As 2023 comes to an end, we can look back on a year marked by significant developments in the field of AI. ChatGPT emerged as the fastest-growing consumer application in history, Microsoft unveiled the new Bing and expanded its investment in OpenAI, Elon Musk’s X introduced Grok, among other notable advancements.

Navigating the vast landscape of AI information proved challenging. The dilemma: Should one rely on social media for updates, attend physical events, or depend on peers for insights?

In this article, I will take a deep dive into how you can stay updated with AI in 2024 based on my learnings. So let’s get started.

Social Media

Platforms like Twitter and LinkedIn offer a wealth of AI-related information. While it can be overwhelming to sift through numerous posts, these platforms provide a rapid, albeit superficial, overview of AI advancements. Diving deeper into specific tools and technologies is essential for a more comprehensive understanding.

Following knowledgeable individuals in the field is crucial. I can recommend taking a look at these users (linked to their X profiles):

• Aakash Gupta
• Allie K. Miller
• Harsh Makadia
• Rowan Cheung

Other valuable sources for in-depth insights include here on Medium, LinkedIn, Towards Data Science, TechCrunch, and Reddit where subreddits such as r/ChatGPT illuminate interesting use cases of AI.

Newsletters

Newsletters are an efficient way to receive curated AI updates directly in your inbox, ideal for those constantly on the move. They reduce the effort in seeking out information yourself as the authors themselves have handpicked the AI-related news they find relevant themselves.

Some recommended newsletters include:

• The Rundown by Rowan Cheung
• MIT Sloan Management Review
• The Neuron

Educational Courses

Staying current in AI also involves enhancing your knowledge and skills. Online platforms like Coursera, Udemy, and LinkedIn Learning offer a plethora of courses. Webinars and workshops are also beneficial for direct learning from experts.

Some notable free courses include:

• Generative AI Fundamentals by Google Cloud
• AI for Everyone

Initiating Projects

Embarking on personal projects exposes you to new tools and challenges. For instance, in my projects on optimizing stock market decisions, I discovered FinChat, a detailed financial analytics tool.

When starting projects yourself you will also stumble across specific problems for your specific project. This will allow you to find more niche AI tools that can help you solve problems exactly for your project like I e.g. did with FinChat.

Multimedia Learning

Podcasts, YouTube channels, and LinkedIn courses are excellent for those who prefer audiovisual learning. They cover a range of topics from introductory to advanced.

Podcasts I listen to myself include:

• Ted Tech
• Practical AI: Machine Learning, Data Science
• Towards Data Science
• Lex Fridman Podcast

…and to my Danish readers, I can add the following two to the list:

• TechTopia
• KPMG Tech Talk

Networking

Lastly, reach out to others. Whether it’s friends, colleagues, student peers, family, or anything in between. I actually find it harder to find acquaintances who have not used some kind of AI tools rather than those who have. Reaching out to people and engaging in a conversation about the use of AI tools e.g. might enlighten you and provide you with some insights that you wouldn't have found yourself.

I’ve personally found it especially insightful to engage with people working in industry-specific sectors and learn how they use AI to optimize processes. Different firms in different industries will meet various kinds of challenges that can require the help of AI which can give you some insights that you wouldn't have stumbled across yourself.

2024 will undoubtedly be an interesting year within the field of AI and if the developments continue at the same rapid pace, it’s crucial to stay updated.

Artificial intelligence (AI) is transforming the world of business, creating new opportunities, challenges, and competitive advantages. But how can you become an AI company, and what does it take to succeed in the AI era?

In this article, I will share some insights on how to become an AI company, drawing from insights made by Andrew Ng, one of the leading experts and educators in AI, and build upon some of the points he presents on how to become an AI company.

I hope you will find this article useful and inspiring!

1. Execute pilot projects to gain momentum

You might be tempted to sit down and try to figure out which area of your business would benefit the most from using AI, but that might not be the best way to start. Instead, you should focus on smaller and maybe more simple projects and succeed with those projects. The goal is to gain some momentum and familiarity with AI and to demonstrate the business value of AI to your stakeholders.

It is crucial that your organization sees some concrete results and benefits from AI so that they can get excited and motivated to invest more in AI. By having a few smaller AI projects that succeed others in your organization might also get hands-on experience with AI, thus creating a positive feedback loop in the use of AI in your organization.

You should also be aware of the potential limitations of your organization and the technology. You should not try to solve the biggest and most complex problem you have, because the AI technology might not be mature enough to handle it, or your organization might not have the necessary competencies or resources to execute it.

Thus, start with smaller projects that are feasible and measurable, and show the impact of AI on your business.

2. Build an in-house AI team that can execute further AI projects

Outsourcing the development of AI projects might be beneficial in the short term, but in the long term, you must have a dedicated AI team in your organization. This will help you to keep your projects within the company and potentially create competitive advantages with your projects.

Andrew Ng suggests that you could form a new “AI unit” that works across the existing business units, almost like internal consultants. This AI unit would be responsible for developing and deploying AI solutions for different problems and processes in your organization, and for collaborating and communicating with the other units.

3. Provide broad AI training to employees to foster a company-wide AI-driven mindset

Naturally, not all of your employees will have the necessary skills and knowledge to drive AI projects. They need to understand what AI is, how it works, and how to use it to improve their work and performance.

Thus, AI training is essential to integrate AI into your organization, and to create a culture that embraces AI.

Fortunately, there are many platforms and resources that you can use to provide AI training to your employees, such as Coursera, LinkedIn Learning, YouTube, etc. These platforms are cost-effective and easy to set up, and they offer a variety of courses and topics on AI.

By educating your employees, you will reduce the need for external help over time, and you will also increase the confidence and trust of your employees in AI. They will see AI as a tool that helps them, not as a threat that replaces them.

4. Develop an AI strategy for your business that aligns with your corporate strategy

After you have gained some experience and success with AI, you can start to develop a more comprehensive and coherent AI strategy for your business. This strategy should align with your overall corporate strategy, and reflect your vision and goals.

You should ask yourself: How can we use AI to achieve our objectives? Which processes or functions should we prioritize for AI? How can we create a competitive advantage with AI in our industry?

You should not just “use AI” in a generic sense, because that will not give you any edge over your competitors. You should figure out how you can use AI to gain a competitive advantage for your specific industry sector.

One of the classic strategies derived from AI is the virtuous cycle:

This strategy is used by many companies, such as Uber, Spotify, Instagram, etc. It refers to how you can collect data, and “lock in” your users or customers over time. Specifically, the virtuous cycle means that the more a user uses your product or service, the more data they give your company.

The more data you have, the better your algorithms can become, and the better the algorithm, the better the service, leading to more usage. Think about how tailored your Instagram feed e.g. is.

Following such a strategy also sets some demands on your IT infrastructure. Is your data architecture fragmented and siloed? Or is it consolidated into one data warehouse, making data extraction frictionless?

Building upon Andrew’s steps on how to lead your company into the AI era, I think that it is also crucial to be aware of the potential business benefits of AI may take time. You cannot just implement AI and expect to see results immediately.

Take the virtuous cycle, for example. To gain a competitive advantage with the virtuous cycle, users need to use your product or service for a while, data needs to be stored and processed correctly, and your algorithm needs to be fine-tuned before you can deliver a better service. The benefits are not necessarily measurable the day after you have completed an AI project.

Moreover, I think that it is important to embrace and not be discouraged by failure. Implementing AI into your organization is not easy, and your first few projects might fail, but that should not stop you. Embrace a build-measure-learn and fail-fast mindset to foster growth and learning, and the results will come over time. In the era of AI, it is better to try than not to try at all.

I have previously published different articles about how I, enhanced by generative AI, have written algorithms in Python to optimize decision-making when engaging in investment activities (you can find the articles at the end of this article).

But how do the AI-generated algorithms actually perform? And how can I evaluate their outcome?

As highlighted in the article “How I built a stock recommendation algorithm using ChatGPT — and how you could do it” I present an algorithm that through 4 different technical indicators gives a recommendation that can be either ‘Buy’, ‘Sell’ or ‘Hold’.

But how reliable are these recommendations? One way to test that is to follow stocks with buy signals over time. So, that’s what I’ve done. Let’s take a deep dive into how:

At the beginning of 2023, I executed the algorithm with just 100 stock tickers. I printed the output in a CSV file to examine the data more carefully. This led to the following:

Afterwards, I simply filtered the table to only those with ‘Buy’ signals which resulted in the following 7 stock tickers:

Thus, I had a list of 7 companies that, based on the 4 technical indicators, indicated ‘Buy’ signals. If you’re not familiar with the stock tickers the companies' full names are:

AAPL: Apple

AXP: American Express Company

CCJ: Cameco Cooperation

CRM: Salesforce

DIS: The Walt Disney Company

DKNG: DraftKings Inc.

DOW: Dow Inc.

Then, I simply waited and tracked the different stock prices over time until today (12. October 2023). I decided to create an overview in the data visualization tool Microsoft Power-BI.

But before that, I needed to get data in relation to the company's historical stock prices starting from the day I executed the algorithm (31. January 2023). Thus, I took advantage of the API that Financial Modelling Prep is offering. Financial Modelling Prep offers almost every financial aspect that you can imagine — from financial statements to insider trading. And more relevant in this occasion: Historical data.

I went through their documentation and saw that historical data could be extracted through this endpoint: https://financialmodelingprep.com/api/v3/historical-price-full/{INSERT_STOCK_TICKER}.

Then, I simply made a direct connection from Power-BI to the endpoint from Financial Modelling Prep and made a data model in Power-BI.

I then uploaded the CSV file consisting of the 100 stock tickers that went through the algorithm, and made a one-to-many relationship between the two tables:

Then, I could start experimenting in Power-BI.

My approach was quite simple: To measure the change in price from the start date (when I executed the algorithm) (31–01–2023) until today’s date (12–10–2023).

That led to this overview (It might seem overly complex at first so let’s break it down slowly):

So what do we see in the overview?

Firstly, at the bottom in the left corner, we have a filter that filters out all the companies with buy signals. That results in the list right above it and is essentially the same as the Excel screenshots we saw earlier.

At the top of the Power-BI report, we have some interesting KPIs. These include:

Closing price 31–01–2023: A measure that shows the closing price of each stock at the start date.

Closing price 12–10–2023: A measure that shows the closing price of each stock at the end date.

Difference in USD: Difference between closing price start date and closing price end date in USD.

Difference in %: Difference between closing price start date and closing price end date in percentage.

S&P500 difference in %: Difference between closing price start date and closing price end date in percentage (it shows empty now, but we’ll get back to that later).

In the middle of the report, we have a graph consisting of all the different 7 companies with their stock prices from the start date until the end date.

So, what are the insights?

Well, firstly we can see that the accumulated closing price on the start date was around 698 USD and around 738 USD at the end date. This gives an accumulated growth across all the 7 stocks of 5.71%.

Let’s try to take a look at specific stocks:

In the screenshot above, we take a closer look at DKNG (DraftKings) which has experienced incredible growth since the beginning of the year (96.53%!!!).

This is by far the stock that has seen the highest growth during the time period. Let’s compare to the rest of the stocks:

In the screenshot above we see the closing prices of each stock during the time period, as well as the difference in price in percentage. 4/7 stocks have performed really well, while the story is a bit different for the last 3 as seen in the table.

So, let’s say we invested in the companies when we first executed the algorithm back in January. Let’s imagine we bought 10 stocks in each firm at the closing price of 31–01–2023. This would look as follows:

In this overly simplified example, we can follow how a total investment of 6.980,30 USD made across the 7 companies would have made us a total of 7.379,10 USD which is an increase of 5.71% during 9 months.

It’s not that bad, is it?

So can we just label the AI-powered algorithm to be ‘good’ since it gave us a profit of 5.71%? I wouldn’t say so.

One way to evaluate whether or not the stocks provided by the AI-powered algorithm is to compare their accumulated growth with the market growth during the same time period.

Thus, I have included the S&P 500 representing the market:

As we can see in this picture (and more specifically in the upper right corner) the market (S&P 500 in this example) has actually outperformed the stocks provided by the AI-powered algorithm with a total percentage change of 6.69%.

So let’s imagine we’d invested the same amount (6.980,30 USD) in S&P500 instead. That would look somewhat like the following:

So, we look at a total profit of 466,75 USD compared to 398,80 USD if investing in the S&P 500 during the same time period.

Let’s take a complete look at the fictional investment:

Thus, by investing in the 7 AI-powered stocks, we wouldn’t beat the market. The AI-powered stocks fall short at about a 1% lower growth compared to the market.

Let’s try taking a more in-depth look at how the changes in percentages have looked during the time period:

What we see in the picture above is a visualization of the growth in percentage of the AI-provided stocks (blue graph) compared to the S&P 500 (yellow graph).

The visualization captures how much our fictional investment would have increased/decreased during the time period.

Interestingly, we see that the AI-provided stocks actually follow the trends in the market and actually beat the market quite significantly up until August 2023.

Insights

So what are the insights and key points we can learn from?

• The AI-provided stocks actually gave us a profit, increasing by 5.71% during the time period.
• The AI-provided stocks provided us with 7 companies that I normally wouldn’t consider investing in / didn’t know about (apart from Apple and Salesforce). It’s especially worth highlighting that it provided us with DraftKings whose stock price grew 96.53% during the time period.
• The AI-provided stocks didn’t beat the market during the time period (5.71% vs. 6.69%)

I think that the most important key insight is that the AI-powered algorithm is not a standalone method to use in investment activities. It cannot, and should not, be the only tool you are basing your decision-making upon.

However, I think that it definitely can enhance your decision-making when buying stocks — but you certainly need other tools and your own rational judgment power to accompany it.

Say that we instead of just “investing” in the 7 AI-provided stocks actually did some more research about these companies. How is their financial health? What products are they going to launch? What is the demand for their products?

Conducting more comprehensive research might have led to only picking the companies that actually saw positive growth in their stock prices.

Link to related articles:

• How I built a simple algorithm to analyze a company’s financial health — and how to do it
• How I built a stock recommendation algorithm using ChatGPT – and how you could do it
• How I build a stock price forecasting model using ChatGPT

Do you want to know the secret behind the success of some of the most influential tech companies in the world? It’s called network effects. It’s not just a fancy term; it’s a powerful phenomenon that can make your digital platform more valuable with every new user.

In this article, we will explore what network effects are, how they work, and how you can use them to grow your digital business.

So firstly, let’s dive into what networks effects really are. Network effects are a phenomenon that occurs when the value of a product or service increases as more people use it. Network effects are crucial for the success of many digital platforms and businesses, as they create a positive feedback loop that attracts and retains users, customers, and partners.

Network effects can also create barriers to entry and competitive advantages for the market leaders, as they benefit from economies of scale and network density.

Network effects can be classified into three types: same-side, cross-side, and data-driven. In this article, we will explain each type of network effect with examples and discuss their implications for businesses and consumers.

What are Same-Side Network Effects?

Same-side network effects occur when the value of a product or service increases for all users as more users join the platform. For example, Facebook becomes more valuable for everyone as more people sign up and interact with each other. It’s a virtuous cycle of growth that creates a strong network effect. The more users, the better the experience.

What are Cross-Side Network Effects?

Cross-side network effects happen when the value of a product or service increases for one group of users as more users from another group join the platform. For instance, online marketplaces like eBay or Amazon become more valuable for buyers as more sellers offer their products, and vice versa. It’s a win-win situation that enhances growth and customer satisfaction.

But network effects are not always positive.

What are Negative Network Effects?

Negative network effects occur when the value of a product or service decreases for some or all users as more users join the platform. For example, firms like Uber can suffer from same-side negative network effects if there are too many riders and not enough drivers. The result is a crowded and frustrating user experience.

Cross-side negative network effects can also happen. For example, Facebook can face cross-side negative network effects if there are too many ads (sellers) and not enough users (buyers). The result is a diminished and annoying user experience.

How do Positive Feedback Loops Work?

Positive feedback loops are the mechanisms that drive network effects. The logic is simple: the more users on a platform, the more attractive it becomes for new users. It’s a self-reinforcing process that leads to market domination. In theory, markets tend to favor firms with the largest user base and the strongest network effects.

How can Start-ups Compete with Incumbents?

One of the biggest challenges for start-ups is to compete with incumbents that already have a large user base and strong network effects. The solution is to gain an initial user base, time their entry, and take advantage of emerging technologies that disrupt the market.

How can Incumbents Defend their Position?

One of the biggest threats for incumbents is to lose their user base and network effects to start-ups that offer better products or services. The solution is to improve their product quality, enhance their user interaction, and build trust with their customers.

How does Data Create Network Effects?

In today’s digital age, data itself can create network effects. The more data a firm collects and analyzes, the more valuable its platform becomes for each user. For example, Uber can optimize its surge pricing by using user data. It’s an example of how data-driven network effects can boost performance.

Conclusion: How to Use Network Effects to Grow Your Digital Business

Network effects are the key to digital market domination. Whether you are a start-up trying to challenge the incumbents or an incumbent trying to maintain your leadership, understanding and leveraging network effects is essential for success. In this fast-changing digital world, being adaptable, innovative, and strategic with network effects is your way to win.

Mergers and acquisitions (M&A) are common strategies for firms to grow their business, diversify their offerings, or access new capabilities. But M&A deals are not easy to execute, especially when it comes to integrating the IT systems of the acquirer and the target.

IT integration is the process of aligning, consolidating, or transforming the IT systems, data, and processes of the two firms to support the business objectives and operations of the merged entity.

IT integration can enable the business benefits and synergies from M&A, such as economies of scale, economies of scope, process improvement, or organizational transformation.

But IT integration can also be a major challenge and a source of failure for M&A deals.

According to Bain & Co, over 50% of business synergies are technology-enabled, meaning that the acquirer seeks to profit from the technology that the target has developed or acquired.

Moreover, Accenture reports that among the recent high-value transactions, 80% of companies placed a significant emphasis on technology.

However, many leaders tend to underestimate the importance of IT integration in M&A.

Only 56% of decision-makers took IT issues into consideration during the due diligence phase.

This is a major oversight because the costs and complexities of IT integration can stall operational plans and dramatically increase the timeline of the project.

For example, a bank acquiring a payment company may overlook the fact that their legacy core systems are not technologically compatible with modular, microservices architecture, used by the acquired payment app.

Post-M&A, this would leave the IT team under pressure to re-architect the existing systems, build new data integration, and otherwise technologically reconcile the two different products.

Also, if you lack in-house expertise for such projects, this would create further chaos and delays.

In this article, we’ll go through why IT integration is crucial for M&A success, the main challenges and best practices of IT integration, the four IT integration strategies that you can choose from, and examples of successful IT integration in M&A.

Why IT integration is crucial for M&A success

IT integration is the process of aligning, consolidating, or transforming the IT systems, data, and processes of the acquirer and the target to support the business objectives and operations of the merged entity.

IT integration can enable the business benefits.

However, IT integration is not a one-size-fits-all solution. Firms need to choose the right IT integration strategy that matches their business objectives, IT capabilities, and target characteristics.

There are four main IT integration strategies that firms can choose from:

• IS (information system) absorption: The acquirer’s IS is chosen to be the IS for the merged organisation. As such, the other organisation is migrated entirely onto the chosen IS. This strategy is suitable for firms that have a dominant or superior IS, or that seek to achieve high standardization or consolidation of systems or processes.
• IS co-existence: Almost the opposite to absorption, co-existence aims to keep each company operating on their own IS platforms with minimal standardization of IS functions. This strategy is suitable for firms that have incompatible or diverse IS, or that seek to preserve autonomy or flexibility of systems or processes.
• IS best of breed: One IS platform is the desired goal for best of breed strategy. However, as opposed to picking outright one organisation’s IS over another, the firms select which system best performs each function from the catalogue of the two firms. This strategy is suitable for firms that have complementary or specialized IS, or that seek to achieve optimal performance or functionality of systems or processes.
• IS renewal: In renewal, the IS of the two firms are replaced with a new IS setup specifically for the newly formed, single organisation. This strategy is suitable for firms that have outdated or inadequate IS, or that seek to achieve transformation or innovation of systems or processes.

Each IT integration strategy has its own advantages and disadvantages, and requires different resources and capabilities to implement successfully. Firms need to carefully evaluate their options and select the best IT integration strategy for their M&A deal.

What are the main challenges of IT integration

IT integration is not a trivial task. It involves many technical, organizational, and strategic challenges that can affect the success of M&A. Some of these challenges are:

• High investment costs and risks: IT integration requires significant investments in hardware, software, or human resources, and entails high risks of failure or obsolescence due to technical issues, compatibility problems, or security breaches.
• Reduced focus and flexibility: IT integration may distract firms from their core competencies or sources of differentiation, and make them less flexible or responsive to changing customer needs or market opportunities due to increased complexity or rigidity of systems or processes.
• Increased complexity and coordination costs: IT integration may increase the complexity and coordination costs of managing multiple systems, data sources, or processes across different functions or units due to the need for interoperability, standardization, or alignment.
• Potential conflicts of interest or goals: IT integration may create potential conflicts of interest or goals between different functions or units that have different stakeholder expectations, performance metrics, or incentives.

What are the best practices of IT integration

To overcome the challenges and achieve successful IT integration in M&A, firms need to follow some best practices, such as:

• Define the IT integration strategy and objectives: Firms need to assess their own and their target’s IT capabilities, systems, data, and processes before initiating any deals. They need to identify the strategic objectives and expected benefits of IT integration, such as enhancing performance, creating synergies, or enabling innovation. They also need to choose the appropriate IT integration strategy, such as absorption, co-existence, best of breed, or renewal, depending on the characteristics and fit of the acquirer and the target.
• Establish the IT integration governance and communication structure: Firms need to involve IT executives and experts in the due diligence phase to provide valuable input on the feasibility and practicality of IT integration. They also need to define clear roles and responsibilities for each activity, allocate adequate resources and time for each activity, and establish clear governance and communication mechanisms for each activity. They also need to monitor and control their IT integration activities, using regular checkpoints and feedback loops to track their progress, performance, and quality.
• Use appropriate tools and methods for data and system integration: Firms need to use appropriate tools and methods to migrate, consolidate, or transform their systems, data, and processes. Depending on their IT integration strategy and objectives, they may choose from different data integration methods and strategies, such as manual, application-based, uniform access, common storage, or virtual integration (Talend, n.d.). They may also choose from different system integration approaches and tools, such as continuous practices (e.g., continuous integration, delivery, and deployment), which can reduce build and test time, increase visibility and awareness on build and test results, support (semi-) automated continuous testing, detect violations, flaws and faults in CI, address security and scalability issues in deployment pipeline, or improve dependability and reliability of deployment process.
• Train your teams and end-users on the new or changed systems, data, and processes: Firms need to train their IT staff and end-users on the new or changed systems, data, and processes. They need to provide adequate documentation, guidance, and support to facilitate the adoption and use of the new or changed systems, data, and processes. They also need to solicit feedback and suggestions from their IT staff and end-users.

What are some examples of successful IT integration in M&A?

IT integration can be applied in different industries and sectors, such as IT, technology, and AI. Here are some examples of successful IT integration in M&A:

• Microsoft: Microsoft is a leading example of IT integration in M&A. Microsoft has acquired over 200 companies since its inception, including LinkedIn, Skype, GitHub, and Nuance. By integrating these companies’ IT systems, data, and processes with its own, Microsoft has enhanced its product portfolio, expanded its customer base, accessed new capabilities or resources, and created new business opportunities. Microsoft has used different IT integration strategies depending on the target characteristics and objectives. For example, Microsoft used IS absorption for Skype and LinkedIn, IS co-existence for GitHub, and IS renewal for Nuance.
• Amazon: Amazon is another example of IT integration in M&A. Amazon has acquired over 100 companies since its inception, including Whole Foods, Zappos, Twitch, and Ring. By integrating these companies’ IT systems, data, and processes with its own, Amazon has diversified its product portfolio, increased its operational efficiency, improved its customer satisfaction, and captured more value from customers or suppliers. Amazon has also used different IT integration strategies depending on the target characteristics and objectives. For example, Amazon used IS absorption for Zappos and Twitch, IS co-existence for Whole Foods and Ring, and IS best of breed for Kiva Systems (a robotics company).
• Google: Google is a third example of IT integration in M&A. Google has acquired over 200 companies since its inception, including YouTube, Waze, DeepMind, and Fitbit. By integrating these companies’ IT systems, data, and processes with its own, Google has enhanced its product quality and innovation, increased its market share, accessed new capabilities or resources, and created new business models. Google has also used different IT integration strategies depending on the target characteristics and objectives. For example, Google used IS absorption for YouTube and Waze, IS co-existence for DeepMind and Fitbit, and IS renewal for Motorola Mobility (a smartphone company).

Conclusion

IT integration is a vital component of M&A that can enable the business benefits and synergies from the deal. However, IT integration is also a challenging and complex process that requires careful planning, execution, and evaluation. Firms need to follow some best practices to achieve successful IT integration in M&A. Firms also need to learn from some examples of successful IT integration in M&A in different industries and sectors.

I hope you found this article helpful and informative. If you did, please share it with your network or leave a comment below. I would love to hear your thoughts or feedback on this topic.

Thank you for reading!

What is vertical integration?

Vertical integration is a firm’s ownership and control of multiple vertical stages in the supply of a product. The extent of a firm’s vertical integration is indicated by the number of stages of the industry’s value chain that it spans, and can be measured by the ratio of its value added to sales revenue.

Vertical integration can be either backward (or upstream) into its suppliers’ activities or forward (or downstream) into its customers’ activities. Upstream refers to activities closer to distribution (think making the raw materials) whereas downstream refers to activities closer to the market (think marketing, launching a product to the market). Vertical integration may also be full or partial. VI refers to the change in scope of activities that are under the firm’s control.

Why vertical integration?

One of the main reasons for adopting vertical integration is to reduce transaction costs, which refer to the cost a firm will have to pay another firm for a service. If a firm has many suppliers, they would have high transaction costs.

Vertical integration lowers transaction costs due to the fact that a single firm could produce everything themselves. However, vertical integration may also impose administrative costs instead. Therefore, firms need to compare their internal and external transaction costs and decide whether vertical integration is worth it or not.

Another reason for adopting vertical integration is to cope with uncertainty in supply, demand, technology, and competition. Uncertainty can increase transaction costs and reduce efficiency and quality in production. By integrating vertically, firms can have more control over their inputs and outputs, reduce their dependence on external parties, and increase their responsiveness and adaptability to changing market conditions.

A third reason for adopting vertical integration is to gain competitive advantage by creating differentiation, increasing efficiency, enhancing quality, or capturing more value from customers or suppliers. By integrating vertically, firms can leverage their core competencies, exploit economies of scale or scope, improve coordination and communication across functions or stages, or access valuable resources or capabilities that are otherwise unavailable or costly.

What are the benefits and challenges?

Vertical integration can bring several benefits for firms, such as:

• Improved data quality and availability: By integrating information systems across different levels and stages of production, firms can collect and process more accurate and timely data that can support decision-making and optimization.
• Increased operational efficiency and flexibility: By integrating production processes across different stages or functions, firms can reduce waste, improve coordination, streamline workflows, automate tasks, and adjust production parameters according to demand or product specifications.
• Enhanced product quality and innovation: By integrating product design and development with production processes, firms can ensure consistency between design specifications and actual outcomes, improve feedback loops, enable rapid prototyping and testing, and foster creativity and experimentation.
• Greater customer satisfaction and loyalty: By integrating customer-facing activities with production processes, firms can offer more customized products or services, provide faster delivery or response times, improve after-sales service or support, and build stronger relationships with customers.
• Higher profitability and market share: By integrating value-adding activities across different stages of production or distribution, firms can capture more value from customers or suppliers, reduce costs, increase revenues, or create entry barriers for competitors.
• Gaining more control: By vertically integrating, firms can gain more control over their supply chain. They would ensure that each chain in their business would work towards the same goal, thus avoiding e.g. gaming problems or increased prices from external suppliers.

However, vertical integration also poses some challenges for firms, such as:

• Higher investment costs and risks: By integrating multiple stages or functions of production, firms need to invest more in capital, technology, or human resources, and face higher risks of failure or obsolescence.
• Reduced focus and flexibility: By integrating activities that are not part of their core competencies, firms may lose focus on their main strengths or sources of differentiation, and become less flexible or responsive to changing customer needs or market opportunities.
• Increased complexity and coordination costs: By integrating information systems and production processes across different levels and stages, firms may face increased complexity and coordination costs due to the need for interoperability, standardization, or alignment of data, systems, or processes.
• Potential conflicts of interest or goals: By integrating activities that involve different stakeholders or objectives, firms may encounter potential conflicts of interest or goals between different functions or units, such as between R&D and production, or between production and sales.

What are some examples?

Vertical integration can be applied in different industries and sectors, such as IT, technology, and AI. Here are some examples of successful cases of vertical integration in these domains:

• Apple: Apple is one of the most well-known examples of vertical integration in the IT industry. Apple designs, develops, manufactures, and sells its own hardware and software products, such as the iPhone, iPad, Mac, Apple Watch, AirPods, iOS, macOS, iCloud, and Apple Music. By integrating vertically, Apple can ensure high quality and compatibility across its products, create a unique user experience, differentiate itself from competitors, and capture more value from customers.
• Tesla: Tesla is another example of vertical integration in the technology industry. Tesla designs, manufactures, sells, and services its own electric vehicles, batteries, solar panels, and charging stations. By integrating vertically, Tesla can control the quality and performance of its products, reduce its dependence on suppliers, lower its costs, innovate faster, and offer more value to customers.
• OpenAI: OpenAI is an example of vertical integration in the AI industry. OpenAI is a research organization that aims to create artificial general intelligence (AGI), which is AI that can perform any intellectual task that humans can. OpenAI develops its own AI systems and platforms, such as GPT-3, DALL-E, Codex, and OpenAI Codex. By integrating vertically, OpenAI can pursue its vision of creating AGI that is aligned with human values and can benefit humanity.

Conclusion

Vertical integration can be a key strategy for firms that can bring many benefits to firms in different industries and sectors. However, vertical integration also involves some challenges and trade-offs that firms need to consider carefully before implementing it.

Firms need to assess their internal and external transaction costs, their sources of uncertainty, their competitive advantage, and their goals and objectives when deciding whether to integrate vertically or not. Vertical integration is not a one-size-fits-all solution, but a strategic choice that depends on the context and circumstances of each firm.

I’ve always been told that the best way to decide whether to buy stocks in a certain company was to read annual reports. And that might still be the case.

Reading annual reports might be, for some at least, a tiresome process, and some might just skip right to the pages filled with the financial statements or balance sheets.

So, what if you could build a smarter and more efficient way to extract key information from annual reports?

I’ve previously written articles about how I built a stock recommendation algorithm and a stock price forecasting model using Python with the help of ChatGPT which you can read here:

• How I built a stock recommendation algorithm using ChatGPT – and how you could do it
• How I build a stock price forecasting model using ChatGPT

But now, I thought it was time to try to write a simple algorithm that could help me get insights into a company’s financial health, without the need of reading annual reports.

I decided to write in Python and execute the code in PyCharm.

The first thing needed was to find out how I could extract financial data from companies without the need to e.g. downloading annual reports and uploading these to the algorithm.

So, I did a little research and fell over the page Financial Modelling Prep which holds data about companies’ financial statements. More importantly, they offer an API that can be utilized to automatically extract data from certain companies.

Important to note that the free version of Financial Modelling Prep’s API only contains data about companies listed in the US.

By obtaining the API key, I could slowly begin to write a simple algorithm in Python, getting insights into companies' financial health.

I decided to explore metrics such as liabilities, equity, assets, debt, current ratio, and debt-to-equity ratio to analyze the financial health of companies. These metrics provide a holistic view of a company’s stability, leverage, liquidity, and overall financial well-being.

In the code, the first thing needed is to define your API key and the URL in which information about companies’ financial statements exists:

def financial_health(ticker):
    api_key = "Insert_Your_API_Key_Here"
    url = f"https://financialmodelingprep.com/api/v3/balance-sheet-statement/{ticker}?apikey={api_key}"
    response = requests.get(url)
    data = response.json()[0]

As seen in the code above, the data is stored in JSON format.

The next thing I did was to define which variables and numbers I needed from the balance sheet to calculate the metrics mentioned before:

liabilities = data["totalLiabilities"]
    equity = data["totalEquity"]
    assets = data["totalAssets"]
    debt = data["totalDebt"]
    current_ratio = data["totalCurrentAssets"] / data["totalCurrentLiabilities"]
    debt_to_equity = debt / equity

Then, we can do the simple calculations and add a small text to the output:

    if assets > liabilities:
        print(f"\n- {ticker} has a strong financial position with assets exceeding liabilities.")
    elif assets == liabilities:
        print(f"\n- {ticker} has a balanced financial position with assets equal to liabilities.")
    else:
        print(f"\n- {ticker} has a weak financial position with liabilities exceeding assets.")

    if equity > debt:
        print(f"- {ticker} has a strong equity position with equity exceeding debt.")
    elif equity == debt:
        print(f"- {ticker} has a balanced equity position with equity equal to debt.")
    else:
        print(f"- {ticker} has a weak equity position with debt exceeding equity.")

    if current_ratio > 1:
        print(f"- {ticker} has a strong current position with current assets exceeding current liabilities.")
    elif current_ratio == 1:
        print(f"- {ticker} has a balanced current position with current assets equal to current liabilities.")
    else:
        print(f"- {ticker} has a weak current position with current liabilities exceeding current assets.")

    if debt_to_equity < 1:
        print(f"- {ticker} has a manageable debt level with debt to equity ratio less than 1.")
    else:
        print(f"- {ticker} has a high debt level with debt to equity ratio greater than or equal to 1.")

Last thing needed is now to define which stock you want to analyze:

financial_health("Insert_Stock_Ticker")

By inserting “AAPL” we will get the following output:

Financial Health Analysis for AAPL as of 2022-09-24:

- AAPL has a strong financial position with assets exceeding liabilities.
- AAPL has a weak equity position with debt exceeding equity.
- AAPL has a weak current position with current liabilities exceeding current assets.
- AAPL has a high debt level with debt to equity ratio greater than or equal to 1.

And there you have it! You now have a simple algorithm that can help you enhance your decision-making when buying stocks based on a company’s financial health.

I do not think that this should be an alternative or a standalone analysis in contrast to reading annual reports, but it can however help you to quickly get an overview of a company’s financial health.

Feel free to give any feedback or suggestions to optimize the code, or simply state if you have any wishes on what I should try to build next!

You have the full code here:

import requests


def financial_health(ticker):
    api_key = "Insert_API_Key_Here"
    url = f"https://financialmodelingprep.com/api/v3/balance-sheet-statement/{ticker}?apikey={api_key}"
    response = requests.get(url)
    data = response.json()[0]

    liabilities = data["totalLiabilities"]
    equity = data["totalEquity"]
    assets = data["totalAssets"]
    debt = data["totalDebt"]
    current_ratio = data["totalCurrentAssets"] / data["totalCurrentLiabilities"]
    debt_to_equity = debt / equity

    print(f"Financial Health Analysis for {ticker} as of {data['date']}:")

    if assets > liabilities:
        print(f"\n- {ticker} has a strong financial position with assets exceeding liabilities.")
    elif assets == liabilities:
        print(f"\n- {ticker} has a balanced financial position with assets equal to liabilities.")
    else:
        print(f"\n- {ticker} has a weak financial position with liabilities exceeding assets.")

    if equity > debt:
        print(f"- {ticker} has a strong equity position with equity exceeding debt.")
    elif equity == debt:
        print(f"- {ticker} has a balanced equity position with equity equal to debt.")
    else:
        print(f"- {ticker} has a weak equity position with debt exceeding equity.")

    if current_ratio > 1:
        print(f"- {ticker} has a strong current position with current assets exceeding current liabilities.")
    elif current_ratio == 1:
        print(f"- {ticker} has a balanced current position with current assets equal to current liabilities.")
    else:
        print(f"- {ticker} has a weak current position with current liabilities exceeding current assets.")

    if debt_to_equity < 1:
        print(f"- {ticker} has a manageable debt level with debt to equity ratio less than 1.")
    else:
        print(f"- {ticker} has a high debt level with debt to equity ratio greater than or equal to 1.")


financial_health("Insert_Stock_Ticker")

Deciding whether to buy stocks in either Microsoft, Apple, or maybe both, can be a rather daunting task. The more you start reading about certain companies and listening to people’s opinions on different media might even make you more unsure about what stocks you should buy.

So what should you base your decision-making upon? Reading rumors in the news? Make a technical analysis? Listening to your financial advisor? Or maybe listening to advice from the subreddit /wallstreetbets?

Well, unfortunately, there doesn’t exist one perfect recipe to analyze stocks in relation to getting buy/sell indications. However, I tried asking ChatGPT what type of analysis it would recommend making when deciding whether or not to buy a stock. Here’s what it came up with:

1. Fundamental analysis: Analyze the company’s financial health and performance.
2. Technical Analysis: Use charts and technical indicators to identify trends and potential price movements.
3. Industry Analysis: Analyze the industry and market trends.
4. Management Analysis: Assess the company’s leadership team.
5. Valuation Analysis: Compare the stock price to its intrinsic value.
6. Risk Analysis: Assess potential risks associated with investing in the stock.

While these are excellent points, this article will take a deep dive into how I built a stock recommendation algorithm — and how you could do it. So let’s get into it!

Starting, I have myself tried to take different approaches to analyze stocks, varying from analyzing companies’ financial health to making a stock forecasting model, which I previously have described in this article:

How I build a stock price forecasting model using ChatGPT

However, I didn’t yet try to make an analysis based on technical indicators. So what exactly are technical indicators in stock analysis?

“Technical indicators are tools that help traders and analysts to study the movement and patterns of stock prices based on historical data. They can be used to predict future price movements, identify trends, and generate buy and sell signals.”

And it’s especially the last part from the paragraph above I have focused on, namely “generating buy and sell signals.”

There exist various numbers of technical indicators that you could focus on in relation to generating buy or sell signals, but through a bit of research and advisory from ChatGPT, I chose to focus on 4 different technical indicators:

1. Moving Average: A tool that shows the average price of a stock over a certain period of time, which can help identify whether the stock is trending up or down.
2. RSI (Relative Strength Index): A tool that helps determine whether a stock is overbought (meaning it might be due for a price drop) or oversold (meaning it might be due for a price increase).
3. A/D (Accumulation/Distribution) Lines: A tool that helps determine whether there is more buying or selling pressure on a stock based on the relationship between price and trading volume.
4. ADX (Average Directional Index): A tool that measures the strength of a stock’s trend, which can help identify whether the stock is trending up or down.

So these 4 technical indicators were the ones to go with, and I then tried to see if I, augmented by ChatGPT, could build an algorithm that would analyze a stock based on these indicators, and then give me either a ‘Buy’, ‘Sell’ or ‘Hold’ recommendation.

Thus, the idea was that I, as simply as possible, could insert a stock ticker and then get a recommendation telling me to either buy, sell, or hold a certain stock, based on the 4 technical indicators.

So I simply started to chat with ChatGPT and slowly had a foundation on which I could test the code. I decided to code in python and execute and test the code in PyCharm.

The code extracts the financial data from the ‘yfinance’ (Yahoo Finance) library from a stock ticker that you can insert yourself:

Then, you can specify how long ago you want to download the financial data from:

The code then slowly iterates through the stock ticker you have inserted and calculates the values of the four technical indicators. Afterward, it determines the recommendation based on all the indicators and prints out either ‘Buy’, ‘Sell’, or ‘Recommendation’:

Then, it simply prints out the recommendation in the terminal in PyCharm (or you could simply end the code with a results.to.csv(“/write_your_path_here.csv”) if you would want the output in a csv file instead).

Here is an example output with Microsoft analyzed:

And yes, it’s possible to insert multiple tickers at the beginning of the code. I thought myself that an optimal scenario would be to just extract all the stock tickers from Yahoo Finance, analyze them in relation to the technical indicators, and print out the recommendations in a csv file.

Then, you could have analyzed all the stock tickers in the database from the Yahoo Finance library and have the recommendations in an Excel file, allowing you to simply filter out all those stocks having a ‘Buy’ recommendation.

This would be ideal since you could overcome bias in relation to only analyzing stocks you know of or have heard about before, instead of stocks you might never have heard about before.

However, I wasn’t able to extract stock tickers automatically from the Yahoo Finance library, so what I did instead was finding a database that had all stock tickers and downloaded as many down as I could (they had a paywall if you wanted to download all stock tickers).

Then, I would manually insert all the stock tickers (I think it was about 1000) in the first line of the code, and now I had a full Excel file containing 1000 known and unknown stock ticker symbols, and could easily filter out all those with ‘Buy’ recommendations.

The screenshot below is from the analysis made on February 7th, 2023 filtering out all the ‘Buy’ recommendations:

Anyways, here’s the full code. I would love any feedback, criticism, ideas, or something in between to optimize the code in some way so we can make it better.

Hope you can find it useful!

import yfinance as yf
import pandas as pd

# Define the ticker symbols of the stocks you want to analyze
ticker_symbols = [‘MSFT’]

# Create an empty DataFrame to store the results
results = pd.DataFrame(columns=[‘Ticker’, ‘Moving Average’, ‘RSI’, ‘A/D Lines’, ‘ADX’, ‘Recommendation’])

# Iterate through the ticker symbols
for ticker in ticker_symbols:
    # Download historical data for the current ticker
    data = yf.download(ticker, start=’2000-01-01')

    # Calculate the 200-day moving average
    data[‘MA’] = data[‘Close’].rolling(window=200).mean()

    # Calculate the RSI
    delta = data[‘Close’].diff()
    gain = delta.where(delta > 0, 0)
    loss = -delta.where(delta < 0, 0)
    avg_gain = gain.rolling(window=14).mean()
    avg_loss = loss.rolling(window=14).mean()
    rs = avg_gain / avg_loss
    rsi = 100 - (100 / (1 + rs))

    # Calculate A/D Lines
    data[‘UpMove’] = data[‘High’] - data[‘High’].shift(1)
    data[‘DownMove’] = data[‘Low’].shift(1) - data[‘Low’]
    data[‘UpVolume’] = data[‘UpMove’] * data[‘Volume’]
    data[‘DownVolume’] = data[‘DownMove’] * data[‘Volume’]
    data[‘PosDM’] = data[‘UpMove’]
    data[‘NegDM’] = data[‘DownMove’]
    data.loc[data.UpMove < data.DownMove, ‘PosDM’] = 0
    data.loc[data.UpMove > data.DownMove, ‘NegDM’] = 0
    data[‘PosDI’] = data[‘PosDM’].rolling(window=14).mean()
    data[‘NegDI’] = data[‘NegDM’].rolling(window=14).mean()
    data[‘AD’] = (data[‘PosDI’] - data[‘NegDI’]) / (data[‘PosDI’] + data[‘NegDI’])

    # Calculate ADX
    data[‘ADX’] = 100 * (data[‘PosDI’] - data[‘NegDI’]) / (data[‘PosDI’] + data[‘NegDI’])
    data[‘ADX’] = data[‘ADX’].rolling(window=14).mean()

    # Determine the recommendation
    if data[‘Close’].iloc[-1] > data[‘MA’].iloc[-1] and rsi.iloc[-1] > 70 and data[‘AD’].iloc[-1] > data[‘AD’].iloc[
        -2] and data[‘ADX’].iloc[-1] > 25:
        recommendation = ‘Buy’
    elif data[‘Close’].iloc[-1] < data[‘MA’].iloc[-1] and rsi.iloc[-1] < 30 and data[‘AD’].iloc[-1] < data[‘AD’].iloc[
        -2] and data[‘ADX’].iloc[-1] > 25:
        recommendation = ‘Sell’
    else:
        recommendation = ‘Hold’

    # Append the results to the DataFrame
    results = results.append(
        {‘Ticker’: ticker, ‘Moving Average’: data[‘MA’].iloc[-1], ‘RSI’: rsi.iloc[-1], ‘A/D Lines’: data[‘AD’].iloc[-1],
         ‘ADX’: data[‘ADX’].iloc[-1], ‘Recommendation’: recommendation}, ignore_index=True)

# Print the results
print(results)

…if you’re interested to see got the algorithm performed on the stock market, make sure to read my newest article:

How my AI-powered stock recommendations performed in the past year

The development within the field of AI is growing at a rapid pace and it almost seems like there pop new AI-powered tools up every day.

It can be a bit difficult to keep track of all those new tools, especially to find out whether or not they could benefit you, how they could benefit you, and how to implement them in your everyday life.

So if you’re curious about taking advantage of new AI-powered tools to optimize your productivity, read along!

1. Resolve AI

Let’s start with what might be my favorite, Resolve AI. Have you ever thought about building a chatbot for your business, or maybe even for your own personal project? Then look no further! Resolve AI is here to help you.

“Supercharge your business with ChatGPT powered AI Assistants”

Resolve AI functions by using ChatGPT as its “motor” and all you need to do to make a chatbot for your own business is by feeding it data. You should simply just create a knowledge base that includes writing/inserting one or more documents to their platform which the chatbot then will learn from! These documents could be anything from your company’s FAQ, guidelines, or any other qualitative data basically.

Then, Resolve AI will ask you what the chatbot should look like, and what welcome messages (questions like “Hey there!” or “Ask me anything about x!”) you would like your chatbot to have. It’s here where you can tailor your chatbot to your business and purpose.

Resolve AI then gives you a script that can be integrated with your website, Facebook Messenger or any other platform you would want your chatbot to be. It’s that easy!

And that’s it! You now have your own chatbot. Check Resolve AI and their solution out on their website.

2. ChatWebPage

Have you ever wished you could chat with any webpage and ask it questions? Well, now you can with ChatWebPage, an AI tool that lets you interact with any website using natural language. ChatWebPage uses ChatGPT to understand your queries and generate responses based on the content of the webpage.

You can use ChatWebPage to get information, learn new things, or just have fun. All you need to do is enter a URL and start chatting. Check out ChatWebPage and see what you can discover.

3. STORYD

Whether you’re studying or working full-time, creating presentations will most likely always follow you throughout your career. For some, creating presentations might be a task they just want to get over with and it may even be tasks that you give to students/junior consultants in your firm, so you can focus on areas you find more interesting and meaningful.

“STORYD helps you create data presentations leaders love, in seconds”

Whether you’re seeking inspiration, or simply want a presentation done quickly, STORYD might just be the right tool for you. By simply describing the topic of your presentation, STORYD will create the rest.

STORYD will then create a script that includes the content of the slides, titles, etc., and allows you to edit it, and add either more text or graphs into the presentation, thus tailoring the presentation for your purpose.

Learn more about STORYD here!

4. Magic Eraser

It’s always annoying when you have found or taken an almost perfect picture — but then notices something that you would just wish wasn’t in the frame. Introducing Magic Eraser.

If you want to remove unwanted things from your photos in seconds, you should try Magic Eraser. Magic Eraser is an AI-powered tool that lets you erase any object or area from your image with a simple click. You don’t need any editing skills or software to use Magic Eraser.

You just have to upload your image, mark the bit you need to be removed, and download the result. Magic Eraser will automatically recreate the background and blend it seamlessly with the rest of the image. You can use Magic Eraser to remove distractions, enhance your property photos, or create stunning compositions.

Magic Eraser is free to use and no signup is required. Check out Magic Eraser and see what you can do with it.

5. Scholarcy

This AI-powered tool is especially handy for students struggling to get through many research articles. Scholarcy helps you create flashcards based on the research articles you upload to it and highlights things such as key concepts, abstracts, comparative analysis, methods, and much more!

Another use case of Scholarcy is that you can download it as a chrome extension and then create presentations based on the research articles you upload to it! This can be handy if you have to make a presentation about a certain topic and want help to create one, or it could also be handy just to have a presentation containing the key findings from the articles, which can help you to quickly get an overview about the most important aspects from the article.

You can add the chrome extension here or read more about Scholarcy on their website.

6. jenni

“Supercharge your writing with the most advanced AI writing assistant.”

jenni is your way to enhance your writing through AI. jenni gives you an indication of whether your title e.g. is weak/strong based on certain parameters (kinda similar to getting feedback on your password in relation to whether or not it’s strong). After writing a title, jenni will generate a suitable sentence to start your writing!

Moreover, you can highlight some of the paragraphs you have written, and ask jenni to e.g. simplify the text, shorten it, rephrase it more academically, or elaborate on it by adding more in-depth text. Check it out here!

7. Transcribe

During the last 5 years when I attended uni, several of my projects included qualitative research. Especially, this included interviewing practitioners from organizations we wrote our projects with. While that was indeed interesting, transcribing the interviews was a bit the opposite.

Transcribe is an AI-powered tool that transcribes your recordings in minutes. You can use Transcribe for any type of content, such as interviews, podcasts, lectures, speeches, meetings, and more. Transcribe supports more than 80 languages and dialects, and can handle multiple speakers and accents.

You can also edit and format your transcript using Transcribe’s online editor. You can export your transcript as doc, txt, or subtitle files. Transcribe is privacy-focused and does not store your audio or transcript data. Transcribe is free to use for up to 15 minutes of transcription per month, and you can sign up for a subscription to get more features. Check it out here!

8. Perplexity

I think we all know the power of both ChatGPT and Bing AI now. However, from my experience, most users access these tools from their computers. Introducing perplexity, a mobile application built on GPT but with a connection to the internet. Oh, and it supports voice search.

As with ChatGPT, you can ask it anything. It will store your different conversations in a conversation thread, so you can always go back to see your chat history. It’s incredibly good for research, since it also provides you with references, just like Bing AI.

You can download perplexity for iPhone here.

9. genei

If you want to research faster and smarter with AI, you should try genei. genei is an AI-powered summarisation and research tool that helps you read, analyze, and write better. You can use genei for any type of content, such as articles, reports, books, and web pages. genei can automatically summarise, paraphrase, rephrase, and cite any document or group of documents.

You can also ask questions and get answers from genei’s AI. genei helps you save time, find insights, and improve your understanding of any topic. genei is free to use for up to 10 articles per month, and you can sign up for a subscription to get more features. Check it out here!

10. Glass AI

The last one could be a game-changer for clinicians. Glass AI is a platform that allows you to enter a diagnostic problem representation below to generate either a DDx (differential diagnosis) or a clinical plan.

For example, you can enter something like “A 45-year-old male with chest pain, dyspnea, and diaphoresis” and Glass AI will generate a list of possible diagnoses or a plan of action.

Glass AI uses an AI model that has been trained on millions of clinical cases and medical literature to generate the DDx or the clinical plan. It analyzes the diagnostic problem representation and compares it with similar cases and evidence-based guidelines. It then provides you with the most likely or relevant diagnoses or actions, along with their probabilities, explanations, and references.

Glass health AI can help you learn and practice medicine more efficiently and effectively by providing you with instant feedback and guidance. It can also help you improve your clinical reasoning skills, broaden your differential diagnosis and avoid cognitive biases. However, it’s important to notice that it’s still in beta version. Check it out here.

2023, and March alone, has seen some incredible development in the field of AI. There are an immense amount of AI tools that you today could take advance of and optimize your productivity.

Are you ready to master these tools?

Information technology (IT) is not just a tool for supporting business processes, but a strategic driver of business value. IT can enable organizations to run more efficiently, innovate faster, and deliver better outcomes for their customers and stakeholders. However, demonstrating the business value of IT is not always easy, as it requires a clear alignment between IT and business goals, a focus on outcomes rather than outputs, and a compelling narrative that resonates with key decision-makers.

In this article, we will explore some of the challenges and best practices for communicating the business value of IT.

IT investments cannot stand alone in businesses, thus there exist several factors that complicate the investments made in IT and their potential value creation which we will take a deep dive into:

1. Strategic investment in IT

The first insight for demonstrating the strategic role of IT is to ensure that your IT investments are aligned with your business strategy and your resource base. Not all IT investments are equally valuable or relevant for achieving competitive advantage. Some may be essential for running your business, but not for growing or transforming it. Some may be common among your competitors, but not unique or distinctive for your firm. Some may be easy to copy or substitute, but not difficult or costly to imitate or acquire.

To assess whether an IT investment is strategic, you can use the VRIN framework, which stands for value, rarity, inimitability, and non-substitutability. These are the four criteria that determine whether an IT investment can create and sustain competitive advantage for your firm.

By applying the VRIN framework, you can identify and prioritize those IT investments that can help you exploit opportunities or neutralize threats in the environment, leverage or enhance your existing resources and capabilities, and differentiate yourself from your competitors.

2. IT does not create value in isolation

The second insight for demonstrating the strategic role of IT is to ensure that your IT investments are integrated with your resource base. Your resource base is the set of all the assets, capabilities, processes, information, and knowledge that you control and can use to improve your performance. Your IT investments should not be seen as standalone projects but as part of a larger system that supports and enhances your resource base.

According to the resource-based view, IT creates value when it meets two conditions: (a) it is consistent with your resource base, meaning that it fits well with your existing resources and capabilities and does not create conflicts or redundancies and (b) it matches your resources, meaning that it complements or supplements your existing resources and capabilities and does not create gaps or weaknesses.

If these two conditions are not met, your IT investments may fail to deliver value or even destroy value for your firm. Therefore, you should always evaluate how well your IT investments align with your resource base and address any misalignments or mismatches. You should also consider how to leverage or improve your existing and previously failed IT investments, rather than discarding them or ignoring them.

3. IT-based value manifests itself in several ways

IT can create value in various ways that go beyond monetary returns. Managers should not have unrealistic expectations when investing in IT but rather consider the different types of value that IT can offer to the organization and its stakeholders.

For example, IT can help improve (1) internal value: such as enhancing process efficiency, reducing errors, and increasing product quality and innovation, and (2) external value: such as strengthening supply chain collaboration, improving customer satisfaction and loyalty, and creating competitive advantage.

4. IT-based value manifests itself across multiple levels

IT can create value at different levels of the organization, depending on how it is used and integrated. IT may not benefit every unit equally, but only some departments that use it more effectively. For example, IT can help improve the efficiency and quality of operations, such as production, logistics, and accounting. IT can also help enhance the innovation and creativity of functions, such as marketing, research, and development. IT can also help support the coordination and communication of activities, such as planning, decision-making, and problem-solving.

Specific investments made in IT in your organization might thus benefit only a few units that get direct value made from the investment. This could e.g. include a sales department that uses AI technology to find leads, which mainly creates value for that department.

5. Technology is not a competitive advantage in its own right

Technology alone does not give you a competitive edge. Technology does not guarantee value creation, it depends on how you use it. Technology is a tool that can enable or enhance your capabilities, but it is not a substitute for your strategy, skills, or resources. Technology can help you achieve your goals, but it cannot define them for you.

Technology is non-deterministic implying that it does not guarantee (determine) how valuable the investment will be for the firm.

6. IT based value can be latent

IT-based value can take time to emerge. Sometimes, the value of IT is not immediately visible or measurable, but it becomes apparent later on. For example, IT can help you build capabilities, relationships, or reputation that will pay off in the future.

Another example of latent business value derived from IT relates to how using AI technology in a firm might not create value immediately, but rather over time. AI technology needs to learn from data before it can create immense value in your business, thus making the value creation latent.

There’s no doubt about how IT can help any organization grow and make them more profitable. But it is, however, crucial for managers to invest in the right, meaning strategic, technology that aligns with their existing resource base, and be aware of some of the confounding factors that might complicate the business value creation the investment potentially will have.