Artificial Intelligence Journey
Deploying artificial intelligence well can transform how an organisation operates and performs. Achieving this transformation at scale has many challenges including a rapidly changing landscape of available technologies. Our artificial intelligence journey, describing how organisations must go on a voyage of discovery from AI novices through to AI-first experts, overcoming many challenges along the way and delivering massive value for your organisation.
Of course, Combined Intelligence can help you accelerate your progress through this journey. We can help at each phase, whether you are just setting out, looking to scale your programme, or are looking to utilise artificial intelligence and achieve AI everywhere within your organisation. We have created some specific initiatives to help with this, follow these links for more information:
Install – Build a successful AI programme and capability.
Intensify – Scale existing programmes and moving towards a firm-wide scale of adoption.
Infuse – Evolve your organisation’s DNA to be an AI-first company.
Artificial Intelligence provides the ability to transform how an organisation operates and performs, including:
- Enhancing the customer experience.
- Improving products and service delivery
- Increasing the accuracy of operational tasks
- Reducing errors within data and processing
- Increasing efficiencies with processes and operations
- Improving quality, consistency and reliability of processes and data
- Removing mundane work, freeing people time for higher value tasks
- Expanding management and business information and insight
- Reducing risk and enhancing security
- Significantly reducing operating costs
- Increasing employee satisfaction
Achieving and maximising these benefits provides multiple challenges, requiring organisations to go on a journey of discovery and, to be successful, to make the right intelligent decisions along the way.
The journey begins with an organisation accepting the need for artificial intelligence. It then progresses through a series of phases (see illustration below), ultimately (ideally) the organisation achieves the goal of AI-everywhere, having AI existing throughout the organisation and is embedded within its DNA.
The journey can be broken down into three key phases – build, scale and evolve:
- Build – Transitioning from identifying the need through to building successful AI use-cases that deliver initial benefit to the organisation.
- Need – Recognising the need for artificial intelligence across the organisation.
- Vision – Setting an incremental vision for AI and the benefits expected to be achieved.
- Innovate – Demonstrating the potential through initial pilots and innovation activities.
- Scale – Making the step from an initial collection of AI use-cases to operating at scale with advanced examples of AI delivering the full range of benefits across the organisation.
- Groups – Siloed groups within the organisation start to explore individual AI use-cases
- Community – A working group / community of practice forms to connect with separated groups and to start the process of sharing knowledge and experience of AI implementations
- Department – A departmental initiate brings together some of the groups working on AI adoption and looks to share skills, processes and tools.
- Governance – A departmental governance for AI adoption starts to form and defines initial procedures and best practice
- Evolve – Going beyond simple utilization and evolving towards an AI-first and AI-everywhere organisation.
- Firm-wide – Utilising AI across the organisation, front and back office for all departments.
- AI-First – Thinking AI first when implementing new use-cases, services and products.
- AI Everywhere – Completing the retro fit of AI capabilities across all functions and processes.
“Artificial Intelligence has the potential to give organisations a quantum leap in their capabilities and offerings but needs strong execution to deliver the benefits”
Professor Andy Pardoe
As with many journeys, there are challenges to overcome throughout the journey in order to successfully reach the end goal. Some organisations have found this difficult, leading to AI programmes stalling at key stages of the journey and not achieving the full range and extent of potential benefits.
The challenges are not just technology, they come from many different areas, including;
- the organisational structure and operating model
- the culture and skills of the people involved, from C-suite to the data science teams and aligned IT teams
- ethics, audit, governance and responsible AI considerations
- not having a robust data science framework
- problems with scaling AI and AI Factories and specifically the move from development to production
- poor management of the process, workflow and lifecycle of AI models.
Examples of the challenges that occur at each stage of the journey include:
- Defining a realistic and achievable incremental vision for artificial intelligence.
- Creating a centre of excellence with the right skills, capabilities and scale.
- Selecting the right technology tools and platform and, potentially, partners to support delivery.
- Creating the best IT and security environment.
- Achieving buy-in across the organisation (c-level to front line) including the acceptance of change.
- Identifying, capturing and prioritising the right opportunities for AI.
- Creating and following effective processes across design, implementation, deployment and operation.
- Designing and delivering reliably multiple use-cases of AI.
- Leveraging AI as part of a wider Digital Transformation programme.
- Designing and implementing for maintainability, performance, security and scale.
- Managing change, failure, ongoing optimisation and model accuracy.
- Gaining organisational wide appreciation that AI is beneficial and long term.
- Achieving and maintaining performance and availability appropriate to a business-critical platform.
- Utilising, the more complex techniques and AI model, including computer vision and language processing and understanding.
- Sharing insights and decisions based on machine learnt experiences.
- Providing management and team knowledge via advanced intelligent analytics
- Deliver competitive advantage through innovative use of intelligence technologies for new products and services
- Completing a cultural change within the organisation to embed AI within its DNA.
These challenges can be overcome but doing this without pain, excess work, negativity towards the data science team/programme, unexpected costs, reduced benefits and significant delays are key to the success of the AI programme and onward progression of the organisation through its AI journey. Combined Intelligence can help you overcome these challenges.
Combined Intelligence exists to help organisations achieve the maximum benefit from intelligence technologies such as intelligent process automation and artificial intelligence. We have a team with the relevant skills combined with real-world practical experience to help organisations identify opportunities and overcome challenges throughout their journey to hyper-automation.
To help achieve this, we have created three initiatives aimed at providing initial guidance and inspiration to organisations at each phase of their AI journey. Each initiative can be performed remotely (utilising collaboration technology) and quickly with outcomes being delivered within 3 – 4 elapsed weeks. The initiatives tackle challenges at each of the three phases of the AI journey, select the one most relevant to your organisation to understand more:
- Build – How to Install and successfully progress an AI programme.
- Scale – How to Intensify an existing programme and take it to the next level across the organisation
- Evolve – How to Infuse artificial intelligence into the DNA of the organisation.
Where an organisation believes they are progressing on their journey well, these initiatives can provide a health check, delivering reassurance to the organisation as well as providing some additional innovative ideas and thoughts to aid them accelerate their programme further.
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