As an entrepreneur, understanding the strengths and weaknesses of your business is crucial to its success. However, keeping on top of your business’s position can be difficult in ever-evolving markets. This is where environmental scanning comes in.
In this article, we explore environmental scanning more in-depth and discuss how artificial intelligence can enhance the practice. Keep reading to learn more.
What Is Environmental Scanning?
Environmental scanning is the process of identifying and analyzing external and internal factors–or “events”–that could impact your business either positively or negatively. This involves gathering data about such events and reviewing them using a scanning model like SWOT or PEST.
From this process, your businesses can better understand the relationship between internal and external factors and the event itself. For example, how would a new store selling similar products impact your business if it were to open on the same street?
Environmental Scanning Methods
As mentioned above, there are several modes of environmental scanning methods that you can employ, depending on your needs and whether you’re reviewing internal or external events.
A Strengths, Weaknesses, Opportunities, and Threats (SWOT) analysis is a fundamental model of environmental scanning that handles both internal and external events. This model allows you to size your business up against external threats, identify opportunities, and understand your weak points competitors could target.
Below is an example of a SWOT analysis from the perspective of an independent clothing producer who operates from their home:
PEST, which stands for Political, Environmental, Social, and Technological, is another common model of environmental scanning. However, unlike SWOT which enables businesses to review both internal and external factors, PEST analyses are more concerned with external factors. For instance, you might use a PEST analysis to identify how new government regulation or technological advancements could affect your business.
By this token, here’s an example of a PEST analysis from the perspective of an AI startup:
Focused on external business factors, an Environmental Threat and Opportunity Profile (ETOP) is often used to inform a SWOT analysis. While this model cannot be broken down into four, digestible quadrants like SWOT or PEST, ETOP analyses can be used to analyze any relevant external factors, such as business weaknesses and political events.
Here is an example of an ETOP analysis for a petrol car manufacturing company:
|Social Attitudes||As environmental concern grows, so too does demand for electric vehicles, reducing demand for petrol cars||Start producing electric vehicles or hybrid cars|
|Government||Possibility of car emissions bill, as various countries are aiming to ban non-EV by 2030||2030 is enough time to refocus our long-term business plan|
|Suppliers||Many parts are no longer available due to material shortages, meaning some cars can no longer be maintained||Increase the price of pre-owned cars when re-selling|
Benefits of Environmental Scanning
So, why might you, as a business owner, want to employ environmental scanning in your business? It is, after all, considered an essential part of running any business. Here are a few top benefits of environmental scanning.
To improve your business’s weakness, you must first identify its issues. Environmental scanning allows you to consider internal weaknesses, such as problems within management structure, as well as external weaknesses like market positioning. By considering your additional findings from environmental scanning in terms of threats and opportunities, your business can work toward strengthening its weaknesses through a more holistic approach.
Find New Opportunities
By regularly conducting environmental scanning analyses, you are able to find new and more fruitful opportunities that may have otherwise been overlooked. What’s more, you’re better positioned to identify whether or not opportunities are suited toward your business.
For example, maybe a new office block opens up in your local area and you can afford to move in; however, after scanning your environment, you may realize it requires you to take out new, costly permits. In this instance, you might reject this opportunity, clearly understanding and weighing the pros, cons, and overall impact on your business.
Better Understand Competitors
Environmental scanning enables you to better understand your competitors by identifying their strengths, weaknesses, and potential opportunities they may have over your business. As a result, you are better poised to out-position your competitors by staying ahead of them, playing to your strengths, and exploiting their weaknesses.
How AI Is Used in Environmental Scanning
While environmental scanning techniques are an effective, tried and tested way of analyzing your business by themselves, the process can be taken to the next level with artificial intelligence.Here are a few ways in which AI is being used to enhance environmental scanning.
Environmental scanning requires an extensive amount of data for best results, which can be cumbersome for a human to analyze. However, AI technologies like machine learning excel at revealing patterns within vast, complex data. Algorithms can rapidly parse volumes of unstructured text, survey responses, and news trends to identify trends and essential information.
Natural language processing specifically lets systems extract meanings and relationships from text data. This enables AI to autonomously organize and interpret insights from social media posts, competitor intel, customer feedback, and other narrative sources.
Environmental scanning involves many repetitive administrative tasks, including data collection, cleaning, and processing, that can be handled by AI in order to free up staff. For example, intelligent scrapers can pull data from web sources, while bots can clean inconsistencies and normalize formats, enabling employees to focus on higher-level analysis and planning.
The speed and tireless nature of AI automation ultimately allows more data to be processed with fewer people more quickly. Additionally, fewer manual processes means less potential for human error.
With AI’s ability to rapidly process expansive, multifaceted data, environmental scanning becomes more holistic and actionable. Advanced analytics techniques uncover correlations and patterns otherwise missed within massive datasets by humans. The correlations and conclusions derived from AI insights can also provide additional context that empowers more informed and strategic decisions.
What’s more, statistical learning algorithms can ingest and cross-reference data sources to find non-obvious relationships. This leads to better opportunity predicting, as more data sources can be considered.
Technology Behind AI-Driven Environmental Scanning
When it comes to environmental scanning, exploring the technology behind the AI-driven software is key in order to fully understand its powerful capabilities.
Large Language Models
Large language models (LLMs) like GPT-3 are trained on massive text-based datasets that allow them to understand and generate human-like language. By analyzing relationships between content within enormous datasets, LLMs can write complete sentences, answer questions, and summarize texts.
For environmental scanning, LLMs enable automatically generating human-like reports, competitor briefs, and other narrative materials using natural language. Large language models continue to improve as they ingest more data, meaning the more environmental scanning completed, the better they become.
Machine learning refers to algorithms that can improve at tasks through experience without direct human input. These models, which are largely based on statistical information, uncover hidden correlations by analyzing data to optimize predictions and decisions.
Machine learning excels at finding signals within massive, multifaceted datasets generated via environmental scanning. Techniques like clustering reveal distinct consumer segments for targeted strategies, while forecasting–or predictive–models predict emerging trends and future disruptions. Overall, machine learning is excellent for discovering otherwise inaccessible insights.
Generative AI can automatically create new, human-like content such as images, videos, audio and text. For environmental scanning, it enables on-demand generation of graphs, data visualizations, marketing content, and other assets tailored to current findings.
Rather than relying solely on manual reporting, generative models can instantly produce customized graphics, presentations and materials visualizing the latest insights. This brings key information to stakeholders more efficiently and ensures materials can be dynamically updated to stay relevant, even as new data emerges.
Deep learning refers to neural networks with multiple layers that can infer very complex patterns. These brain-inspired models process raw, unstructured data like images, video, audio and sensor data.
For environmental scanning, deep learning can facilitate the analysis of satellite imagery, retail traffic video, and product quality sensor data. This bypasses manual data analysis and offers new observational sources to broaden insights. Deep learning also continues improving as it ingests more raw data over time, just like large language models.
Challenges of Implementing AI-Driven Environmental Scanning Into Your Business
Despite the powerful technology behind AI environmental scanning software, there are still some challenges to consider.
Lack of Expertise
As artificial intelligence is a relatively new and constantly evolving technology, there are few true AI experts available, leading to a deficit in skills. Consequently, robustly building and maintaining AI-driven environmental scanning software is difficult, unless you hire an experienced development agency, like Idea Maker.
Requires Vast Computing Power
If you elect to build your AI environmental scanning software in-house, be aware that it requires vast computing power, including a solid server infrastructure, which is both costly and resource intensive. As a result, you should consider outsourcing your infrastructure needs to a cloud-service provider or use an existing AI model API such as GPT. Otherwise, you can hire a software development company to handle these issues for you.
High Initial Cost
Developing AI powered software has a high initial cost due to developer wages, infrastructure needs, access to APIs, and data gathering. However, in the long run, AI environmental scanning software will serve to identify high value opportunities, making it well worth the money. Many software development agencies will also offer a minimal viable product (MVP) at a more affordable rate.
How Can Idea Maker Help Your Business Implement AI-Powered Environmental Scanning Software?
At Idea Maker, we have a team of software development and artificial intelligence experts dedicated to delivering high-quality software solutions. So, if you’re looking to build your own AI-driven environmental scanning software, schedule a free consultation with us today.