AI in Business Planning and Forecasting

GENERAL OVERVIEW

The creation of a successful start-up is largely dependent on the interplay amongst ideas (service or products), capital, people and market (Aulet, 2013). A start-up is mainly a business that has just been newly formed based on a novel idea with an ambition of exponential growth in its early years. Stakeholders have been able to earn income from numerous revenue streams because of the modern financial realm. A prime example is a mortgage-backed security whereby investors are able to invest in different revenue stream tranches that matched their tolerance of risks.

As a result of this advanced financial complexity, top investors are forced to rely on edge cutting computer software to help them in calculating the potential benefits of different options in real time (Muñoz-Bullon et al, 2015). One technology that can be a central tool of the decision-making process is cloud computing as it facilitates; 1. Event probability calculation that is even more profitable, 2. The faster crunching of numbers, and 3. More agile value assignment for different mezzanine ownership stakes (Hashem et al, 2015). Modern software programs are able to provide analytics, colour-coded graphs and charts. These templates can be overlaid so as to gain an even better visual understanding. This study seeks to develop an algorithm that can help start-ups and entrepreneurs to make the best financial decisions and also assist CEOs for strategic financial decision makings at top levels using AI technology and Big Data.

Making decisions is one of the key roles of managers. Artificial Intelligence`s power lies in its abilities of reducing prediction costs and as such providing managers with even better certainty, a commodity that is not invaluable especially when there exists a general consensus today that uncertainty and volatility are some of the forces that are dominant in the business climate. As such, AI`s potential of unlocking the secrets in the ever-increasing amounts of data that are collected has been helping in transforming an important and particular part of the business of prediction –planning and forecasting.

AUTOMATION AND INTEGRATION OF ALGORITHMS

In computer science and even mathematics, an algorithm includes a specification that is ambiguous of the procedure to be followed in solving problems. Algorithms are able to perform automated reasoning tasks, processing of data and calculations (Ahuja, 2017). As a means that is effective, it is possible to define algorithms within an amount of time and space that is finite. Beginning from an initial state and initial input, the instructions are able to provide a description of a computation which proceeds through a number of well-defined successive states which are finite when executed, producing an output eventually at a final ending stage. Automation of business processes involves the technology-enabled automation of business processes that are of a complex nature (Pinedo, 2016).

An important role in the success of a firm is played by the linkage of technology strategy and corporate business strategy (Herrera, 2015). Technology assessment, technology forecasting and the planning of products are integrated by road mapping. A practical instrument for long and middle range technology development and corporate business strategy formulation is provided by integrated technology road mapping through the aligning of social marketing factors and external and internal resources. Through the use of technology roadmaps, it is possible to identify fresh opportunities for business, validate internal knowledge and communicate ideas, improve a technology and develop technology strategies that are effective (Haddad and Maldonado, 2015).

Effective road mapping leads to developing of portfolios, which creates an opportunity for division-level project evaluation and companywide assessment of technology (Aleina et al, 2016). Algorithms are able to define actions. They are the pieces of software which are very good at actions that are specific, in a way that is much better than human beings. As such the more organizations adopt algorithms, the more people will lose their jobs going forward. More than 35% of the current jobs in the UK are actually at a risk as a result of computerization (Kitchin, 2017). The UK is not alone in this, the research, almost 47% of the jobs in the US are at a risk of being lost as a result of algorithmic business in the coming decades (Arntz, Gregory and Zierahn, 2016).

LITERATURE REVIEW

Gomez Uribe and Hunt (2016) hold the view that through automation, a business can be streamlined for simplicity, helped to achieve digital transformation, increase the quality of services, contain costs and even improve on the delivery of services. Business automation consists of the integration of applications, restructuring of labour resources and the use of software applications throughout an organization. In developing a good technology, the first step would be to clearly define the product and its goals. This can be achieved by creating a document that specifies all of the details of a product including features, target retail price, size, functions and even the name of the technology. According to Tarapore et al., (2016), during this phase, it should be kept in mind that the development of a product is a balancing act between making compromises and pushing new limits. Just like with life, it is not possible to have everything.

For example, during the development of tech products that are wearable, sizes that are small may be the developer’s highest priority which implies that other specifications like battery life and performance can be compromised. The compromise that is most common is usually in regards to cost and it is in big corporations and start-ups that this give and take is most common.

The decision by Steve Jobs to eliminate dedicated keyboards on portable devices was one of the more famous product compromises. Even though at that time some designers were of the view that having a physical keyboard was important, Jobs insisted that a larger screen size and appearance were more important as compared to a real keyboard.

According to Gillespie (2014), disruptions all over the world have been happening in the business world, thanks to algorithms. Uber, which is the largest taxi company in the world has no taxis of its own, but through the adoption of smart algorithms, the company is able to connect passengers to taxis (Shapiro, 2018). WhatsApp which is the largest telephone company in the world owns no telephone infrastructure of its own, however, through the adoption of algorithms, the company sends over 35 billion messages a day in day out (Kalra, 2018). Alibaba, which is one of the leading retailers in the world has no inventory of its own, however, through the use of algorithms, the company is able to help others sell products (Jia et al., 2018). Companies like these are a clear indication of how entire industries can be disrupted by algorithms.

Pal and Wang (2017), posit that with the advancements in technology, more and more data has been observed to be generated by consumers and organizations. Dozens of petabytes are created and stored by some organizations like Walmart (Marr, 2017). However, the collection and storage of massive amounts are really not enough for gaining a competitive advantage. As such, in a bid to beat the competition, organizations need to do more and not just analyse data.

Saurwein and Latzer (2015) posit that too much dependency on technology can leave one grasping at straws. It is prudent to do careful planning as to which data to accumulate, how to go about with organizing the data and what it means. Interpretation of said data is one of the most crucial steps of the financial decision-making process. Good decision-making skills and good leadership cannot be replaced by technology, however, these attributes can be complemented by technology. According to Gong et al (2015), technology has the ability to make the financial decision-making process even smoother.

The enhanced application of algorithms has been happening at a staggering pace. Organizations like Associated Press are already using algorithms in writing financial reports at rates of up to 2000 stories every minute. The Venture Capital Firm is another example in the financial world. An algorithm was appointed into this firm’s board of management in 2014 which gets to vote on whether an investment in a particular company is to be made or not. The VC fund has a special focus on age-related diseases and life sciences and the algorithm which is known as VITAL is able to do an analysis of data from different sources for example financial details, clinical trials, intellectual property and previous funding rounds.

RESEARCH QUESTIONS

1. What are the potential opportunities and threats for adopting the algorithms in making financial decisions for a business?

2. What are the costs involved in the designing and adoption of a new algorithm?

3. Is the use of algorithms in decision making healthy?

METHODOLOGY

For this study, different algorithms will be developed, observed for any bugs and the most suitable algorithm adopted so as to help entrepreneurs and start-ups in the making of financial decisions.

In developing an algorithm, there are four major steps that are followed and as such, the timing of this project will be centred on those four activities. These steps include designing, analysing, implementing and experimenting (van Aarle et al., 2015). Design involves the identification of the problem and understanding it thoroughly. Once you have identified the problem, what follows is analysing the efficiency of the code in the solving of the problem. The design of algorithms is subject to individual plans and is very fluid. After analysis, what follows is writing and coding of the algorithm. It is always important to write the algorithm in the coding language that you understand best. Experimentation which is the final step usually involves experimenting with different variables in the algorithm. This is achieved by trying and entering data that will make the algorithm fail or even try and rewrite the code so as to work it out in an even more efficient manner.

EXPECTED RESULTS

At the end of the study, it is expected that a tool with the capabilities of making sound financial decisions will be developed.

TIMESCALE

REFERENCES

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