Artificial Intelligence and the Machine Learning Revolution in Finance: Cogent Labs and the Google Cloud Platform (GCP) SWOT Analysis / TOWS Matrix / Weighted SWOT Analysis
Innovation & Entrepreneurship
Strategy / MBA Resources
Case Study SWOT Analysis Solution
Case Study Description of Artificial Intelligence and the Machine Learning Revolution in Finance: Cogent Labs and the Google Cloud Platform (GCP)
This case examines the intersection of two firms (Cogent Labs-a machine learning software firm in Tokyo; and Google, the technology infrastructure giant) attempting to exploit the benefits of artificial intelligence and machine learning in the financial services sector. The case protagonist, David Malkin, known as the "AI Architect" at Cogent Labs, must decide how best to position his firm for growth. Malkin knew that artificial intelligence had great potential to revolutionize several aspects of the financial services industry, but he also knew that artificial intelligence's greatest achievements to date were in very narrow functions. Malkin further knew that large, sophisticated financial service clients owned a vast array of proprietary datasets that were impossible to replicate. Meanwhile the major "cloud" providers like Google, Amazon, and Microsoft had large-scale computing infrastructures and multi-billion-dollar research and development budgets with which they could (and did) generate innovative artificial intelligence software of their own. Malkin wondered how a small software firm like Cogent Labs without its own proprietary datasets, or a large-scale computing infrastructure, or a multi-billion R&D budget could fit in? Would Cogent Labs' current approach of developing their own proprietary machine learning applications to run on the cloud and sell directly to financial services firms in Tokyo prove to be a sustainable model? Or would Cogent Labs ultimately need to partner/merge with one of the major cloud providers in order to provide the expertise necessary to customize their offerings for financial services clients? Or, was the future even more uncertain; would software firms like Cogent eventually need to create and own new datasets of their own, and build their own infrastructures to host their own new data, in order to avoid being disintermediated in the future if (and when) machine learning expertise became truly commoditized?
Authors :: Lauren H. Cohen, Christopher Malloy, William Powley
Swot Analysis of "Artificial Intelligence and the Machine Learning Revolution in Finance: Cogent Labs and the Google Cloud Platform (GCP)" written by Lauren H. Cohen, Christopher Malloy, William Powley includes – strengths weakness that are internal strategic factors of the organization, and opportunities and threats that Cogent Labs facing as an external strategic factors. Some of the topics covered in Artificial Intelligence and the Machine Learning Revolution in Finance: Cogent Labs and the Google Cloud Platform (GCP) case study are - Strategic Management Strategies, Financial management and Innovation & Entrepreneurship.
Some of the macro environment factors that can be used to understand the Artificial Intelligence and the Machine Learning Revolution in Finance: Cogent Labs and the Google Cloud Platform (GCP) casestudy better are - – increasing household debt because of falling income levels, there is increasing trade war between United States & China, wage bills are increasing, customer relationship management is fast transforming because of increasing concerns over data privacy, increasing energy prices, there is backlash against globalization, supply chains are disrupted by pandemic ,
banking and financial system is disrupted by Bitcoin and other crypto currencies, increasing government debt because of Covid-19 spendings, etc
Introduction to SWOT Analysis of Artificial Intelligence and the Machine Learning Revolution in Finance: Cogent Labs and the Google Cloud Platform (GCP)
SWOT stands for an organization’s Strengths, Weaknesses, Opportunities and Threats . At Oak Spring University , we believe that protagonist in Artificial Intelligence and the Machine Learning Revolution in Finance: Cogent Labs and the Google Cloud Platform (GCP) case study can use SWOT analysis as a strategic management tool to assess the current internal strengths and weaknesses of the Cogent Labs, and to figure out the opportunities and threats in the macro environment – technological, environmental, political, economic, social, demographic, etc in which Cogent Labs 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 Artificial Intelligence and the Machine Learning Revolution in Finance: Cogent Labs and the Google Cloud Platform (GCP) can be done for the following purposes –
1. Strategic planning using facts provided in Artificial Intelligence and the Machine Learning Revolution in Finance: Cogent Labs and the Google Cloud Platform (GCP) case study
2. Improving business portfolio management of Cogent Labs
3. Assessing feasibility of the new initiative in Innovation & Entrepreneurship field.
4. Making a Innovation & Entrepreneurship topic specific business decision
5. Set goals for the organization
6. Organizational restructuring of Cogent Labs
Strengths Artificial Intelligence and the Machine Learning Revolution in Finance: Cogent Labs and the Google Cloud Platform (GCP) | Internal Strategic Factors
What are Strengths in SWOT Analysis / TOWS Matrix / Weighted SWOT Analysis
The strengths of Cogent Labs in Artificial Intelligence and the Machine Learning Revolution in Finance: Cogent Labs and the Google Cloud Platform (GCP) Harvard Business Review case study are -
Sustainable margins compare to other players in Innovation & Entrepreneurship industry
– Artificial Intelligence and the Machine Learning Revolution in Finance: Cogent Labs and the Google Cloud Platform (GCP) firm has clearly differentiated products in the market place. This has enabled Cogent Labs to fetch slight price premium compare to the competitors in the Innovation & Entrepreneurship industry. The sustainable margins have also helped Cogent Labs to invest into research and development (R&D) and innovation.
Strong track record of project management
– Cogent Labs is known for sticking to its project targets. This enables the firm to manage – time, project costs, and have sustainable margins on the projects.
Learning organization
- Cogent Labs is a learning organization. It has inculcated three key characters of learning organization in its processes and operations – exploration, creativity, and expansiveness. The work place at Cogent Labs is open place that encourages instructiveness, ideation, open minded discussions, and creativity. Employees and leaders in Artificial Intelligence and the Machine Learning Revolution in Finance: Cogent Labs and the Google Cloud Platform (GCP) Harvard Business Review case study emphasize – knowledge, initiative, and innovation.
Successful track record of launching new products
– Cogent Labs has launched numerous new products in last few years, keeping in mind evolving customer preferences and competitive pressures. Cogent Labs has effective processes in place that helps in exploring new product needs, doing quick pilot testing, and then launching the products quickly using its extensive distribution network.
Organizational Resilience of Cogent Labs
– The covid-19 pandemic has put organizational resilience at the centre of everthing that Cogent Labs does. Organizational resilience comprises - Financial Resilience, Operational Resilience, Technological Resilience, Organizational Resilience, Business Model Resilience, and Reputation Resilience.
Analytics focus
– Cogent Labs is putting a lot of focus on utilizing the power of analytics in business decision making. This has put it among the leading players in the industry. The technology infrastructure suggested by Lauren H. Cohen, Christopher Malloy, William Powley can also help it to harness the power of analytics for – marketing optimization, demand forecasting, customer relationship management, inventory management, information sharing across the value chain etc.
Digital Transformation in Innovation & Entrepreneurship segment
- digital transformation varies from industry to industry. For Cogent Labs digital transformation journey comprises differing goals based on market maturity, customer technology acceptance, and organizational culture. Cogent Labs has successfully integrated the four key components of digital transformation – digital integration in processes, digital integration in marketing and customer relationship management, digital integration into the value chain, and using technology to explore new products and market opportunities.
Highly skilled collaborators
– Cogent Labs has highly efficient outsourcing and offshoring strategy. It has resulted in greater operational flexibility and bringing down the costs in highly price sensitive segment. Secondly the value chain collaborators of the firm in Artificial Intelligence and the Machine Learning Revolution in Finance: Cogent Labs and the Google Cloud Platform (GCP) HBR case study have helped the firm to develop new products and bring them quickly to the marketplace.
High switching costs
– The high switching costs that Cogent Labs 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.
Training and development
– Cogent Labs has one of the best training and development program in the industry. The effectiveness of the training programs can be measured in Artificial Intelligence and the Machine Learning Revolution in Finance: Cogent Labs and the Google Cloud Platform (GCP) Harvard Business Review case study by analyzing – employees retention, in-house promotion, loyalty, new venture initiation, lack of conflict, and high level of both employees and customer engagement.
High brand equity
– Cogent Labs has strong brand awareness and brand recognition among both - the exiting customers and potential new customers. Strong brand equity has enabled Cogent Labs to keep acquiring new customers and building profitable relationship with both the new and loyal customers.
Low bargaining power of suppliers
– Suppliers of Cogent Labs in the sector have low bargaining power. Artificial Intelligence and the Machine Learning Revolution in Finance: Cogent Labs and the Google Cloud Platform (GCP) has further diversified its suppliers portfolio by building a robust supply chain across various countries. This helps Cogent Labs to manage not only supply disruptions but also source products at highly competitive prices.
Weaknesses Artificial Intelligence and the Machine Learning Revolution in Finance: Cogent Labs and the Google Cloud Platform (GCP) | Internal Strategic Factors
What are Weaknesses in SWOT Analysis / TOWS Matrix / Weighted SWOT Analysis
The weaknesses of Artificial Intelligence and the Machine Learning Revolution in Finance: Cogent Labs and the Google Cloud Platform (GCP) are -
High dependence on star products
– The top 2 products and services of the firm as mentioned in the Artificial Intelligence and the Machine Learning Revolution in Finance: Cogent Labs and the Google Cloud Platform (GCP) 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 Cogent Labs has relatively successful track record of launching new products.
Workers concerns about automation
– As automation is fast increasing in the segment, Cogent Labs 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.
Slow decision making process
– As mentioned earlier in the report, Cogent Labs has a very deliberative decision making approach. This approach has resulted in prudent decisions, but it has also resulted in missing opportunities in the industry over the last five years. Cogent Labs even though has strong showing on digital transformation primary two stages, it has struggled to capitalize the power of digital transformation in marketing efforts and new venture efforts.
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 Cogent Labs supply chain. Even after few cautionary changes mentioned in the HBR case study - Artificial Intelligence and the Machine Learning Revolution in Finance: Cogent Labs and the Google Cloud Platform (GCP), it is still heavily dependent upon the existing supply chain. The existing supply chain though brings in cost efficiencies but it has left Cogent Labs vulnerable to further global disruptions in South East Asia.
Skills based hiring
– The stress on hiring functional specialists at Cogent Labs has created an environment where the organization is dominated by functional specialists rather than management generalist. This has resulted into product oriented approach rather than marketing oriented approach or consumers oriented approach.
No frontier risks strategy
– After analyzing the HBR case study Artificial Intelligence and the Machine Learning Revolution in Finance: Cogent Labs and the Google Cloud Platform (GCP), it seems that company is thinking about the frontier risks that can impact Innovation & Entrepreneurship strategy. But it has very little resources allocation to manage the risks emerging from events such as natural disasters, climate change, melting of permafrost, tacking the rise of artificial intelligence, opportunities and threats emerging from commercialization of space etc.
Interest costs
– Compare to the competition, Cogent Labs 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.
Aligning sales with marketing
– It come across in the case study Artificial Intelligence and the Machine Learning Revolution in Finance: Cogent Labs and the Google Cloud Platform (GCP) that the firm needs to have more collaboration between its sales team and marketing team. Sales professionals in the industry have deep experience in developing customer relationships. Marketing department in the case Artificial Intelligence and the Machine Learning Revolution in Finance: Cogent Labs and the Google Cloud Platform (GCP) can leverage the sales team experience to cultivate customer relationships as Cogent Labs is planning to shift buying processes online.
Capital Spending Reduction
– Even during the low interest decade, Cogent Labs 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.
Compensation and incentives
– The revenue per employee as mentioned in the HBR case study Artificial Intelligence and the Machine Learning Revolution in Finance: Cogent Labs and the Google Cloud Platform (GCP), is just above the industry average. Cogent Labs 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.
Products dominated business model
– Even though Cogent Labs has some of the most successful products in the industry, this business model has made each new product launch extremely critical for continuous financial growth of the organization. firm in the HBR case study - Artificial Intelligence and the Machine Learning Revolution in Finance: Cogent Labs and the Google Cloud Platform (GCP) should strive to include more intangible value offerings along with its core products and services.
Opportunities Artificial Intelligence and the Machine Learning Revolution in Finance: Cogent Labs and the Google Cloud Platform (GCP) | 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 Artificial Intelligence and the Machine Learning Revolution in Finance: Cogent Labs and the Google Cloud Platform (GCP) are -
Manufacturing automation
– Cogent Labs can use the latest technology developments to improve its manufacturing and designing process in Innovation & Entrepreneurship 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.
Loyalty marketing
– Cogent Labs has focused on building a highly responsive customer relationship management platform. This platform is built on in-house data and driven by analytics and artificial intelligence. The customer analytics can help the organization to fine tune its loyalty marketing efforts, increase the wallet share of the organization, reduce wastage on mainstream advertising spending, build better pricing strategies using personalization, etc.
Creating value in data economy
– The success of analytics program of Cogent Labs has opened avenues for new revenue streams for the organization in the industry. This can help Cogent Labs to build a more holistic ecosystem as suggested in the Artificial Intelligence and the Machine Learning Revolution in Finance: Cogent Labs and the Google Cloud Platform (GCP) case study. Cogent Labs can build new products and services such as - data insight services, data privacy related products, data based consulting services, etc.
Using analytics as competitive advantage
– Cogent Labs 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 Artificial Intelligence and the Machine Learning Revolution in Finance: Cogent Labs and the Google Cloud Platform (GCP) - to build a competitive advantage using analytics. The analytics driven competitive advantage can help Cogent Labs to build faster Go To Market strategies, better consumer insights, developing relevant product features, and building a highly efficient supply chain.
Lowering marketing communication costs
– 5G expansion will open new opportunities for Cogent Labs in the field of marketing communication. It will bring down the cost of doing business, provide technology platform to build new products in the Innovation & Entrepreneurship segment, and it will provide faster access to the consumers.
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 Cogent Labs 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.
Building a culture of innovation
– managers at Cogent Labs can make experimentation a productive activity and build a culture of innovation using approaches such as – mining transaction data, A/B testing of websites and selling platforms, engaging potential customers over various needs, and building on small ideas in the Innovation & Entrepreneurship segment.
Changes in consumer behavior post Covid-19
– Consumer behavior has changed in the Innovation & Entrepreneurship industry because of Covid-19 restrictions. Some of this behavior will stay once things get back to normal. Cogent Labs can take advantage of these changes in consumer behavior to build a far more efficient business model. For example consumer regular ordering of products can reduce both last mile delivery costs and market penetration costs. Cogent Labs can further use this consumer data to build better customer loyalty, provide better products and service collection, and improve the value proposition in inflationary times.
Better consumer reach
– The expansion of the 5G network will help Cogent Labs to increase its market reach. Cogent Labs will be able to reach out to new customers. Secondly 5G will also provide technology framework to build new tools and products that can help more immersive consumer experience and faster consumer journey.
Buying journey improvements
– Cogent Labs can improve the customer journey of consumers in the industry by using analytics and artificial intelligence. Artificial Intelligence and the Machine Learning Revolution in Finance: Cogent Labs and the Google Cloud Platform (GCP) 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.
Redefining models of collaboration and team work
– As explained in the weaknesses section, Cogent Labs is facing challenges because of the dominance of functional experts in the organization. Artificial Intelligence and the Machine Learning Revolution in Finance: Cogent Labs and the Google Cloud Platform (GCP) case study suggests that firm can utilize new technology to build more coordinated teams and streamline operations and communications using tools such as CAD, Zoom, etc.
Reforming the budgeting process
- By establishing new metrics that will be used to evaluate both existing and potential projects Cogent Labs can not only reduce the costs of the project but also help it in integrating the projects with other processes within the organization.
Remote work and new talent hiring opportunities
– The widespread usage of remote working technologies during Covid-19 has opened opportunities for Cogent Labs 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 Cogent Labs to hire the very best people irrespective of their geographical location.
Threats Artificial Intelligence and the Machine Learning Revolution in Finance: Cogent Labs and the Google Cloud Platform (GCP) External Strategic Factors
What are Threats in the SWOT Analysis / TOWS Matrix / Weighted SWOT Analysis
The threats mentioned in the HBR case study Artificial Intelligence and the Machine Learning Revolution in Finance: Cogent Labs and the Google Cloud Platform (GCP) are -
Stagnating economy with rate increase
– Cogent Labs can face lack of demand in the market place because of Fed actions to reduce inflation. This can lead to sluggish growth in the economy, lower demands, lower investments, higher borrowing costs, and consolidation in the field.
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.
High dependence on third party suppliers
– Cogent Labs 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.
Trade war between China and United States
– The trade war between two of the biggest economies can hugely impact the opportunities for Cogent Labs in the Innovation & Entrepreneurship industry. The Innovation & Entrepreneurship 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.
Regulatory challenges
– Cogent Labs 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 Innovation & Entrepreneurship industry regulations.
Shortening product life cycle
– it is one of the major threat that Cogent Labs is facing in Innovation & Entrepreneurship sector. It can lead to higher research and development costs, higher marketing expenses, lower customer loyalty, etc.
Increasing international competition and downward pressure on margins
– Apart from technology driven competitive advantage dilution, Cogent Labs can face downward pressure on margins from increasing competition from international players. The international players have stable revenue in their home market and can use those resources to penetrate prominent markets illustrated in HBR case study Artificial Intelligence and the Machine Learning Revolution in Finance: Cogent Labs and the Google Cloud Platform (GCP) .
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 Cogent Labs business can come under increasing regulations regarding data privacy, data security, etc.
Consumer confidence and its impact on Cogent Labs 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.
Instability in the European markets
– European Union markets are facing three big challenges post Covid – expanded balance sheets, Brexit related business disruption, and aggressive Russia looking to distract the existing security mechanism. Cogent Labs will face different problems in different parts of Europe. For example it will face inflationary pressures in UK, France, and Germany, balance sheet expansion and demand challenges in Southern European countries, and geopolitical instability in the Eastern Europe.
Learning curve for new practices
– As the technology based on artificial intelligence and machine learning platform is getting complex, as highlighted in case study Artificial Intelligence and the Machine Learning Revolution in Finance: Cogent Labs and the Google Cloud Platform (GCP), Cogent Labs may face longer learning curve for training and development of existing employees. This can open space for more nimble competitors in the field of Innovation & Entrepreneurship .
Easy access to finance
– Easy access to finance in Innovation & Entrepreneurship field will also reduce the barriers to entry in the industry, thus putting downward pressure on the prices because of increasing competition. Cogent Labs can utilize it by borrowing at lower rates and invest it into research and development, capital expenditure to fortify its core competitive advantage.
Technology disruption because of hacks, piracy etc
– The colonial pipeline illustrated, how vulnerable modern organization are to international hackers, miscreants, and disruptors. The cyber security interruption, data leaks, etc can seriously jeopardize the future growth of the organization.
Weighted SWOT Analysis of Artificial Intelligence and the Machine Learning Revolution in Finance: Cogent Labs and the Google Cloud Platform (GCP) 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 Artificial Intelligence and the Machine Learning Revolution in Finance: Cogent Labs and the Google Cloud Platform (GCP) 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 Artificial Intelligence and the Machine Learning Revolution in Finance: Cogent Labs and the Google Cloud Platform (GCP) 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 Artificial Intelligence and the Machine Learning Revolution in Finance: Cogent Labs and the Google Cloud Platform (GCP) 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 Artificial Intelligence and the Machine Learning Revolution in Finance: Cogent Labs and the Google Cloud Platform (GCP) 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 Cogent Labs needs to make to build a sustainable competitive advantage.
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