From the clients we have been speaking with it seems like everyone is at a slightly different stage of their AI journey.
Many companies are now exploring and identifying how the application of automation, artificial intelligence and data analytics technologies can provide significant business value and competitive advantage. However, few companies have the internal resources who fully understand the wide complexities of using AI and machine learning in an organisation, especially when the desire is to benefit from these technologies at scale across the entire operations of the firm.
We understand that each company is at a different point of their own AI adoption journey, and that at each stage there are a multitude of both challenges and opportunities that must be navigated. An increased focus on digital transformation, automation and data analytics has resulted from the forced social distancing, remote working and business stress due to the pandemic. We see demand for an AI strategy to ensure the investment in digital transformation is put to best use and creates actionable and valuable results.
AI adoption is a complex journey
We have identified ten stages of the AI adoption journey, and the requirements for your AI Strategy will differ depending on your current stage.
The roadmap for these ten stages is detailed in our AI Journey article, which also highlights some of the challenges at different stages. Setting out a plan and strategy at the early stages of your AI adoption will really help accelerate your adoption, correct any problems encountered already and ultimately help maximise the benefits from the digital transformation investment.
An AI Strategy turns challenges into opportunities
A detailed AI strategy will be based on the current use and experience with the technologies and the ambition to move forward for successful deployments at scale, helping you navigate towards the later stages of AI adoption.
You maybe just starting, exploring via a few proof-of-value experiments to understand the challenges and value in using Machine Learning to improve the processing of a task. You might have a number of data science teams working across the organisation delivering a range of predictive algorithms, chatbots and other AI capabilities into various parts of your organisation.
However, it seems evident from the research we have done, that few if any organisations are operating as efficiently as they could be in terms of delivering AI applications to the business. For organisations that have already started exploring the use of AI capabilities, we not only see the advantage of producing an AI strategy, but also having a focus on aligning the current operations to the strategy recommendations. This is the real value of having an AI strategy.
We believe not having an AI strategy is a potentially huge mistake. An AI strategy is going to become a significant part of any company’s technology strategy given the range of AI technologies and methods can be applied across the entire company value chain and for both front and back office operations.
A comprehensive AI strategy will include many different aspects, from alignment to the company business strategy to organisational structure, operational concerns, culture, governance and ethical considerations as much as the underlying technology platforms, frameworks and tools. The AI Strategy will help accelerate your adoption of AI across your organisation in a way that allows for a smooth transition from innovation pilots to full scale production capabilities. It will also both help identify and solve many of the common challenges of adoption of AI we see in other organisations, making your AI journey smoother and reducing the friction of adoption.
Without a strategy, organisations are most likely to setup multiple data science teams, most likely aligned with organisational boundaries making them siloed and disconnected from each other. This creates problems that if not addressed early enough will cause significant problems longer term. They will also potentially miss the need for governance and ethical oversight as the rollout of algorithms increases.
Key Elements of an AI Strategy
Having a strategy is the keystone of organisational AI delivery and covers a range factors. The six key areas for an AI strategy;
- Use-cases, Prioritisation and Business Alignment
- Technology, Tools and Infrastructure
- Process and Workflow Frameworks
- People, Culture, Skills & Training
- Governance, Audit, Transparency & Best Practice
- Innovation for New Products & Services
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Often, the AI adoption is complicated with other related programmes and projects. From cloud migration programmes, functional change projects, big data consolidation, platform and application upgrades, to underlying data quality issues. Many of these projects will take priority or be used as an excuse to delay the adoption of AI capabilities. However, the truth is that early adoption of machine learning and data analytics can actually both support and accelerate some of these other project activities.
The AI strategy will not only identify opportunities within your organisation to use machine learning and data analytics to improve efficiency and accuracy while reducing overall costs across the business value chain, both front office customer engagement and back office operations, but can open up the possibilities of new products and services only achievable with the capabilities of AI. This is moving from digital migration to proper digital transformation and opens up the real value generation for businesses applying AI within their business.
At Combined Intelligence we can support the creation and implementation of your AI Strategy. We have helped many companies understand the benefits and challenges of deploying AI at Scale across the organisation.
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About the author
This article was written by Prof Andy Pardoe, CAIO & Chair of Combined Intelligence. Andy has many years experience with Big Data transformation and migration projects together with delivery of analytics and AI solutions.
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