The future role of AI in a connected business
Three out of four c-suite executives believe that if they don’t scale artificial intelligence (AI) in the next five years, they risk going out of business entirely, according to an Accenture study.
In the not-so-distant future, AI will no longer be an option but a requirement for a company’s success.
However, AI deployment today is largely limited to specific lines of business use cases, such as helping marketers predict customer behaviour, or empowering talent teams to determine the best hire. Pretty insightful and valuable and, sometimes, eye opening and scary in terms of accuracy!
Executives that have been progressive in rolling out AI across their organisations, beyond advanced analytics, are gaining a competitive advantage by creating connected businesses. This is where systems, data, processes and people are unified and actionable insights are intelligently distributed in real-time using machine learning capabilities.
Those visionary companies that understand the value and role of AI in running high performing business operations will lead in the AI economy of tomorrow.
So, what does this mean for organisations today? Well, if you have not deployed AI, you’d better catch-up, fast. If you have rolled out aspects of AI, keep going at speed so you can create competitive advantage.
To help inspire you, I have provided several examples of how AI will be used to create, run and grow connected businesses, where people will have on-demand access to insights for collaboratively understanding, anticipating and engaging customers, suppliers, partners and other key stakeholders.
The examples have been organised around the core pillars that power connected businesses. Together, via a single platform that includes system integration, data management, process automation and continuous intelligence, can help you achieve event-driven decision-making.
- Field discovery: by analysing the header, metadata and content of all your data, the AI system can identify which points, or fields, can be used to link data from different systems together into a single data lake or warehouse, automating tedious tasks for data engineers.
- Cleansing and simplification: AI can recognise data points or entire columns that are not adding any information to the data, or even worse, are anomalies that can distract your analysts from recognising the “bigger picture”. Pointing these records or columns out can assist decision-making about data sources and how to interpret and respond to anomalies.
- Analytics suggestions: once systems are integrated, AI helps recognise what different areas in the data mean, by identifying sales orders and where they are located in the data, for example. The same goes for returns, customer profiles or marketing campaigns. This is then used to automatically build insights platforms, freeing up data engineers and data analysts to focus on other tasks.
- Entity recognition: AI eliminates duplicate records in CRM or CDP systems, for example, by detecting and fixing common misspellings, verifying post codes and recognising that Robert and Bob may be the same person. The same applies to other entities such as places (UK or Britain), or products.
- Data imputation: errors in manual data input or transmissions, systems going down, and more can create gaps in data. AI can use advanced data imputation techniques to fill in those gaps, predicting the most likely value that those “known unknowns” should have.
- Real-time alerting: AI can identify oddities in live data systems, such as when values are off from what should be expected, so your team can quickly identify how data is changing into “new normals”.
- Automated performance improvement: as more data comes in, an AI can automatically decide the predicting power of newer data, triggering processes for the AI to self-update when needed, without any human interaction. These internal self-improvement metrics that evaluate data quality can be surfaced allowing your analysts or data scientists to diagnose the state of the entire system.
- Automated timely customer communications: AI can automatically draft communications and decide when to send them over to your customers (by email, by SMS or messaging apps, or post). It can evaluate its own performance in terms of customer response making improvements to communications where necessary.
- Automated insights: in addition to streamlining analytics development, AI can help automate the highlighting insights and form them into a natural (human) language. When a certain figure in a chart goes up, for example, AI can interpret that as sales having gone up 10%, identify possible causes, as well as related events, and produce the text that can be communicated to the data team and decision makers.
- Link with live open data: automatically interpreting and analysing the content from news outlets, social media, stock markets, weather forecasts, even the census, can help integrate business data within a wider socio-economic and demographic context so the AI system can enhance predictions while increasing awareness of defects or contradictions in data.
- Everything in real-time: since the data is ingested by the integration platform, all of the data processes can be monitored and controlled by the AI system, which functions as a guardian of the data, seeking anomalies, errors and cleaning up data plus serving useful and detailed information in dashboards and automated reports.
The future of business is connected and it’s being powered by AI. Those that embrace this vision and move quickly toward this reality will be the winners.
Head of Data Science
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