Introduction to implementing AI

Capacity Development.

Implementing AI solutions requires organisations to develop:

·      Human Capacity

·      Technical Capacity

·      Business Capacity

Human Capacity.

Organisations that wish to exploit the power of AI will need to have people who can recognise opportunities for AI, understand how to apply it and have the confidence to implement and use AI-based solutions. To achieve this, individual talent and capabilities should be strengthened through learning programs with the goal of staff being able to use AI to add value to their work. Organisations would also need to build an AI talent network - an ever-increasing network for talent acquisition, leading to engagement with ambitious and capable people who are motivated to work for the organisation.

Leaders need to ask whether they have the right talent across the company, from people involved in high level vision and strategy to those involved in implementation and deployment. Organisations require a synthesis of AI expertise, domain knowledge, business acumen, and corporate strategy and vision. AI implementations require coordinated efforts between Data Scientists, machine learning experts, developers, architects, designers, domain experts and stakeholders.

In such a fast-evolving technological environment leader need to find trusted advisors to guide next steps - experts who speak their language and keep pace with AI's rapid growth as the field reaches and exceeds successive tipping points.

Technical Capacity.

Data Science capabilities are essential to an organisation that wants to exploit AI Data Scientists are needed to work alongside domain experts to build, implement and maintain AI solutions. These AI solutions should enable improved speed & accuracy; better business processes; and predictive capabilities, and enable the organisation to develop and exploit new business opportunities.

As well as developing capacities internally, organisations should seriously consider investing in technology businesses that can add value, and even incubating new AI businesses.  

Technical capabilities can be built through developing an AI community through AI related networking events and online fora, bringing together researchers, entrepreneurs and investors who can support with ideas, research, commercial opportunities and finance.

To develop technical capacity, key tasks include:  

1.    Architecture Planning – build a technical framework for exploiting AI, and think of scalability from the start

2.    AI Roadmap – develop capacities along a strategic pathway, focusing on priority use-cases

3.    AI Foundations – layered IT building blocks, for example: Data Assimilation  Data pre-processing Descriptive Analytics  Predictive Analytics

4.    Internal-facing solutions development – drive the development, and adoption of internal tools and solutions

5.    Customer-facing product development – drive the development, adoption and sale of new products.

Business Capacity.

Exploiting AI offers organisations the opportunity to take more sophisticated approaches to business development. For example, being able to identify and reach ‘segments of one’ through AI-driven marketing technologies, and automated processes for customer acquisition and on-boarding. AI also offers the chance to open new revenue streams from new products.

The use of AI to provide increasingly customised products and services could also mean a shift away from the production of commodities and towards the production of solutions. Coupled with Industry 4.0 approaches, AI can accelerate mass customisation. This has implications for the Sales function in the organisation, such as ensuring that sales people have a consultative and solutions-orientated approach to selling.

The same can be said about partnering. In such a rapidly moving environment, monolithic partnerships are likely to be replaced by dynamic and adaptable partnering models.

Complete and Continue