The world of business is gradually coming to terms with the reality that at some point (hopefully sooner) they have to say “aye, aye” to AI (Artificial Intelligence). Yet, despite the potential for innovation and efficiency, many enterprises remain wary of embracing AI.
This hesitancy often stems from the overwhelming nature of transformation, the complex nature of implementation, and concerns about the potential disruptions it might introduce.
There are also concerns around change management and automation to more complex issues such as how to implement AI and benefit from it, as well as the risks and ethical aspects associated with it.
However, there are enough and more examples of successful implementation of AI around the world and across industries.
An energy company implemented a Natural Language Processing (NLP) classification model to classify and redirect incoming public emails (1,000+ per day) to appropriate teams within the business. This resulted in 40% faster response times and a 15% increase in customer satisfaction ratings.
An Australian call centre in retail implemented a predictive solution to forecast resourcing requirements. A predictive AI solution outperformed human-level accuracy by 17.2 percent saving huge overstaffing costs.
A local government implemented a computer vision solution for roof inspection, reducing workplace injuries and improving efficiency by 70%.
And we haven’t even spoken about complex machine learning applications, like those used in ride-hailing services, or for health prognosis and prediction.
The possibilities and opportunities for companies are endless, allowing them to streamline operations, make better use of time and resources, and cater to client/user needs more efficiently.
The concerns then often stem from popular perception, and lack of goals, a plan and due diligence.
In short, there is comfort in knowing where you are headed.
AI Use Cases
Data Analysis
One area in which AI plays a key role is data analysis. It can process vast amounts of data quickly and with high accuracy, providing insights that can go a long way in aiding the decision-making process. The benefits can be seen in business intelligence, forecasting and strategic planning.
Resource Optimization and Automation
Growth-focused businesses often find themselves restricted by lack of resources – people, financial or time.
AI helps automate repetitive tasks, allowing team members to focus on more strategic and creative tasks. This, in turn, enhances productivity, sparks strategic initiatives and even reduces operational costs.
Competitive Edge
Technology in general, and now AI capabilities in particular, are helping businesses become more agile, proactive and efficient in meeting customer demands, and responding to market or macro changes.
At the same time, companies can make significant gains (cost or time savings or improved efficiency) from optimized resource allocation, such as inventory management, supply chain logistics and workforce scheduling, thanks to AI.
The technology will also provide the ability to better personalize their products, services and marketing efforts for individual habits, leading to higher satisfaction and loyalty.
Customer Support
From answering queries to resolving issues and offering recommendations without a break, chatbots and virtual assistants play an increasingly pivotal role in enhancing customer experience.
Predictive Analytics
Decision-makers in companies can get actionable insights and recommendations from AI-powered analytics tools, which will help in strategic planning, risk assessment and performance optimization.
In more technical areas, predictive solutions have helped with proactive maintenance, safety protocols and more. A reduction in downtime and an increase in lifespan of capital assets is a major benefit.
Risk Management through Pattern Recognition
Risk management, analyzing complex data patterns to identify potential risks and fraud is an emerging use case, especially in financial services. This, in turn, will help businesses mitigate losses and ensure security.
Product Development
A critical aspect of AI use is innovation.
With advanced analytics and automation at the core of the development processes, AI enables businesses to develop new products, services and revenue streams.
“It’s hard to avoid conversations about AI in business today. healthcare, retail, financial services, manufacturing—whatever the industry, business leaders want to know how using data can give them a competitive advantage and help address the post-COVID challenges they face each day,” says a report by the IBM Data and AI team.
The Path Forward
Any aspect of digital transformation, whether limited or exhaustive in scope, benefits from a comprehensive Discovery process.
This process could be undertaken internally, provided the capabilities exist, or with the help of experts who are well-versed in different use cases.
A thorough Discovery process, like GENESIS by Launchcode, explores current landscape, identifies your business needs and defines specific requirements, including AI, outlines user stories, reviews existing technology stack and conducts research.
The roadmap offered by such an extensive process offers clarity in scope and budget, recommends goals and reduces challenges during development and implementation.
For example, any business will be best served by aiming for the proverbial “low-hanging fruit” in terms of AI implementation.
Identifying small goals, starting with simpler tasks and developing them with the aid of the right business metrics will yield positive results and also keep expectations under check.
No Data, No AI
“If your data isn’t ready for generative AI, your business isn’t ready for Generative AI,” says a McKinsey & Company report.
Let’s talk Generative AI.
OpenAI’s ChatGPT started conversations about the use of Generative AI, which has since found favour across a wide range of industries, including software development, healthcare, finance, entertainment, customer service, sales and marketing, art, writing, fashion, product design and more.
It is capable of generating text, images or other data using generative models, often in response to prompts.
Simplistically put, Generative AI models learn the patterns and structure of their input training data and then generate new data that has similar characteristics.
A 2023 research by McKinsey & Company estimated that generative AI could add the equivalent of $2.6-4.4 trillion in annual economic benefits across 63 use cases.
But “pull the thread on each of these cases, and it will lead back to data,” they said.
Further highlighting the importance of data in AI, the IBM report says: “A common phrase you’ll hear around AI is that artificial intelligence is only as good as the data foundation that shapes it. Therefore, a well-built AI for business program must also have a good data governance framework.”
Small steps, big gains
At Launchcode, we believe Digital Transformation to be a three-step process.
The first step is Digitization, which focuses on the digitization of data. No transformation, including AI, is possible if your data is on a spreadsheet or isn’t clean.
Digitization is followed by Digitalization, which focuses on leveraging the digitized data to improve processes and foster efficiency.
And then comes Digital transformation, which leverages the first two steps to make a business agile, ready to create new revenue streams, business models, and adapt to changing micro and macro business landscapes.
Adoption of AI typically follows a similar process:
- Clean data
- Limited-scope implementations
- Exhaustive-scope implementations
Adopt to Adapt
By all accounts, AI is here to stay, and enterprises will eventually have to find ways to adopt AI based on their requirements.
It is also increasingly evident that reluctance in its adoption is overshadowed by the immense potential it holds.
It's time for enterprises to not just embrace AI but to wield it as a catalyst for unprecedented growth and evolution.
Want to explore the adoption of AI at your business? Let’s talk.
