Case Study Description of Leadership Forum: Machine Learning 101
In this excerpt from the University of Toronto's Conference on Machine Learning and The Market for Intelligence, senior leaders from Uber, Bloomberg Beta, Stanford and AME Capital discuss how firms are embracing machine learning and artificial intelligence to either disrupt others or avoid being disrupted. They provide a cross-section of viewpoints on strategies to navigate what is becoming an increasingly disruptive economy.
Authors :: Mike Del Balso, Nick Adams, Shivon Zilis, Jerry Kaplan
Swot Analysis of "Leadership Forum: Machine Learning 101" written by Mike Del Balso, Nick Adams, Shivon Zilis, Jerry Kaplan includes – strengths weakness that are internal strategic factors of the organization, and opportunities and threats that Machine Learning facing as an external strategic factors. Some of the topics covered in Leadership Forum: Machine Learning 101 case study are - Strategic Management Strategies, Technology and Strategy & Execution.
Some of the macro environment factors that can be used to understand the Leadership Forum: Machine Learning 101 casestudy better are - – cloud computing is disrupting traditional business models, increasing government debt because of Covid-19 spendings, central banks are concerned over increasing inflation, competitive advantages are harder to sustain because of technology dispersion, increasing household debt because of falling income levels, supply chains are disrupted by pandemic , technology disruption,
there is increasing trade war between United States & China, wage bills are increasing, etc
Introduction to SWOT Analysis of Leadership Forum: Machine Learning 101
SWOT stands for an organization’s Strengths, Weaknesses, Opportunities and Threats . At Oak Spring University , we believe that protagonist in Leadership Forum: Machine Learning 101 case study can use SWOT analysis as a strategic management tool to assess the current internal strengths and weaknesses of the Machine Learning, and to figure out the opportunities and threats in the macro environment – technological, environmental, political, economic, social, demographic, etc in which Machine Learning operates in.
According to Harvard Business Review, 75% of the managers use SWOT analysis for various purposes such as – evaluating current scenario, strategic planning, new venture feasibility, personal growth goals, new market entry, Go To market strategies, portfolio management and strategic trade-off assessment, organizational restructuring, etc.
SWOT Objectives / Importance of SWOT Analysis and SWOT Matrix
SWOT analysis of Leadership Forum: Machine Learning 101 can be done for the following purposes –
1. Strategic planning using facts provided in Leadership Forum: Machine Learning 101 case study
2. Improving business portfolio management of Machine Learning
3. Assessing feasibility of the new initiative in Strategy & Execution field.
4. Making a Strategy & Execution topic specific business decision
5. Set goals for the organization
6. Organizational restructuring of Machine Learning
Strengths Leadership Forum: Machine Learning 101 | Internal Strategic Factors
What are Strengths in SWOT Analysis / TOWS Matrix / Weighted SWOT Analysis
The strengths of Machine Learning in Leadership Forum: Machine Learning 101 Harvard Business Review case study are -
Low bargaining power of suppliers
– Suppliers of Machine Learning in the sector have low bargaining power. Leadership Forum: Machine Learning 101 has further diversified its suppliers portfolio by building a robust supply chain across various countries. This helps Machine Learning to manage not only supply disruptions but also source products at highly competitive prices.
Cross disciplinary teams
– Horizontal connected teams at the Machine Learning are driving operational speed, building greater agility, and keeping the organization nimble to compete with new competitors. It helps are organization to ideate new ideas, and execute them swiftly in the marketplace.
High switching costs
– The high switching costs that Machine Learning has built up over years in its products and services combo offer has resulted in high retention of customers, lower marketing costs, and greater ability of the firm to focus on its customers.
Ability to recruit top talent
– Machine Learning is one of the leading recruiters in the industry. Managers in the Leadership Forum: Machine Learning 101 are in a position to attract the best talent available. The firm has a robust talent identification program that helps in identifying the brightest.
Operational resilience
– The operational resilience strategy in the Leadership Forum: Machine Learning 101 Harvard Business Review case study comprises – understanding the underlying the factors in the industry, building diversified operations across different geographies so that disruption in one part of the world doesn’t impact the overall performance of the firm, and integrating the various business operations and processes through its digital transformation drive.
Organizational Resilience of Machine Learning
– The covid-19 pandemic has put organizational resilience at the centre of everthing that Machine Learning does. Organizational resilience comprises - Financial Resilience, Operational Resilience, Technological Resilience, Organizational Resilience, Business Model Resilience, and Reputation Resilience.
Ability to lead change in Strategy & Execution field
– Machine Learning is one of the leading players in its industry. Over the years it has not only transformed the business landscape in its segment but also across the whole industry. The ability to lead change has enabled Machine Learning in – penetrating new markets, reaching out to new customers, and providing different value propositions to different customers in the international markets.
Sustainable margins compare to other players in Strategy & Execution industry
– Leadership Forum: Machine Learning 101 firm has clearly differentiated products in the market place. This has enabled Machine Learning to fetch slight price premium compare to the competitors in the Strategy & Execution industry. The sustainable margins have also helped Machine Learning to invest into research and development (R&D) and innovation.
Diverse revenue streams
– Machine Learning is present in almost all the verticals within the industry. This has provided firm in Leadership Forum: Machine Learning 101 case study a diverse revenue stream that has helped it to survive disruptions such as global pandemic in Covid-19, financial disruption of 2008, and supply chain disruption of 2021.
Superior customer experience
– The customer experience strategy of Machine Learning in the segment is based on four key concepts – personalization, simplification of complex needs, prompt response, and continuous engagement.
Innovation driven organization
– Machine Learning is one of the most innovative firm in sector. Manager in Leadership Forum: Machine Learning 101 Harvard Business Review case study can use Clayton Christensen Disruptive Innovation strategies to further increase the scale of innovtions in the organization.
High brand equity
– Machine Learning has strong brand awareness and brand recognition among both - the exiting customers and potential new customers. Strong brand equity has enabled Machine Learning to keep acquiring new customers and building profitable relationship with both the new and loyal customers.
Weaknesses Leadership Forum: Machine Learning 101 | Internal Strategic Factors
What are Weaknesses in SWOT Analysis / TOWS Matrix / Weighted SWOT Analysis
The weaknesses of Leadership Forum: Machine Learning 101 are -
Slow to strategic competitive environment developments
– As Leadership Forum: Machine Learning 101 HBR case study mentions - Machine Learning takes time to assess the upcoming competitions. This has led to missing out on atleast 2-3 big opportunities in the industry in last five years.
Capital Spending Reduction
– Even during the low interest decade, Machine Learning has not been able to do capital spending to the tune of the competition. This has resulted into fewer innovations and company facing stiff competition from both existing competitors and new entrants who are disrupting the industry using digital technology.
High bargaining power of channel partners
– Because of the regulatory requirements, Mike Del Balso, Nick Adams, Shivon Zilis, Jerry Kaplan suggests that, Machine Learning is facing high bargaining power of the channel partners. So far it has not able to streamline the operations to reduce the bargaining power of the value chain partners in the industry.
Interest costs
– Compare to the competition, Machine Learning has borrowed money from the capital market at higher rates. It needs to restructure the interest payment and costs so that it can compete better and improve profitability.
Workers concerns about automation
– As automation is fast increasing in the segment, Machine Learning needs to come up with a strategy to reduce the workers concern regarding automation. Without a clear strategy, it could lead to disruption and uncertainty within the organization.
Compensation and incentives
– The revenue per employee as mentioned in the HBR case study Leadership Forum: Machine Learning 101, is just above the industry average. Machine Learning needs to redesign the compensation structure and incentives to increase the revenue per employees. Some of the steps that it can take are – hiring more specialists on project basis, etc.
High dependence on star products
– The top 2 products and services of the firm as mentioned in the Leadership Forum: Machine Learning 101 HBR case study still accounts for major business revenue. This dependence on star products in has resulted into insufficient focus on developing new products, even though Machine Learning has relatively successful track record of launching new products.
High dependence on existing supply chain
– The disruption in the global supply chains because of the Covid-19 pandemic and blockage of the Suez Canal illustrated the fragile nature of Machine Learning supply chain. Even after few cautionary changes mentioned in the HBR case study - Leadership Forum: Machine Learning 101, it is still heavily dependent upon the existing supply chain. The existing supply chain though brings in cost efficiencies but it has left Machine Learning vulnerable to further global disruptions in South East Asia.
Need for greater diversity
– Machine Learning has taken concrete steps on diversity, equity, and inclusion. But the efforts so far has resulted in limited success. It needs to expand the recruitment and selection process to hire more people from the minorities and underprivileged background.
Employees’ incomplete understanding of strategy
– From the instances in the HBR case study Leadership Forum: Machine Learning 101, it seems that the employees of Machine Learning don’t have comprehensive understanding of the firm’s strategy. This is reflected in number of promotional campaigns over the last few years that had mixed messaging and competing priorities. Some of the strategic activities and services promoted in the promotional campaigns were not consistent with the organization’s strategy.
Ability to respond to the competition
– As the decision making is very deliberative, highlighted in the case study Leadership Forum: Machine Learning 101, in the dynamic environment Machine Learning has struggled to respond to the nimble upstart competition. Machine Learning has reasonably good record with similar level competitors but it has struggled with new entrants taking away niches of its business.
Opportunities Leadership Forum: Machine Learning 101 | External Strategic Factors
What are Opportunities in the SWOT Analysis / TOWS Matrix / Weighted SWOT Analysis
The opportunities highlighted in the Harvard Business Review case study Leadership Forum: Machine Learning 101 are -
Use of Bitcoin and other crypto currencies for transactions
– The popularity of Bitcoin and other crypto currencies as asset class and medium of transaction has opened new opportunities for Machine Learning in the consumer business. Now Machine Learning can target international markets with far fewer capital restrictions requirements than the existing system.
Buying journey improvements
– Machine Learning can improve the customer journey of consumers in the industry by using analytics and artificial intelligence. Leadership Forum: Machine Learning 101 suggest that firm can provide automated chats to help consumers solve their own problems, provide online suggestions to get maximum out of the products and services, and help consumers to build a community where they can interact with each other to develop new features and uses.
Reconfiguring business model
– The expansion of digital payment system, the bringing down of international transactions costs using Bitcoin and other blockchain based currencies, etc can help Machine Learning to reconfigure its entire business model. For example it can used blockchain based technologies to reduce piracy of its products in the big markets such as China. Secondly it can use the popularity of e-commerce in various developing markets to build a Direct to Customer business model rather than the current Channel Heavy distribution network.
Low interest rates
– Even though inflation is raising its head in most developed economies, Machine Learning can still utilize the low interest rates to borrow money for capital investment. Secondly it can also use the increase of government spending in infrastructure projects to get new business.
Identify volunteer opportunities
– Covid-19 has impacted working population in two ways – it has led to people soul searching about their professional choices, resulting in mass resignation. Secondly it has encouraged people to do things that they are passionate about. This has opened opportunities for businesses to build volunteer oriented socially driven projects. Machine Learning can explore opportunities that can attract volunteers and are consistent with its mission and vision.
Lowering marketing communication costs
– 5G expansion will open new opportunities for Machine Learning in the field of marketing communication. It will bring down the cost of doing business, provide technology platform to build new products in the Strategy & Execution segment, and it will provide faster access to the consumers.
Increase in government spending
– As the United States and other governments are increasing social spending and infrastructure spending to build economies post Covid-19, Machine Learning can use these opportunities to build new business models that can help the communities that Machine Learning operates in. Secondly it can use opportunities from government spending in Strategy & Execution sector.
Reforming the budgeting process
- By establishing new metrics that will be used to evaluate both existing and potential projects Machine Learning can not only reduce the costs of the project but also help it in integrating the projects with other processes within the organization.
Leveraging digital technologies
– Machine Learning can leverage digital technologies such as artificial intelligence and machine learning to automate the production process, customer analytics to get better insights into consumer behavior, realtime digital dashboards to get better sales tracking, logistics and transportation, product tracking, etc.
Remote work and new talent hiring opportunities
– The widespread usage of remote working technologies during Covid-19 has opened opportunities for Machine Learning to expand its talent hiring zone. According to McKinsey Global Institute, 20% of the high end workforce in fields such as finance, information technology, can continously work from remote local post Covid-19. This presents a really great opportunity for Machine Learning to hire the very best people irrespective of their geographical location.
Manufacturing automation
– Machine Learning can use the latest technology developments to improve its manufacturing and designing process in Strategy & Execution segment. It can use CAD and 3D printing to build a quick prototype and pilot testing products. It can leverage automation using machine learning and artificial intelligence to do faster production at lowers costs, and it can leverage the growth in satellite and tracking technologies to improve inventory management, transportation, and shipping.
Using analytics as competitive advantage
– Machine Learning has spent a significant amount of money and effort to integrate analytics and machine learning into its operations in the sector. This continuous investment in analytics has enabled, as illustrated in the Harvard case study Leadership Forum: Machine Learning 101 - to build a competitive advantage using analytics. The analytics driven competitive advantage can help Machine Learning to build faster Go To Market strategies, better consumer insights, developing relevant product features, and building a highly efficient supply chain.
Harnessing reconfiguration of the global supply chains
– As the trade war between US and China heats up in the coming years, Machine Learning can build a diversified supply chain model across various countries in - South East Asia, India, and other parts of the world. This reconfiguration of global supply chain can help, as suggested in case study, Leadership Forum: Machine Learning 101, to buy more products closer to the markets, and it can leverage its size and influence to get better deal from the local markets.
Threats Leadership Forum: Machine Learning 101 External Strategic Factors
What are Threats in the SWOT Analysis / TOWS Matrix / Weighted SWOT Analysis
The threats mentioned in the HBR case study Leadership Forum: Machine Learning 101 are -
Environmental challenges
– Machine Learning needs to have a robust strategy against the disruptions arising from climate change and energy requirements. EU has identified it as key priority area and spending 30% of its 880 billion Euros European post Covid-19 recovery funds on green technology. Machine Learning can take advantage of this fund but it will also bring new competitors in the Strategy & Execution industry.
High level of anxiety and lack of motivation
– the Great Resignation in United States is the sign of broader dissatisfaction among the workforce in United States. Machine Learning needs to understand the core reasons impacting the Strategy & Execution industry. This will help it in building a better workplace.
Technology acceleration in Forth Industrial Revolution
– Machine Learning has witnessed rapid integration of technology during Covid-19 in the Strategy & Execution industry. As one of the leading players in the industry, Machine Learning needs to keep up with the evolution of technology in the Strategy & Execution sector. According to Mckinsey study top managers believe that the adoption of technology in operations, communications is 20-25 times faster than what they planned in the beginning of 2019.
Learning curve for new practices
– As the technology based on artificial intelligence and machine learning platform is getting complex, as highlighted in case study Leadership Forum: Machine Learning 101, Machine Learning may face longer learning curve for training and development of existing employees. This can open space for more nimble competitors in the field of Strategy & Execution .
Increasing wage structure of Machine Learning
– Post Covid-19 there is a sharp increase in the wages especially in the jobs that require interaction with people. The increasing wages can put downward pressure on the margins of Machine Learning.
Aging population
– As the populations of most advanced economies are aging, it will lead to high social security costs, higher savings among population, and lower demand for goods and services in the economy. The household savings in US, France, UK, Germany, and Japan are growing faster than predicted because of uncertainty caused by pandemic.
Barriers of entry lowering
– As technology is more democratized, the barriers to entry in the industry are lowering. It can presents Machine Learning with greater competitive threats in the near to medium future. Secondly it will also put downward pressure on pricing throughout the sector.
Trade war between China and United States
– The trade war between two of the biggest economies can hugely impact the opportunities for Machine Learning in the Strategy & Execution industry. The Strategy & Execution industry is already at various protected from local competition in China, with the rise of trade war the protection levels may go up. This presents a clear threat of current business model in Chinese market.
Consumer confidence and its impact on Machine Learning demand
– There is a high probability of declining consumer confidence, given – high inflammation rate, rise of gig economy, lower job stability, increasing cost of living, higher interest rates, and aging demography. All the factors contribute to people saving higher rate of their income, resulting in lower consumer demand in the industry and other sectors.
Easy access to finance
– Easy access to finance in Strategy & Execution field will also reduce the barriers to entry in the industry, thus putting downward pressure on the prices because of increasing competition. Machine Learning can utilize it by borrowing at lower rates and invest it into research and development, capital expenditure to fortify its core competitive advantage.
High dependence on third party suppliers
– Machine Learning high dependence on third party suppliers can disrupt its processes and delivery mechanism. For example -the current troubles of car makers because of chip shortage is because the chip companies started producing chips for electronic companies rather than car manufacturers.
Regulatory challenges
– Machine Learning needs to prepare for regulatory challenges as consumer protection groups and other pressure groups are vigorously advocating for more regulations on big business - to reduce inequality, to create a level playing field, to product data privacy and consumer privacy, to reduce the influence of big money on democratic institutions, etc. This can lead to significant changes in the Strategy & Execution industry regulations.
Backlash against dominant players
– US Congress and other legislative arms of the government are getting tough on big business especially technology companies. The digital arm of Machine Learning business can come under increasing regulations regarding data privacy, data security, etc.
Weighted SWOT Analysis of Leadership Forum: Machine Learning 101 Template, Example
Not all factors mentioned under the Strengths, Weakness, Opportunities, and Threats quadrants in the SWOT Analysis are equal. Managers in the HBR case study Leadership Forum: Machine Learning 101 needs to zero down on the relative importance of each factor mentioned in the Strengths, Weakness, Opportunities, and Threats quadrants.
We can provide the relative importance to each factor by assigning relative weights. Weighted SWOT analysis process is a three stage process –
First stage for doing weighted SWOT analysis of the case study Leadership Forum: Machine Learning 101 is to rank the strengths and weaknesses of the organization. This will help you to assess the most important strengths and weaknesses of the firm and which one of the strengths and weaknesses mentioned in the initial lists are marginal and can be left out.
Second stage for conducting weighted SWOT analysis of the Harvard case study Leadership Forum: Machine Learning 101 is to give probabilities to the external strategic factors thus better understanding the opportunities and threats arising out of macro environment changes and developments.
Third stage of constructing weighted SWOT analysis of Leadership Forum: Machine Learning 101 is to provide strategic recommendations includes – joining likelihood of external strategic factors such as opportunities and threats to the internal strategic factors – strengths and weaknesses. You should start with external factors as they will provide the direction of the overall industry. Secondly by joining probabilities with internal strategic factors can help the company not only strategic fit but also the most probably strategic trade-off that Machine Learning needs to make to build a sustainable competitive advantage.