2008 figure at €10.80 billion.

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Ratings and Reviews

by Daniel Robinson

March 30, 2023

by Desmond Garza

March 29, 2023

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Nike in 2024: Detailed SWOT Analysis with a Complimentary PowerPoint Template

Download Nike's 2024 SWOT Analysis: Comprehensive insights and free PowerPoint template. Key strategies, opportunities, and challenges revealed.

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StrategyPunk

Nike in 2024: Detailed SWOT Analysis with a Complimentary PowerPoint Template

Introduction to the post

Welcome readers! The iconic Nike swoosh is one of the most recognizable logos in the world.

This post will conduct an in-depth SWOT analysis of Nike in 2024. Nike is the world's largest supplier of athletic shoes and apparel. We'll examine Nike's internal strengths and weaknesses and external opportunities and threats impacting the company.

To make this analysis useful, we have also created a free, editable SWOT analysis PowerPoint template that summarizes the key takeaways.

Let's dive right in!

Introduction to Nike

Nike, Inc. is an American multinational corporation headquartered in Beaverton, Oregon. It was founded in 1964 as Blue Ribbon Sports by Bill Bowerman, a track-and-field coach, and his former student Phil Knight.

Nike is the world's most extensive athletic footwear and apparel brand today. Its swoosh logo is one of the most recognizable brand symbols globally.

Nike employs over 75,000 people worldwide and manufactures athletic footwear, apparel, equipment, accessories, and services. It caters to a wide range of sports, including running, basketball, football, training, and more.

Some key facts about Nike:

  • Founded: 1964
  • CEO: John Donahoe
  • Headquarters: Beaverton, Oregon
  • Annual revenue (2023): $51.1 billion
  • Net income (2023): $6.7 billion

Next, look at Nike's history and how it became the sportswear empire it is today.

A Brief Look at the History of Nike

Nike's journey began in 1964 when University of Oregon track athlete Phil Knight and his coach Bill Bowerman set up  Blue Ribbon Sports. They started by importing running shoes from Japan's Onitsuka Tiger (now known as ASICS).

In 1971, Blue Ribbon Sports started making its line of footwear marked with its now famous "Swoosh" logo. The brand was renamed Nike in 1978, named after the Greek goddess of victory.

Nike's early innovations in footwear technology, like the 1979 introduction of the Nike Air cushioning system, helped establish Nike as a leader in athletic footwear innovation. Its shoes were lighter, faster , and provided better shock absorption.

In the 1980s and 90s, Nike signed endorsement deals with celebrity athletes like Michael Jordan and Tiger Woods. The Air Jordan brand of basketball shoes brought Nike colossal success.

Nike also expanded aggressively overseas, mainly focusing on China's untapped market. Today, over half of Nike's sales come from outside North America.

Nike has continued to innovate over the decades with advanced proprietary technologies like Flyknit, Dri-FIT moisture-wicking fabric, and Nike+ digital ecosystem.

This focus on constant innovation and endorsement deals with top athletes have made Nike the most valuable sports brand in the world.

Financials of Nike 2023

In fiscal year 2023 (June 2022 to May 2023), Nike delivered strong financial results despite global economic challenges:

  • Annual revenue grew 6% year-over-year to $51.1 billion
  • Net income rose 6% to $6.7 billion
  • Nike Direct sales (online + retail stores) were up 14% over 2022
  • Earnings per share came in at $4.30, up 7%

Nike's revenues have grown at an average rate of over 5% annually in the past five years. In that timeframe, its net profit margins have remained steady between 14-15%.

About 42% of Nike's sales come directly from Nike retail stores and nike.com. The rest is from wholesale customers like department stores and sporting goods retailers.

Geographically, North America remains Nike's largest market, accounting for over 40% of sales. But China, Europe/Middle East/Africa (EMEA), and Asia Pacific/Latin America (APLA) regions are major growth areas.

After reviewing Nike's recent financial performance, let's move on to an in-depth SWOT analysis for 2024.

In-depth SWOT Analysis of Nike 2024

SWOT stands for Strengths, Weaknesses, Opportunities, and Threats.

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It is a structured framework to evaluate a company's strategic position by looking at internal and external factors.

Nike’s Strengths

Nike's key strengths are:

Global Brand Recognition : Nike's Swoosh logo and "Just Do It" slogan are recognized globally. It has built powerful brand awareness through decades of quality products and marketing.

Innovation Capabilities : Nike has continually innovated in product design, proprietary technology, and digital experiences. Its R&D infrastructure helps sustain the innovation pipeline.

Financial Performance : Nike has delivered consistent revenue and earnings growth historically. Its vertically integrated business model and high-margin Nike Direct sales underpin profitability.

Global Production & Distribution : Nike has well-established outsourced manufacturing capabilities and a solid global distribution infrastructure, ensuring efficient worldwide supply.

Nike’s Weaknesses

Some weaknesses Nike needs to manage are:

Over-reliance on Footwear : Footwear makes up over 60% of Nike's sales. It needs to diversify into apparel and accessories to drive sales growth.

Third-Party Retail Dependence : About 60% of Nike's sales come from wholesalers. As direct-to-consumer channels grow, managing third-party retail relationships is vital.

Lack of Agility : As a large company, Nike lacks the agility to respond quickly to market changes. It needs to streamline its structure and decision-making to become more nimble.

Nike’s Opportunities

Nike can capitalize on the following key opportunities:

Direct-to-Consumer Expansion : Nike can accelerate its online and retail store expansion to grow higher-margin direct sales.

New Products & Categories : Nike has growth headroom to expand into related categories like sports equipment/gear and launch new brands targeting niche consumers.

Emerging Markets : There is significant untapped growth potential for Nike in emerging markets like China, India, the Middle East, and Latin America through localization strategy.

Nike’s Threats

Some threats Nike faces are:

Intensifying Competition : Rivals like Adidas, Under Armour, Lululemon, and local players are competing aggressively and eroding market share.

Margin Pressures : Input cost inflation, wholesale discounting demands, and foreign exchange fluctuations may compress Nike's healthy margins.

Counterfeits : Counterfeit Nike products illegally sold globally can negatively impact brand image and sales. Nike continues its brand protection efforts against counterfeiting.

Nike SWOT Analysis Summary

Internal factors.

Nike's substantial brand equity, innovation capacity, and financial track record are critical internal strategic advantages.

However, over-dependence on footwear and third-party distribution pose risks requiring mitigation focus.

External Factors

Favorable industry growth trends, emerging market expansion potential, and direct sales growth avenues are significant opportunities for Nike. Competition pressures, margin headwinds, and counterfeiting risks need managing through targeted strategies.

Nike can retain its sportswear industry leadership by leveraging its strengths, overcoming weaknesses, seizing opportunities, and countering threats. But it will require strategic clarity and effective execution.

Frequently Asked Questions

related to to SWOT Analysis Nike

What are Nike’s biggest strengths?

Nike’s strengths are its powerful global brand recognition, product development capabilities, extensive distribution network, and consistent financial results.

What are the major threats facing Nike?

Key threats Nike faces are intensifying competition from brands like Adidas and Under Armour, counterfeiting its trademarks, and potential consumer spending declines in an economic recession.

What opportunities can fuel Nike's future growth?

Major opportunities to spur Nike’s growth include expanding digital and e-commerce sales, entering developing markets, and showcasing sustainability credentials to attract socially-conscious shoppers.

What is Nike's competitive strategy?

Nike's competitive strategy is centered on product differentiation. It aims to offer athletic shoes and apparel with cutting-edge styles, technologies, and performance that stand out from rivals. Nike also invests heavily in signing endorsement deals with top athletes to enhance its aspirational and "cool" brand image.

How does Nike's marketing support its business strategy?

Nike's marketing reinforces its product differentiation strategy. Advertising campaigns spotlight product innovations and link Nike to elite athletic performance. Sponsorships of superstars like LeBron James and emotional storytelling around sports tap into consumers' passions. The goal is to strengthen Nike's brand prestige and premium positioning.

What sustainability initiatives has Nike undertaken?

Key sustainability initiatives for Nike include using recycled polyester and leather, eliminating virgin plastic in apparel, implementing renewable energy in owned facilities, and setting targets to cut carbon emissions across its supply chain. The Nike Grind program also recycles old athletic shoes into material for surfaces like running tracks and playgrounds.

How can sustainability provide Nike strategic benefits?

Sustainability can enhance Nike's branding among environmentally-conscious younger consumers who increasingly favor responsible companies. It also makes supply chains more resilient against climate disruptions and criticism regarding labor policies and waste. Furthermore, greener manufacturing can reduce costs over the long run as energy and material expenses decline.

Have there been any notable acquisitions in Nike's history?

Major past acquisitions fueled Nike's growth, including its purchase of surf-inspired footwear brand Hurley International in 2002 and sports technology firm Celect in 2019. Nike obtained valuable intellectual property and innovative product designs from both deals. The Celect takeover also bolstered Nike's data analytics capabilities for demand forecasting and inventory optimization.

Could Nike acquire other companies in the future?

Nike could acquire more petite sports apparel and accessories brands catering to specific consumer niches like yoga, cycling, or outdoor adventure. It might also pursue data analytics firms to continue improving its digital sales platforms and supply chain resilience. However, Nike already possesses a formidable scale, so it seems less inclined toward transformational mergers improved to expanding organically.

Nike SWOT Analysis PowerPoint Template

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A SWOT analysis evaluates the strengths, weaknesses, opportunities, and threats impacting a company.

This free editable PowerPoint template provides a SWOT analysis framework to evaluate Nike's internal strengths and weaknesses and external opportunities and threats. 

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Download this pre-made SWOT analysis template to evaluate Nike's business strategy and document your SWOT conclusions. Customize it easily with your content.

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A generative AI reset: Rewiring to turn potential into value in 2024

It’s time for a generative AI (gen AI) reset. The initial enthusiasm and flurry of activity in 2023 is giving way to second thoughts and recalibrations as companies realize that capturing gen AI’s enormous potential value is harder than expected .

With 2024 shaping up to be the year for gen AI to prove its value, companies should keep in mind the hard lessons learned with digital and AI transformations: competitive advantage comes from building organizational and technological capabilities to broadly innovate, deploy, and improve solutions at scale—in effect, rewiring the business  for distributed digital and AI innovation.

About QuantumBlack, AI by McKinsey

QuantumBlack, McKinsey’s AI arm, helps companies transform using the power of technology, technical expertise, and industry experts. With thousands of practitioners at QuantumBlack (data engineers, data scientists, product managers, designers, and software engineers) and McKinsey (industry and domain experts), we are working to solve the world’s most important AI challenges. QuantumBlack Labs is our center of technology development and client innovation, which has been driving cutting-edge advancements and developments in AI through locations across the globe.

Companies looking to score early wins with gen AI should move quickly. But those hoping that gen AI offers a shortcut past the tough—and necessary—organizational surgery are likely to meet with disappointing results. Launching pilots is (relatively) easy; getting pilots to scale and create meaningful value is hard because they require a broad set of changes to the way work actually gets done.

Let’s briefly look at what this has meant for one Pacific region telecommunications company. The company hired a chief data and AI officer with a mandate to “enable the organization to create value with data and AI.” The chief data and AI officer worked with the business to develop the strategic vision and implement the road map for the use cases. After a scan of domains (that is, customer journeys or functions) and use case opportunities across the enterprise, leadership prioritized the home-servicing/maintenance domain to pilot and then scale as part of a larger sequencing of initiatives. They targeted, in particular, the development of a gen AI tool to help dispatchers and service operators better predict the types of calls and parts needed when servicing homes.

Leadership put in place cross-functional product teams with shared objectives and incentives to build the gen AI tool. As part of an effort to upskill the entire enterprise to better work with data and gen AI tools, they also set up a data and AI academy, which the dispatchers and service operators enrolled in as part of their training. To provide the technology and data underpinnings for gen AI, the chief data and AI officer also selected a large language model (LLM) and cloud provider that could meet the needs of the domain as well as serve other parts of the enterprise. The chief data and AI officer also oversaw the implementation of a data architecture so that the clean and reliable data (including service histories and inventory databases) needed to build the gen AI tool could be delivered quickly and responsibly.

Never just tech

Creating value beyond the hype

Let’s deliver on the promise of technology from strategy to scale.

Our book Rewired: The McKinsey Guide to Outcompeting in the Age of Digital and AI (Wiley, June 2023) provides a detailed manual on the six capabilities needed to deliver the kind of broad change that harnesses digital and AI technology. In this article, we will explore how to extend each of those capabilities to implement a successful gen AI program at scale. While recognizing that these are still early days and that there is much more to learn, our experience has shown that breaking open the gen AI opportunity requires companies to rewire how they work in the following ways.

Figure out where gen AI copilots can give you a real competitive advantage

The broad excitement around gen AI and its relative ease of use has led to a burst of experimentation across organizations. Most of these initiatives, however, won’t generate a competitive advantage. One bank, for example, bought tens of thousands of GitHub Copilot licenses, but since it didn’t have a clear sense of how to work with the technology, progress was slow. Another unfocused effort we often see is when companies move to incorporate gen AI into their customer service capabilities. Customer service is a commodity capability, not part of the core business, for most companies. While gen AI might help with productivity in such cases, it won’t create a competitive advantage.

To create competitive advantage, companies should first understand the difference between being a “taker” (a user of available tools, often via APIs and subscription services), a “shaper” (an integrator of available models with proprietary data), and a “maker” (a builder of LLMs). For now, the maker approach is too expensive for most companies, so the sweet spot for businesses is implementing a taker model for productivity improvements while building shaper applications for competitive advantage.

Much of gen AI’s near-term value is closely tied to its ability to help people do their current jobs better. In this way, gen AI tools act as copilots that work side by side with an employee, creating an initial block of code that a developer can adapt, for example, or drafting a requisition order for a new part that a maintenance worker in the field can review and submit (see sidebar “Copilot examples across three generative AI archetypes”). This means companies should be focusing on where copilot technology can have the biggest impact on their priority programs.

Copilot examples across three generative AI archetypes

  • “Taker” copilots help real estate customers sift through property options and find the most promising one, write code for a developer, and summarize investor transcripts.
  • “Shaper” copilots provide recommendations to sales reps for upselling customers by connecting generative AI tools to customer relationship management systems, financial systems, and customer behavior histories; create virtual assistants to personalize treatments for patients; and recommend solutions for maintenance workers based on historical data.
  • “Maker” copilots are foundation models that lab scientists at pharmaceutical companies can use to find and test new and better drugs more quickly.

Some industrial companies, for example, have identified maintenance as a critical domain for their business. Reviewing maintenance reports and spending time with workers on the front lines can help determine where a gen AI copilot could make a big difference, such as in identifying issues with equipment failures quickly and early on. A gen AI copilot can also help identify root causes of truck breakdowns and recommend resolutions much more quickly than usual, as well as act as an ongoing source for best practices or standard operating procedures.

The challenge with copilots is figuring out how to generate revenue from increased productivity. In the case of customer service centers, for example, companies can stop recruiting new agents and use attrition to potentially achieve real financial gains. Defining the plans for how to generate revenue from the increased productivity up front, therefore, is crucial to capturing the value.

Upskill the talent you have but be clear about the gen-AI-specific skills you need

By now, most companies have a decent understanding of the technical gen AI skills they need, such as model fine-tuning, vector database administration, prompt engineering, and context engineering. In many cases, these are skills that you can train your existing workforce to develop. Those with existing AI and machine learning (ML) capabilities have a strong head start. Data engineers, for example, can learn multimodal processing and vector database management, MLOps (ML operations) engineers can extend their skills to LLMOps (LLM operations), and data scientists can develop prompt engineering, bias detection, and fine-tuning skills.

A sample of new generative AI skills needed

The following are examples of new skills needed for the successful deployment of generative AI tools:

  • data scientist:
  • prompt engineering
  • in-context learning
  • bias detection
  • pattern identification
  • reinforcement learning from human feedback
  • hyperparameter/large language model fine-tuning; transfer learning
  • data engineer:
  • data wrangling and data warehousing
  • data pipeline construction
  • multimodal processing
  • vector database management

The learning process can take two to three months to get to a decent level of competence because of the complexities in learning what various LLMs can and can’t do and how best to use them. The coders need to gain experience building software, testing, and validating answers, for example. It took one financial-services company three months to train its best data scientists to a high level of competence. While courses and documentation are available—many LLM providers have boot camps for developers—we have found that the most effective way to build capabilities at scale is through apprenticeship, training people to then train others, and building communities of practitioners. Rotating experts through teams to train others, scheduling regular sessions for people to share learnings, and hosting biweekly documentation review sessions are practices that have proven successful in building communities of practitioners (see sidebar “A sample of new generative AI skills needed”).

It’s important to bear in mind that successful gen AI skills are about more than coding proficiency. Our experience in developing our own gen AI platform, Lilli , showed us that the best gen AI technical talent has design skills to uncover where to focus solutions, contextual understanding to ensure the most relevant and high-quality answers are generated, collaboration skills to work well with knowledge experts (to test and validate answers and develop an appropriate curation approach), strong forensic skills to figure out causes of breakdowns (is the issue the data, the interpretation of the user’s intent, the quality of metadata on embeddings, or something else?), and anticipation skills to conceive of and plan for possible outcomes and to put the right kind of tracking into their code. A pure coder who doesn’t intrinsically have these skills may not be as useful a team member.

While current upskilling is largely based on a “learn on the job” approach, we see a rapid market emerging for people who have learned these skills over the past year. That skill growth is moving quickly. GitHub reported that developers were working on gen AI projects “in big numbers,” and that 65,000 public gen AI projects were created on its platform in 2023—a jump of almost 250 percent over the previous year. If your company is just starting its gen AI journey, you could consider hiring two or three senior engineers who have built a gen AI shaper product for their companies. This could greatly accelerate your efforts.

Form a centralized team to establish standards that enable responsible scaling

To ensure that all parts of the business can scale gen AI capabilities, centralizing competencies is a natural first move. The critical focus for this central team will be to develop and put in place protocols and standards to support scale, ensuring that teams can access models while also minimizing risk and containing costs. The team’s work could include, for example, procuring models and prescribing ways to access them, developing standards for data readiness, setting up approved prompt libraries, and allocating resources.

While developing Lilli, our team had its mind on scale when it created an open plug-in architecture and setting standards for how APIs should function and be built.  They developed standardized tooling and infrastructure where teams could securely experiment and access a GPT LLM , a gateway with preapproved APIs that teams could access, and a self-serve developer portal. Our goal is that this approach, over time, can help shift “Lilli as a product” (that a handful of teams use to build specific solutions) to “Lilli as a platform” (that teams across the enterprise can access to build other products).

For teams developing gen AI solutions, squad composition will be similar to AI teams but with data engineers and data scientists with gen AI experience and more contributors from risk management, compliance, and legal functions. The general idea of staffing squads with resources that are federated from the different expertise areas will not change, but the skill composition of a gen-AI-intensive squad will.

Set up the technology architecture to scale

Building a gen AI model is often relatively straightforward, but making it fully operational at scale is a different matter entirely. We’ve seen engineers build a basic chatbot in a week, but releasing a stable, accurate, and compliant version that scales can take four months. That’s why, our experience shows, the actual model costs may be less than 10 to 15 percent of the total costs of the solution.

Building for scale doesn’t mean building a new technology architecture. But it does mean focusing on a few core decisions that simplify and speed up processes without breaking the bank. Three such decisions stand out:

  • Focus on reusing your technology. Reusing code can increase the development speed of gen AI use cases by 30 to 50 percent. One good approach is simply creating a source for approved tools, code, and components. A financial-services company, for example, created a library of production-grade tools, which had been approved by both the security and legal teams, and made them available in a library for teams to use. More important is taking the time to identify and build those capabilities that are common across the most priority use cases. The same financial-services company, for example, identified three components that could be reused for more than 100 identified use cases. By building those first, they were able to generate a significant portion of the code base for all the identified use cases—essentially giving every application a big head start.
  • Focus the architecture on enabling efficient connections between gen AI models and internal systems. For gen AI models to work effectively in the shaper archetype, they need access to a business’s data and applications. Advances in integration and orchestration frameworks have significantly reduced the effort required to make those connections. But laying out what those integrations are and how to enable them is critical to ensure these models work efficiently and to avoid the complexity that creates technical debt  (the “tax” a company pays in terms of time and resources needed to redress existing technology issues). Chief information officers and chief technology officers can define reference architectures and integration standards for their organizations. Key elements should include a model hub, which contains trained and approved models that can be provisioned on demand; standard APIs that act as bridges connecting gen AI models to applications or data; and context management and caching, which speed up processing by providing models with relevant information from enterprise data sources.
  • Build up your testing and quality assurance capabilities. Our own experience building Lilli taught us to prioritize testing over development. Our team invested in not only developing testing protocols for each stage of development but also aligning the entire team so that, for example, it was clear who specifically needed to sign off on each stage of the process. This slowed down initial development but sped up the overall delivery pace and quality by cutting back on errors and the time needed to fix mistakes.

Ensure data quality and focus on unstructured data to fuel your models

The ability of a business to generate and scale value from gen AI models will depend on how well it takes advantage of its own data. As with technology, targeted upgrades to existing data architecture  are needed to maximize the future strategic benefits of gen AI:

  • Be targeted in ramping up your data quality and data augmentation efforts. While data quality has always been an important issue, the scale and scope of data that gen AI models can use—especially unstructured data—has made this issue much more consequential. For this reason, it’s critical to get the data foundations right, from clarifying decision rights to defining clear data processes to establishing taxonomies so models can access the data they need. The companies that do this well tie their data quality and augmentation efforts to the specific AI/gen AI application and use case—you don’t need this data foundation to extend to every corner of the enterprise. This could mean, for example, developing a new data repository for all equipment specifications and reported issues to better support maintenance copilot applications.
  • Understand what value is locked into your unstructured data. Most organizations have traditionally focused their data efforts on structured data (values that can be organized in tables, such as prices and features). But the real value from LLMs comes from their ability to work with unstructured data (for example, PowerPoint slides, videos, and text). Companies can map out which unstructured data sources are most valuable and establish metadata tagging standards so models can process the data and teams can find what they need (tagging is particularly important to help companies remove data from models as well, if necessary). Be creative in thinking about data opportunities. Some companies, for example, are interviewing senior employees as they retire and feeding that captured institutional knowledge into an LLM to help improve their copilot performance.
  • Optimize to lower costs at scale. There is often as much as a tenfold difference between what companies pay for data and what they could be paying if they optimized their data infrastructure and underlying costs. This issue often stems from companies scaling their proofs of concept without optimizing their data approach. Two costs generally stand out. One is storage costs arising from companies uploading terabytes of data into the cloud and wanting that data available 24/7. In practice, companies rarely need more than 10 percent of their data to have that level of availability, and accessing the rest over a 24- or 48-hour period is a much cheaper option. The other costs relate to computation with models that require on-call access to thousands of processors to run. This is especially the case when companies are building their own models (the maker archetype) but also when they are using pretrained models and running them with their own data and use cases (the shaper archetype). Companies could take a close look at how they can optimize computation costs on cloud platforms—for instance, putting some models in a queue to run when processors aren’t being used (such as when Americans go to bed and consumption of computing services like Netflix decreases) is a much cheaper option.

Build trust and reusability to drive adoption and scale

Because many people have concerns about gen AI, the bar on explaining how these tools work is much higher than for most solutions. People who use the tools want to know how they work, not just what they do. So it’s important to invest extra time and money to build trust by ensuring model accuracy and making it easy to check answers.

One insurance company, for example, created a gen AI tool to help manage claims. As part of the tool, it listed all the guardrails that had been put in place, and for each answer provided a link to the sentence or page of the relevant policy documents. The company also used an LLM to generate many variations of the same question to ensure answer consistency. These steps, among others, were critical to helping end users build trust in the tool.

Part of the training for maintenance teams using a gen AI tool should be to help them understand the limitations of models and how best to get the right answers. That includes teaching workers strategies to get to the best answer as fast as possible by starting with broad questions then narrowing them down. This provides the model with more context, and it also helps remove any bias of the people who might think they know the answer already. Having model interfaces that look and feel the same as existing tools also helps users feel less pressured to learn something new each time a new application is introduced.

Getting to scale means that businesses will need to stop building one-off solutions that are hard to use for other similar use cases. One global energy and materials company, for example, has established ease of reuse as a key requirement for all gen AI models, and has found in early iterations that 50 to 60 percent of its components can be reused. This means setting standards for developing gen AI assets (for example, prompts and context) that can be easily reused for other cases.

While many of the risk issues relating to gen AI are evolutions of discussions that were already brewing—for instance, data privacy, security, bias risk, job displacement, and intellectual property protection—gen AI has greatly expanded that risk landscape. Just 21 percent of companies reporting AI adoption say they have established policies governing employees’ use of gen AI technologies.

Similarly, a set of tests for AI/gen AI solutions should be established to demonstrate that data privacy, debiasing, and intellectual property protection are respected. Some organizations, in fact, are proposing to release models accompanied with documentation that details their performance characteristics. Documenting your decisions and rationales can be particularly helpful in conversations with regulators.

In some ways, this article is premature—so much is changing that we’ll likely have a profoundly different understanding of gen AI and its capabilities in a year’s time. But the core truths of finding value and driving change will still apply. How well companies have learned those lessons may largely determine how successful they’ll be in capturing that value.

Eric Lamarre

The authors wish to thank Michael Chui, Juan Couto, Ben Ellencweig, Josh Gartner, Bryce Hall, Holger Harreis, Phil Hudelson, Suzana Iacob, Sid Kamath, Neerav Kingsland, Kitti Lakner, Robert Levin, Matej Macak, Lapo Mori, Alex Peluffo, Aldo Rosales, Erik Roth, Abdul Wahab Shaikh, and Stephen Xu for their contributions to this article.

This article was edited by Barr Seitz, an editorial director in the New York office.

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Nike, Inc. Sara Meenan – CIS1055 Section 7 September 27, 2010 . History. The company was founded in 1964, originally called Blue Ribbon Sports by Bill Bowerman and Philip Knight. The first retail store opened in 1966 in Santa Monica, California.

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Nike, Inc Sara Meenan – CIS1055 Section 7 September 27, 2010

History The company was founded in 1964, originally called Blue Ribbon Sports by Bill Bowerman and Philip Knight. The first retail store opened in 1966 in Santa Monica, California. It officially became Nike, Inc. in 1978 after “Nike”, the Greek goddess of victory. The first shoe sold to the public to have the Swoosh design on it was a soccer shoe called “Nike” which was released in 1971. Sara Meenan- Section 7

Top Executives Sara Meenan- Section 7

Products of Nike Inc. Sara Meenan- Section 7

The Key Competitors of Nike Inc. • Puma – Founded in 1924 by Rudolf Dassler • Reebok – Founded in the 1980’s by J.W. Foster • Adidas – Founded in 1924 by Adolf Dassler • Converse- Founded in 1908 by Marquis M. Converse Sara Meenan- Section 7

Interesting Statistics About Nike Inc. Michael Jordan earns about 80 million dollars from Nike a year. IlieNastase, a tennis was the first pro athlete to sign with Nike (1972). Nike has factories in 45 countries around the world. Nike sells products in more than 160 countries. Nike has approximately 34,000 employees worldwide (As of 2009). star Sara Meenan- Section 7

Nike Inc. Stock Trend Over Last 2 Years Sara Meenan- Section 7

Overview of Nike Inc. Web Site Since the Nike company is Global, first select which language and country to shop and get information from. The website breaks down from Nike.com to: NikeStore NiKEiD NikeBasketball NikeFootball NikeGolf Nike+ NikeSoccer NikeSportswear NikeWomen LIVESTRONG Jordan …Etc. Sara Meenan- Section 7

For More Information: Visit: www.nike.com For Nike questions and assistance, contact Nike USA Consumer Services at: 1-800-344-6453 Corporate Mailing Address: NIKE,Inc. One Bowerman DR Beaverton, OR 97005 Sara Meenan- Section 7

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