Why businesses must invest in AI augmentation
Human fears around artificial intelligence stem from an assumption that the ultimate goal of AI is to replicate, and surpass, human intelligence, thereby threatening our own existence. Indeed, worries about AI supplanting jobs have been constant since the technology’s advent. However, the theory of AI augmentation challenges this end goal, reimagining AI as a way to develop technology to supplement and support human intelligence, with humans remaining at the centre of the decision-making process. Augmented intelligence can therefore be defined as the human-centred partnership model of people and AI working together to enhance cognitive performance and business operations.
Gartner predicts that AI augmentation will create $2.9 trillion of business value and 6.2 billion hours of worker productivity globally, while by 2030 augmentation will surpass all other forms of AI initiatives to account for 44 per cent of the global AI-derived business value. Analysts believe that as AI evolves, combined human and AI capabilities harnessed through augmented intelligence will deliver numerous benefits for business.
For example, combining AI with a process called contextual decision intelligence involves using entity and network analysis techniques in conjunction with advanced analytical methods to gain a better understanding of context. Such technology can empower organisations to uncover hidden risks and new opportunities, and to solve major challenges from credit risk to pricing or customer intelligence. Here, we take a look at how augmented intelligence can deliver such positive business changes, and why organisations should invest in such technologies for the future.
In a post-digital age, technologies like AI are generating change at pace. However, AI needs to be nurtured, taught and given guidelines; the most effective and transparent AI results from the combination of machine learning techniques and also rules, scenarios and set examples provided by experts. Using the expertise of people will be crucial in maintaining a teacher/parental involvement that means any resulting AI will be more explainable and less likely to develop biases. CIOs and business leaders need to invest in augmented AI that replaces manual processes but doesn’t replace humans, instead freeing workers up to focus on more critical processes. Indeed, data can be utilised alongside AI to help people become more effective.
With new roles emerging and new skills being in demand, the existing pool of skilled workers will not be enough to meet demand. Businesses must shift towards using AI alongside contextual decision intelligence to enable people to make better and fast decisions through using the wealth of knowledge from team members and experts within the organisation. Meanwhile, workers can offset more routine tasks to machines and spend more time on the strategic thinking that drives a business forward.
Prebuilt algorithms and open-source machine-learning libraries are allowing workers to handle large amounts of data and gain insights. For example, AI is being used to automate repetitive business processes and improve employee productivity. Even small organisations can take advantage of AI and machine-learning tools since such human-machine partnerships are no longer restricted to giant companies with big budgets.
This means human workers can focus resources on high-level, socially responsible and creative tasks, providing customers with better experiences and more efficient services. Businesses integrating digital workers with content intelligence skills into automation platforms will enjoy a competitive edge.
Many businesses now have huge volumes of data but few insights to promote positive business outcomes. This means they might struggle to achieve ROI, revenue increases, regulatory compliance or improved customer experiences. Augmented intelligence can be used here to augment human capabilities with smart algorithms to provide data-driven insights at speed.
Improved analysis of insights can improve a business’ return on investment by 10-20 per cent, and also increase average profit growth by 14 per cent, according to McKinsey & Company. However, the human brain cannot process the vast amounts of data quickly or effectively to find valuable insights and make better decisions in business process optimisation. Therefore, internal corporate data can be enriched with external data sources and be used in conjunction with AI and innovative technologies such as entity resolution and network analytics to create insights at speed and unite separate data points efficiently.
Currently many AI-branded technologies available for business can and ought to be more accurately defined as augmented intelligence technologies. For example, financial institutions are integrating augmented AI for fraud detection. Using machine learning, such systems can be trained to identify and flag incidences of fraudulent activity. This data is then used by workers to interpret and investigate the fraudulent activity using their knowledge, judgement and expertise.
Sales prospecting and marketing
Sales reps spend on average 80 per cent of their time qualifying leads and 20 per cent closing deals. Qualifying leads requires advance research as well as numerous phone and email hours honing in on a lead that may be transformed into a sale. However, technology can be used to improve the vetting process so that sales professionals can instead focus on work such as relationship-building. Where the technology identifies leads, salespeople can then apply the ‘human touch’.
Analytics and AI technology can be used to improve use of data and prioritise leads based on indicators of what makes a customer ‘good’ so that sales teams know which clients to focus on and how to prioritise their time to close deals and reach quotas. AI and machine learning technologies can recognise patterns effectively, allowing sales teams to find the highest potential new prospects by matching data profiles with existing valuable customers. This means AI-enabled CRM systems can identify the highest potential prospects and prioritise other leads, saving sales teams thousands of hours focusing on the wrong prospects or analysing which to approach. Sales teams adopting AI are seeing an increase in leads and appointments of more than 50 per cent, costs reductions of 60 per cent and call time reductions of 70 per cent.
Businesses can also use AI alongside contextual decision intelligence to connect data and provide context. Such context, built out using network analytics techniques, can allow organisations to construct relationships between separate data points. These techniques can build a comprehensive picture of the client, including their historical information and interaction history. This means businesses can build richer relationships with their customers to increase share of wallet, and gain a deeper insight into their existing clients and what products would resonate at a given point in time.
AI: silver bullet or valuable part of a business’ arsenal?
Companies must look towards augmented AI to gain a competitive edge. This means considering how to streamline and automate processes so employees can be freed up to focus on critical tasks. Far from threatening jobs, if used in the right way, AI can empower employees in the workplace. Moreover, with the digital universe doubling in size every two years, businesses must ensure they are converting these huge data volumes into actionable insights.
Organisations that fail to take advantage of the data-hungry technologies proffered by AI face being overtaken by competitors. What is clear, however, is that, rather than being a ‘silver bullet’, successful implementation of AI means viewing it as an essential tool for solving business challenges, rather than a final outcome in itself. Augmented intelligence needs to be included in your digital transformation plans to achieve faster results, cut costs, generate actionable insights and enhance security. Doing so will put your business firmly on the path to innovation and positive change.