Data & AI Accelerator
Building the right foundations to unlock the potential of AI
AI is all about data.
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Artificial intelligence has been around for decades in different guises but in the last 9 months we've watched as it’s caught the imagination of many through the launch and widespread testing of breakthrough generative AI like ChatGPT.
Generative AI was first seen in the 1960s with primitive chatbots. Fast forward to 2014 and the technology got a little bit better with the use of images and video. Then in 2017 things got really interesting when Google first described a new neural network model called a transformer.
Open AI was able to scale these models to work in real time and in parallel, with the outcome being ChatGPT - Generative Pre-trained Transformer.
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GenAI is only one of many AI tools.
Will Lowe
Chief Data Officer
If you unlock your phone with your face, you’re using AI. If you’ve had a Spotify recommendation, that uses Machine Learning, a subset of AI. If you’re looking at a Netflix suggestion, not only is it using AI to show you relevant content, it’s also using GenAI to tailor the thumbnail linked to that content, based on your preferences.
As you might expect the result of all this is more questions. And lots of them. Questions like: How is AI going to impact organisations, both as a potential opportunity and threat? Questions we want to help you answer.
The Data & AI Accelerator is our point of view on scaling AI-driven outcomes faster. We’ll look at how it’s developing and what its impacts might be, recognising that the end game is uncertain and will depend on some of the key future-thinking, ethical, legal and philosophical questions being more widely debated.
While our Data & AI Accelerator isn’t intended to be a definitive view, we expect it to spark the right debate about how we can help you to get started.
We'd love you to join the conversation.
With over 20+ years in data and analytics, Will Lowe shares his experience about:​
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The history of AI - it's not new
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Work we've done with clients in this area which means you can trust us to advise you
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The importance of testing and how people and AI can work harmoniously
If you’d like to know more about how we can help you to get your data ready and accelerate your AI journey, get in touch. We’d love to talk to you.
Are you ready for AI?
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Hugh Simpson
Data & AI Solutions Director
Introducing the Data & AI Accelerator.
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It's both an exciting and challenging time for businesses as they try to unpack what AI means for them.
Despite AI being around for decades, not all organisations have the foundational building blocks in place to unlock the full potential of data and AI.​ They’re awash with data, often making reactive decisions based on multiple sources of truth.
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Because no enterprise can do everything at once, we’ve devised a simple three-step approach that cuts through the hype, helping you to go back to basics.
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First we need to THINK BIG.
We start by understanding the impact of AI on your business and identifying the gaps between your current state and your ambition.
We then build strategic linkages between your business strategy and data and AI strategy to set the vision for the future underpinned by value-based use cases.
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Every great journey starts with small steps and AI is no different. Unpack your use cases and experiment with small proofs of value to not only test your data quality and infrastructure, but also to build support across the business by showing quick wins.
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The motto here is to act fast, iterate and continuously move forward in months, not years. Don't embark on a multi-year migration without proving value first.
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Moving onto the final step, you’ll take successful proofs of value forward, update your strategy and launch fast, scaling quickly. As you iteratively move forward on the roadmap, don't look to shortcut the need to establish robust governance frameworks that maintain the humanity in any new processes. Continuous integration of new use cases will enable you to reduce time to value whilst being increasingly able to capitalise on the benefits of proactive, automated and value-driven insights.
Data & AI Accelerator - Our step-by-step approach
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If you’d like to know more about how we can help you to get your data ready and accelerate your AI journey, get in touch. We’d love to talk to you.
Are you ready for AI?
Set the Vision
Think BIG and define your Data and AI Strategy
What's the impact?
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You can’t escape the impact of data and AI on your operations. Before you can set your vision though, you need to understand the risks to and impacts on your business.
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Not all impacts are created equal. Some may be critical areas that need to be addressed immediately, others more of a slow burn giving you time to assess.
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Some of the ways AI may impact your business could include:
Ian Pocock
MD Research & Service Design
Daniel Finnigan
Head of User Research
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Automation of routine tasks. AI can be used to automate many of the routine tasks currently performed by humans, such as customer service, data entry and fraud detection. This isn’t about replacing people but instead freeing them up to focus on more creative and strategic work.
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Improving decision-making. Using AI to analyse large amounts of data to identify patterns and trends that might be difficult for humans to see. This information can then be used to make better decisions about everything from product development to marketing campaigns.
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Personalising the customer experience. AI can enhance the collection of data about customer preferences and behaviours. This information can help to personalise the customer experience by recommending products or services that the customer is likely to be interested in.
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Creating new products and services. AI can make it easier to develop new products and services or enhance existing ones. For example, AI-powered chatbots can provide customer service 24/7 and AI-powered image recognition software can be used to identify objects in photos.
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AI in research and design has historically focused on user experience and the building of services.
Ian Pocock and Daniel Finnigan talk about how the technology could have a role to play in predicting future behaviours and the considerations needed to protect the user and their data.
Outcomes from the Transform Impact Assessment​
Identify gaps between now and ambition.
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Once you’ve thought about the sorts of ways AI could impact your business, it’s time to understand your current state. Leveraging Gartner’s AI maturity model, we apply our depth of experience to review your current people, processes, technology, data and governance to understand how big the gap might be between where you are and where you want to get to.
If you’d like to know more about how we can help you understand the impact to your business, get in touch. We’d love to talk to you.
Do you need an Impact Assessment?
What is the Data & AI Strategy?
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During our research, we’ve seen a lot of documents with the words “Data Strategy” or “AI Strategy” at the top. Only a few have actually reflected this simple definition:
"A data and AI strategy defines how an organisation achieves specific business goals through the strategic use of its data assets."
Transform Data & AI Strategy Framework
A data strategy sits between the overall business strategy and the data management or data governance strategy. It’s about how your organisation will maximise the leveraging of its data to generate the greatest business impact.
Understanding your key use cases and how they link to the strategy is accomplished through a thorough current state analysis, an honest assessment of where the organisation is today, across different departments reviewing data capabilities.
Dave Wood
Chief Technology Officer
Linking data and business through a current state analysis
As Transform CTO, Dave Wood, understands the technology better than most and how a clearly defined data and AI strategy can:​
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Deliver success if considered in parallel with people
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Help an organisation to embrace AI quickly and competitively
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Address your important use cases if you ask the right questions
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Support an ethical approach based on making conscious decisions around the use and governance of data
What should be in your Data Strategy and what are the benefits?
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A holistic Data and AI Strategy is more than just slideware. It needs to incorporate practical actions including experimentation through proofs of value. In addition to the traditional people, process and technology components, data governance is critical to ensuring data quality and value-based outcomes comply with all relevant legal and security requirements.
The ultimate goal of an integrated Business, Data and AI strategy is to deliver a platform for automated and predictive insights which enable proactive decision-making.
One of the key tools in doing this is the development of a strategic linkages model to connect business, tech, data and AI strategy to the right use cases.
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Cyril Law
Director, Technology & Data Engineering
Benefits of a Data and AI Strategy include:
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Making faster, better decisions that execute business strategy​
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Improving business agility to respond to change​
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Improving customer-centricity​
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Being able to identify and seize new opportunities
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Redirecting resources to focus on value creation with AI and automation​
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Transparently showing value every step of the way​
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Transform’s Data & AI Strategy Framework helps our clients to spend less time on fancy slideware and more time delivering value.
After 20+ years in the technology space, Cyril Law is well placed to advise on:
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How to identify the business needs for and value to be gained from using AI/ML
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Delivering AI capability in line with existing tech stacks and organisational KPIs
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Tech from an agnostic perspective
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Bringing disparate data sets together into a single source of truth
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Governance requirements when using openAI or chatGPT
If you’d like to know more about how we can help you to get your data ready and accelerate your AI journey, get in touch. We’d love to talk to you.
Are you ready for AI?
Start Small & Launch Fast
Act fast, iterate and continuously move forward
How do you experiment? Why is it important?
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Competition for finite resources within an organisation often means that spending (capex or opex) is limited to those projects that have a quantifiable value assigned to them. Machine Learning and AI projects can struggle to guarantee a defined return in the short-term, making it more likely they’ll be pushed to the side of the desk.
Difficulty in quantifying the outcome of an ML/AI solution could be because of the technology required, data quality, type of data (structured vs unstructured) or the quality of the use case being addressed.
The use case or problem statement is often the hardest part in the whole process. However, when it’s aligned to the business strategy and validated as a problem worth solving, you’ll then have a solid foundation for experimenting. ​
Michael Baines
Head of Data Science & ML/AI
Experimenting is an essential part of using data, ML and AI to explore where you can learn more about your data and the art of the possible in solving your defined challenge.
Taking into account our approach to AI for Enterprise, we recommend the following when experimenting with AI:
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Begin with a small problem. When you’re first starting out, it’s helpful to identify a small problem that you can solve relatively quickly. This will help you to gain experience with AI and to identify the types of experiments that are most likely to be successful.
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Use a variety of datasets. When you’re experimenting, it’s important to use a variety of sizes and types of datasets. This will help you understand how the performance of your AI models varies depending on the data that they’re trained on.
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Be patient. Experimenting with AI can be time-consuming. It’s important to be patient and persevere with your experiments. With time and effort, you’ll eventually find the best solution for your problem.
Michael Baines has worked in data and analytics for 20+ years and knows how experimenting with AI can supercharge plans:​
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Helping to overcome any reluctance the business may have around the use of AI
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Taking advantage of technological advancements to test approaches at pace
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Aligning business understanding with technical expertise
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Focusing on small relevant use cases with a robust governance process
Transform and Health Education England were the winners of the Health Tech Awards 2022, Best Use of AI and Automation Tools.​
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Transform and Health Education partnered to use data and advanced analytics to deliver breakthrough insights and build confidence in AI.
The NHS faces enormous challenges in meeting workforce demand. Recruitment is not enough; training lead times are significant, taking seven years and £250,000 for junior doctors, doubling for certain specialties. Therefore, reducing attrition and increasing attainment through targeted support are critical missions for Health Education England (HEE). They had insight on the drivers of attrition for trainee doctors but didn’t understand the unique blends of drivers for each individual, so Transform prepared data into a modelling dataset, collated and cleaned it, and fed it into a number of supervised machine learning classifier algorithms. This allowed a model to be developed to provide an attrition prediction for a trainee at the start of each placement, so that Health Education England could successfully identify individuals with a high propensity of attrition and better understand possible intervention approaches.
If you’d like to know more about how we can help you to get your data ready and accelerate your AI journey, get in touch. We’d love to talk to you.
Are you ready for AI?
Launch fast with a data-driven culture
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Building a data-driven culture at all levels means your business can take a more iterative and agile approach that embraces failure. Understanding the data maturity of your business is the first step to implementing a culture change programme to support accelerated adoption of AI-driven processes.
Data maturity isn’t just about your systems and processes, it’s also about people. While it’s tempting to buy new, expensive solutions, culture change requires a lot more nuance. If businesses really want to become more data centric, then they need to start with their people. Transform offers three tips to help move this culture dial.
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​1. Data-driven culture starts from the top
You need to lead by example. Do all your leaders believe in using data to support decision makers?
Meghan Walsh
Change Consultant
How can you expect others to adopt a data-driven mindset if it’s not replicated from the top? This is more than simply sharing the top-line data strategy. Leaders must take responsibility in allowing a test, fail and learn approach within their teams, particularly when it comes to adopting a more data-focused way of working. Balancing the risks of adopting this approach is key, as well as championing breakthroughs and proof points.
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Recognising and rewarding employees who demonstrate a commitment to data-driven practices can reinforce the importance of a data-centric culture. Celebrate successes, share stories of impactful data-driven decisions and create incentives that motivate employees to actively engage with data.
By acknowledging and appreciating data-driven efforts, leaders encourage others to follow suit.
2. You have to understand where you’re starting from
Businesses tend to focus on processes and infrastructure before people, however we encourage leaders to start with the people elements and keep them at the centre of decision-making.
Employees should be equipped with the necessary skills to interpret and analyse data effectively regardless of their roles. Investing in training programs, workshops and resources to enhance data literacy among employees, regardless of their job roles empowers individuals to make data-informed decisions and unlocks the collective potential of the workforce.
Having worked with multiple organisations to implement cultural change programmes, Meghan Walsh has the following insights for the AI revolution:
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People demonstrate a data-driven culture, not glossy ppt decks
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Leaders need to be comfortable with risk and failure
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Data maturity includes systems and processes but starts with your people
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Appoint data champions to help drive change internally
Don't forget to bring humanity into AI.
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Artificial intelligence is rapidly becoming a part of our everyday lives. From the smartphones we use to the cars we drive, AI is all around us. As AI continues to evolve, it’s important that we design humanity into these systems.
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AI systems that are designed with human values in mind are more likely to be:
Ian Pocock
MD Research & Service Design
Daniel Finnigan
Head of User Research
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Ethical and safe. AI designed with the value of fairness in mind will be less likely to discriminate against people based on their race, gender, or other factors.
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User-friendly. AI designed with the value of empathy in mind will be more likely to understand and respond to human emotions.
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Beneficial to society. Building AI with the value of sustainability in mind can help us to reduce our environmental impact.
The way we design humanity into AI is to use human-centred design methods. Simply put, this means understanding the needs and wants of users and then designing systems that meet those needs. This approach can help to make sure that AI systems are designed in a way that’s beneficial to those people that actually use them.
The future of AI is indeed looking bright, but it will be even more so if we design humanity into these systems. By doing so, we make sure that AI is used for good and benefits everyone.
AI in service design can provide intelligent signposting to aid the user journey and reduce friction points.
Ian Pocock and Daniel Finnigan talk together about how trust and verification will be even more important in AI driven services.
