Competing effectively in a digital economy that is constantly evolving is challenging.
The post pandemic workplace has shifted, consumer expectations have altered, and spending habits have moved online.
As the world is moving to an ever more digital first environment how can organisations scale better, be more adaptive to change, stay relevant and innovate faster?
Since the onset of Covid, digital transformation has accelerated exponentially. Organisations across all industries have at some point needed to evaluate how they operate, use technology, work together, and engage with their customers.
Why is AI gaining popularity?
As technology shifts towards greater automation, speed, and efficiency as well as becoming more specialised and personalised, the need for better data intelligence to enable businesses to scale and operate more effectively is key. And to do that successfully requires AI.
To truly transform an organisation to not just be digitally led but data led is now becoming a strategic necessity.
Artificial intelligence (AI) technologies such as machine learning (ML), natural language processing (NLP), structured data interaction and intelligent document processing enable the creation of smart business processes and automated workflows that think, learn, and adapt with little or no human intervention.
Cognitive technologies are increasingly being used to solve business problems; indeed, many executives believe that AI will substantially transform their companies within three years. But to what extent can AI transform business performance and innovation? And is the investment worth it?
How is AI being used in business?
Artificial Intelligence is being used by organisations to:
- Improve customer services - e.g., use virtual assistant programs or intelligent chatbots to provide real-time support to users (for example, with billing and other tasks).
- Automate workloads - e.g., collect and analyse data and use machine learning (ML) algorithms to categorise work, automatically route service requests, etc.
- Optimise logistics - e.g., use AI-powered image recognition tools to monitor and optimise your infrastructure, plan transport routes, etc.
- Increase customer experience - smart searches and relevance features, personalisation as a service, matching products to people, providing recommendations and purchase predictions.
- Prevent IT outages - e.g., use anomaly detection techniques to identify patterns that are likely to disrupt your business, such as an IT outage. Specific AI software may also help you to detect and deter security intrusions.
- Predict performance - e.g., use AI applications to determine when you might reach performance goals, such as response time to help desk calls.
- Predict behaviour – e.g., use ML algorithms to analyse patterns of online behaviour to, for example, serve tailored product offers, detect credit card fraud, or target appropriate adverts.
- Manage and analyse your data - e.g., AI can help you interpret and mine your data more efficiently than ever before with faster data extraction and enrichment than is possible by a human and provide meaningful insight into your assets, your brand, staff, or customers.
- Improve your marketing and advertising - for example, effectively track user behaviour, provide personalised recommendations, and content curation, identify patterns and image recognition.
Anywhere there is a manual human process that could be automated, AI could help.
Extracting value and ROI from AI
McKinsey Global Survey 2020 on artificial intelligence (AI) suggest that organisations are using AI as a tool for generating value. Increasingly, that value is coming in the form of revenues. A small contingent of respondents coming from a variety of industries attribute 20 percent or more of their organizations' earnings before interest and taxes (EBIT) to AI.
This is only expected to accelerate as the level of AI maturity across enterprises grows.
More and more organisations are experiencing an uptick in value and ROI from AI as confidence grows and results show a positive impact on multiple value streams including:
- Increased revenue – personalisation optimises purchases in retail for example
- Direct or indirect cost savings – machines can process data much faster and at scale than a human speeding up mundane tasks and creating human resource capacity for other tasks.
- Reducing risk - detecting fraud, predicting insurance risk, identifying missed payments, predicting loan credit risks
- Differentiation from competitors – understanding more about the competitive environment enables businesses to make more effective, data driven decisions to get ahead of the curve.
- Speed to value – deriving insights from data faster than a human means improvements can be implemented much faster.
- Supply chain value – lowering risk of business disruption or delays.
- Regulation and compliance value - Reducing risk of non-compliance.
Using AI to connect the dots in complex datasets
Although AI has so far been used in workflow /data process automation and task automation/robotic process automation the opportunities are much larger.
Once the human has trained the machine to extract the data required, the machine quickly learns which can ultimately support decision automation through machine learning and computer vision/image processing as well as voice/natural language processing.
The challenge is knowing which data is useful and which is not and then being able to unlock the potentially useful data that is typically stuck in non-machine-readable formats – like PDF forms, contracts and leases, lab data, prescriptions, image and audio files, chat etc (unstructured data) and gathering virtual dust in cloud storage silos.
Many organisations have huge amounts of untapped data that could be used to derive useful insights from and make more informed decisions, yet they lack skilled AI engineers who can create the necessary tools and algorithms. Working with specialist partners ensures that the organisation considers all necessary efforts on the “first mile,” that is, how to acquire and organize data and efforts, as well as on the “last mile,” or how to integrate the output of AI models into useful, automated workflows that can lead to business value.
Six steps to AI success
- Have a clear and defined AI vision and strategy which means knowing what problems you want to solve and the business outcomes you desire.
- Choose a proven AI partner to help create a roadmap clearly prioritizing the projects linked to business value across the organization that could be solved with AI.
- Identify the metrics to measure the outcomes and be prepared to make changes to existing systems or create entirely new processes to collect the data needed.
- Start small with rapid prototyping and pilot phases with the help of a specialist to ensure proof of concept.
- Consider usability and interoperability with other systems and platforms when deploying AI value into the business.
- Making sure you have accurate and easy to process data in the beginning will ensure a smooth AI project down the line. Digitising data should be the first thing on the list before you start the real AI work.
Enterprises have a strong interest in AI, with 48% of CIOs saying that they have already deployed or plan to deploy AI and machine learning (ML) technologies within the next 12 months, according to the 2022 Gartner “CIO and Technology Executive Survey.”
Many organisations are using AI alongside traditional data analytics techniques which provides those first tentative steps into building AI maturity which will eventually replace traditional methods and models.
There is no doubt that the potential of AI and Machine Learning technologies is transformational for humankind. Dubbed as the ‘4th industrial ‘evolution', according to Google CEO Sundar Pichai, its impact will be even greater than that of fire or electricity on our development as a species. (Source Forbes)
As we head into 2022, we will see a great deal more of AI maturity among enterprises and growth of new AI and ML ‘as a service' models and no-code and low-code solutions allowing more affordable and versatile AI solutions to enter the market to benefit businesses of different sizes and types.
And, as within other industries like open banking, the ongoing "democratisation” of AI and data technology ultimately means a greater level playing field for all.
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