DISCLAIMER: At the time of writing I am an employee of Cochlear LTD. All information used in this, and related, articles are based on publicly available sources. At no point was privileged or internal information used in any way. This was done as a part of a master's degree assignment.
Introduction
In this business proposal, we will build on the core themes discussed in the management summary for the uses of Artificial Intelligence in Cochlears' business context. This business proposal is for phase one of the program.
What are our Strategic Goals & Challenges?
“To Maintain market leadership by market share by maintaining the highest levels of customer satisfaction, growing our customer base and cost leadership.
We will maintain market leadership of products with Innovation and differentiation, and we will do so with absolute regulatory compliance.”
Competitive Analysis
Regarding Competitive Rivalry, our key competitors, Advanced Bionics and Med-El, retain 37% of the market share. The key driver of competitive growth is product innovation and reliability, as Cochlear Implants are surgically implanted and can have a product lifetime of over 80 years.
Supplier Power is also a significant consideration in that there are only a handful of specialized capital equipment providers for the manufacturing equipment that we require to create both the coils and components for the Cochlear implants.
Fortunately, Buyer Power is relatively negligible for Cochlear Implant products as there is a limited selection available, and once the customer has committed to a Cochlear Implant, there is very little possibility of this changing.
The Threat of Substitution is irrelevant to Cochlear Implant products due to their implanted and medical nature.
We are also fortunate that the Threat of New Entry into the Cochlear Implant market is relatively protected by significant barriers to entry, in terms of medical regulations, and the trust value of history for medical products.
Data, Machine Learning and Artificial Intelligence strategy
Our approach to Data, Machine Learning and Artificial Intelligence strategy will be to do so in a highly organized and incremental way.
This proposed program of work is at the highest level to first achieve Data Mastery by creating and implementing a robust Data Strategy in the organization. We will look to address the easiest and most strategically aligned opportunities during this process.
AI use-case to strategic alignment
The focus of this business proposal will be to support our primary strategic goal, to Maintain market leadership by maintaining the highest levels of customer satisfaction, growing our customer base and cost leadership. We will also look to address some of the key challenges identified, ranked by impact.
To do so we will need to lay the foundations for a robust data strategy that supports the future of Machine Learning and Artificial Intelligence opportunities.
The identified core AI themes with documented improvement for businesses adopting them are listed below.
Source: Deloitte - Uncovering the connection between digital maturity and financial performance
Outline of Program of work
Data Mastery directly addresses the identified challenges of the Increasingly competitive market and disruptive technologies and stringent regulatory & patent environment, as well as supporting all other Machine Learning and Artificial Intelligence objectives. Data is the fuel for all ML and AI.
AI use-case real-world implementation examples and limitations
These are the in-scope use cases, and a description of how we will use these at a high level.
Pattern recognition by using unstructured machine learning to investigate our data sets and identify any interesting correlations for insights.
Currently one of the most successful established implementations of this is Salesforce Einstein, and we will be building on our capabilities by using this tool by using the Einstein Analytics and Artificial Intelligence components, as we already use Salesforce Lightning.
This will also empower us to perform better customer segmentation, social semantics and sentiment analysis across customer touch points outside of the Salesforce ecosystem. We will investigate the backward integration of this data into combined business intelligence dashboards in Salesforce as a part of the program.
Once we have passed both User Acceptance Testing (UAT) and Software Testing (SIT) we will begin with work covering Regulatory, Patent and Maintenance opportunities.
An active Regulatory and Patent monitoring system, which by using web scraping and analyzing tokenized data will allow filtering of relevant items for review and further investigation as needed. These systems are heavily used by media, public relations, and academic organizations.
We will also be performing data analysis and use machine learning to investigate predictive maintenance and Smart maintenance scheduling for customer devices, manufacturing systems and internal infrastructure systems. The approach will be based on a strong focus on determining both physical and software entropy degradation rates, implementing monitoring systems and establishing an optimal approach and schedule for ongoing maintenance. These kinds of systems are very mature, used in several manufacturing organizations and commercially available.
For each of these implementations, we will develop and monitor with continuous evaluation methodologies, which we will review regularly to improve.
Out of scope use cases are any capabilities identified to be part of the DevOps portfolio.
These are Automated insights & Business Intelligence, Fraud detection and prevention for online transactions and Security surveillance.
Specific user benefits from the program
The specific customer and user benefits from this first phase of work will be better and more relevant customer services, and more relevant customer messaging.
For the organization, the benefits will be improved marketing opportunities, improved customer sentiment awareness and the foundations for future AI core themes.
Additional user benefits of significantly improved device maintenance. For the organization, the benefits will be regulatory and patent monitoring, and a robust maintenance schedule across capital equipment and software systems.
Data Obligations and Considerations
All data will have to adhere to the basic standards of data custodian and stewardship principles, namely:
Autonomy in the acquisition of all data by informed consent,
Beneficence in the focus of doing no harm and
Justice in making sure any benefits are evenly distributed to all customers, users, and subscribers.
We will place significant focus on Socio-cultural, ethical and deontological considerations for the data and data usage. Wherever possible we will use anonymized data for analysis purposes and keep the unique identifiers for these restricted to senior staff only.
In the case of first nation customers and users, we would potentially have additional information and disclaimer banners to reconfirm the specificities of knowledge being a community asset requiring cultural respect and approval.
We will also implement a series of bias-seeking analysis checkpoints periodically, for all data used in systems. The biases may be socio-economic, racial, cultural, gendered or political. We will also consult any relevant community and regulatory groups for their feedback and approval.
We must also take into consideration that the black box nature of Artificial intelligence and Machine learning is regulatorily and ethically contentious specifically in the medical context and we will take steps to make sure that we are able to replicate and demonstrate any implementations using patient data.
Supporting considerations
There are a few supporting considerations that will need to be considered, with the most influential and important being managerial and leadership requirements.
A senior executive owner and this champion would be vital to the success of any activity of this nature, as would a group of senior managers to handle the multi-faceted nature of a program of work such as this. These managerial roles will be finalized during the initial workshop phase of the program, as we will also be refining the data strategy for the organization.
The technical requirements for this program of work are an expansion of the current Salesforce system to include the Einstein functionalities.
We will establish the data team who will require supporting computers and software; however, the software backbone of this squad will be Einstein.
For training, we will require a data analytics course for IT data squad members, and Salesforce Einstein Training for IT & Data squads, Corporate Users and Customer Facing Users.
Performance & Quality measurement will be by key performance indicators, namely:
Alignment to benefit ROI projections,
Benchmarking customer growth vs averaged annualized customer growth,
Net promoter scores,
User acceptance testing results,
End of sprint work reviews,
Sprint retrospectives
The senior stakeholders will be the senior VP, DevOps manager, Marketing lead, Customer Service lead, Change leads, and Program Manager.
The communications plan will be to have monthly program alignment meetings, with several dashboards and an internal online reporting update page available.
We will track active Risks in the risk register. Briefly, our key program risks are
Sourcing technical staff,
Training gaps and
Technical challenges related to data migration.
*Omitted sections on Project Plan, Benefits analysis and ROI projections
References
3 Areas Where AI Will Boost Your Competitive Advantage. (2021, December 6). Harvard Business Review. https://hbr.org/2021/12/3-areas-where-ai-will-boost-your-competitive-advantage
Cochlear Implant Market Size & Share Report, 2030. (2022). Grand View Research - Cochlear Implant Market. https://www.grandviewresearch.com/industry-analysis/cochlear-implants-industry
Columbus, L. (2020, September 1). Why Digital Transformation Always Needs To Start With Customers First. Forbes. https://www.forbes.com/sites/louiscolumbus/2020/08/30/why-digital-transformation-always-needs-to-start-with-customers-first/?sh=185632f02c61
Eckroth. (2018). AI Blueprints (1st edition). Packt Publishing.
Fourth Industrial Revolution. (2020, April 7). World Economic Forum. https://www.weforum.org/focus/fourth-industrial-revolution
Gartner Predicts The Future Of AI Technologies. (2019, November 5). Gartner. https://www.gartner.com/smarterwithgartner/gartner-predicts-the-future-of-ai-technologies
NI Business Info. (2020). Examples of artificial intelligence use in business | nibusinessinfo.co.uk. https://www.nibusinessinfo.co.uk/content/examples-artificial-intelligence-use-business
Pivoting to digital maturity. (2021). Deloitte Insights. https://www2.deloitte.com/us/en/insights/focus/digital-maturity/digital-maturity-pivot-model.html
Salesforce Einstein Analytics Reviews, Competitors and Pricing. (2021). Salesforce. https://www.peerspot.com/products/salesforce-einstein-analytics-reviews
Taulli. (2020). Implementing AI Systems: Transform Your Business in 6 Steps. Apress L. P.
The Benefits of Artificial Intelligence for Businesses in Every Industry. (2022). Salesforce EMEA Blog. https://www.salesforce.com/eu/blog/2020/02/ai-for-business-a-function-by-function-birds-eye-view.html
Thomas, R., & Zikopoulos, P. (2020). The AI Ladder. Van Duuren Media.
Uncovering the connection between digital maturity and financial performance. (2021). Deloitte Insights. https://www2.deloitte.com/us/en/insights/topics/digital-transformation/digital-transformation-survey.html
Villegas, C. (2018a, April 6). Roger Pen/ Roger EasyPen (For Cochlear Implants). Arizona Hearing Center. https://www.azhear.com/roger-pen-roger-easypen-for-cochlear-implants/
Villegas, C. (2018b, April 12). Streaming Technology (For Cochlear Implants). Arizona Hearing Center. https://www.azhear.com/streaming-technology-for-cochlear-implants/
Wikipedia contributors. (2022, April 12). Software entropy. Wikipedia. https://en.wikipedia.org/wiki/Software_entropy
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