Finance
Legal
Utility & Telecom
Healthcare
Government
Manufacturing
Retail & eCom
Supply Chain & Logistics
Data Management
The possibilities of gaining a competitive edge through Ai in improvements to speed, time, cost and efficiency and customer needs is driving the growth in these new technologies.
Some examples of these are Chat Bots – automated virtual assistants which are resolving customer queries through online messaging systems and some of these chat bots can undertake over 200 tasks and continue to improve through machine learning.
The use of Bots in trading to quickly identify trading patterns and formulate new trading strategies and management of the vast quantities of customer data financial companies have access to.
Load forecasting
Being able to have advanced knowledge of supply and demand forecast would optimize the economics of load dispatch and machine learning can be used to undertake this.
Load forecasting
Being able to have advanced knowledge of supply and demand forecast would optimize the economics of load dispatch and machine learning can be used to undertake this.
Yields
Optimising yields Ai can assist power providers with power generation efficiency with real time adjustments which can greatly increase energy production.
Customers
Knowledge of customers through the use of machine learning would enable utilities to maximise their price and margin options and reduce customer churn.
Some of the time saving can be in document analysis, so machines can review and flag up documents that are relevant to particular cases. They can undertake due diligence, so confirming facts and figures and evaluating decisions and even predict legal outcomes and arguments.
The technology has already begun entering insurance systems and processes, and started to deliver efficiencies, whilst improving customer experience.
Automating claims processing through AI solutions is an example of how time and speed can be reduced
But Ai in manufacturing can be a lot more than shop floor automation as Ai can automate supply chains, just in time deliveries and ordering and help companies to anticipate marketing changes.
But Ai in manufacturing can be a lot more than shop floor automation as Ai can automate supply chains, just in time deliveries and ordering and help companies to anticipate marketing changes.
Some of these could be –
People Keeping Healthy – Technology applications and apps encourage healthier behaviour in individuals and help with the proactive management of a healthy lifestyle. It puts consumers in control of health and well-being.
Making a Diagnosis
Large amounts of data can be quickly unlocked to present medical information from many sources and technology can be combined to build powerful learning algorithms to solve healthcare issues.
Making Decisions
Pattern recognition can identify patients at risk of developing a condition so allowing action to remedy this.
Early Detection
Consumer wearables and other medical devices combined with Ai to recognise early disease signs
Treatment
Ai can be used to manage drug discovery, creation and testing to cut time to market.
Training
Ai can implement training anywhere with the power of Ai included in a smartphone a trainee can undertake training at any time.
Situation:
Identify all automation opportunities for the entire business unit in less than 2 week
Actions:
- Engaged with over 30 key stakeholders (senior department managers, team leads and subject matter experts)
- Liaised with the RPA development team, arranged demonstrations of Blue Prism’s capabilities
- Created toolkit to assist sub-teams with collating data which was assessed and prioritised
Situation:
- Manual move data between two legacy systems, because systems stopped talking to each other
- Team manager didn’t have the budget to hire 10 more staff, to cope with the forecasted increase in demand
- Overwhelming backlog and increase in SMART meters required an addition 30 FTE+
Actions:
- Listed all the teams processes
- Collated cycle time and volume data the team had collected over the year (sense checked by timing a sample)
- Filtered out only RPS suitable processes (ran a workshop)
- Out of the 100 tasks, used a Pareto graph to calculate the few tasks (7) which took up 80% of the team’s time
- Ran solution design workshop to re-design certain processes (using 5 Whys, and 5Rs)
- Created Process Definition Documents to build the bots
Situation:
-
The normal process is automated, missing data created deviations from the ‘happy path
Different missing data created deviations at various points along the process – creating manually work to add missing data
Team manager wanted to allow team to cope with new customers who would invariably provide suboptimal information
Actions:
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Map out the process End-to-End and identify the deviation areas Zoom into areas and compile a list of manual processes
Run root cause workshops to identify the true reason (customers? outside our control? What Influence do we have?)
Filtered out only RPS suitable processes (ran group interviews with manager and SMEs)
Created Process design documents to build the bots
Situation:
- Team received hundreds of paper forms from Independant Financial Advisors (IFA), which they needed to scan for record-keeping and manually enter data into the CRM system
Actions:
-
Facilitated the collection of high quality scanned forms which were saved in a datalake
the ICR (intelligent character recognition) engine digested the data and converted both text and handwritten data into standardised information
conditions were set of each field and the engine learnt of the correct type of information. e.g. if the handwriting was read ‘LODNON’ the intelligent engine would know that for a UK address this had an acceptably high probablity of being ‘LONDON’
Situation:
- Newly formed RPA support team taking on new client bots in Automation Anywhere, UiPath and Blue Prism for departments in Finance and HR
Actions:
-
Co-managed a team of 10 robotics engineers supporting digital workers (bots) for several clients in Blue Prism, UiPath or Automation Anywhere
Assisted with: Invoicing, Capacity management, Client relationship management, team Capability management and tracking (working closely with Technical lead to identify skill gaps and carrying out interviews)
Assist in setting up KPI tracking and reporting. Developing processes and controls to ensure consistency and quality of service
Facilitated the build of complexity matrix to assess the complexity of supporting RPA bots and making bot changes
Situation:
- Create competitive build of materials (BoMs) for winning £multi-million and £billion tenders
Actions:
-
Compiled all historical publically available contract data into a central repository
Segmented contract builds into components and estimated component costs
Reverse engineered historical contracts to verify calculations
Uploaded data onto SharePoint to assist business development department