One could be forgiven for feeling despair at the current state of the world. It was, unsurprisingly, a persistent theme at both events, with speakers painting a rather stark picture through the lenses of health & wellbeing, the economy and the environment.

It is clear that we have sufficient evidence to make us confident that embracing rapid change is the only sensible direction of travel – as highlighted in the keynote presentations that I’ve summarised below. It’s also evident – from my LinkedIn feed for starters – that there are lots of people and organisations with all sorts of innovative ideas and practices that offer us the chance to make positive radical change immediately.

So in the spirit of Williams, I want to use this article as an opportunity to make hope possible, and as a call for more meaningful innovation to support healthy places.

We know that we must act now and not let opportunities for positive impact slide from one month to another.

Unhealthy cities and climate catastrophe

Professor Sir Michael Marmot set the scene at Healthy Cities with a contextual analysis of the poor health of UK citizens today. He presented a wealth of evidence that showed significant declines in both public sector expenditure and in public health since 2010. Data showed that increases in life expectancy at birth have stalled in England, with health for the poorest people getting worse. Amid a decade of the government’s austerity programme, public sector expenditure shrank from 42% of GDP in 2009-10 to 35% by 2018-19 – which is enormous. Marmot described austerity as an “economic choice, not an economic necessity”.

Marmot’s work is concerned with the social determinants of health and he continued to paint a clear picture of the current situation in the UK. Real wages are lower today than 18 years ago and have fared much worse than our peer nations. Total government spending on healthcare per person, taking age into account, has been falling year-on-year since 2010 – we’re spending less than ever before. Austerity saw a decreasing amount of money being made available to local government; nearly one in five English Councils are at risk of bankruptcy according to the Financial Times. The tax burden in the UK (about 34% of GDP) makes us a low tax country in comparison to G7 and EU14, despite the political narrative claiming the contrary. Without London’s economic might, the national GDP of the UK drops by 14%, which means that we are as poor outside London as the poorest US state, Mississippi. We are, as Marmot pointed out, “a poor country with some rich people”.

Professor Dieter Helm, meanwhile, used his keynote at Said Business School to focus on climate change, during which he said “anyone who thinks we are cracking climate change must come from Mars”. Indeed, 80% of the world’s fuel is still fossil fuel and sustainable innovations are not without their own issues. Electric vehicle supply chains, for example, still cause significant environmental damage and negatively impact biodiversity. The scientific evidence behind this is overwhelming, as explained in this article on MIT’s Climate Portal and this Guardian series examining EV myths.

Helm also honed in on the poor state of Britain’s core infrastructure after 30 years of privatisation; energy networks, water utilities, natural ecosystems and a decent climate to name but a few. He argued that we need to look ahead to the next generation and ensure we can hand over working and viable infrastructure to them. If we can do that, which will involve creating more frameworks and increasing investment, then we have done all we can realistically do to enable resilience for future generations.

I think we can all agree with Dieter Helm (and so many other experts worldwide) when he says that there is an urgent need to sort out the mess. I said this would be an article of hope over despair, so now that we have some shared high-level foundational context – admittedly bleak – let’s move on in a spritely fashion to some of those opportunities for radical change!

Investing in our cities

Impact investing – investing with intentionality for people and the planet – is emerging as a major trend within the financial markets. Its global market value of over $1.1tr is firm proof that businesses are investing with more purpose. Kieron Boyle, chief executive of Impact Investing Institute, was speaking at Healthy Cities to explain why impact investing is becoming one of the world’s most credible tools for addressing cities’ key challenges – including achieving health equity.

Considering the significant influence businesses have on our health and wellbeing, and are themselves affected by the decisions of investors, there is a compelling case for businesses to prioritise health and wellbeing. If commercial activities contribute to prevalent public health issues such as smoking, harmful gambling and poor diet, then it follows that there are direct adverse impacts on the workplace, such as increased sickness and absence, reduced productivity etc.

It was encouraging to hear that investors are considering how to make healthcare more accessible and looking into the broader spectrum of social determinants of health – affordable housing, environmental health, food and nutrition, financial inclusion, community services, etc.

Place-based impact investing, Boyle explained, which focuses on the strengths and needs of a place, is also gaining attention and paving the way for a brighter future for urban areas. Earlier this year, South Yorkshire Pension Authority announced the development of a new Place Based Impact Investment Portfolio with plans to invest £500m in the region over the next five-10 years.

A key challenge for impact investing in urban health, however, is that it still lacks models for investment in urban health equity. Additionally, governments have been slow to take advantage of this capital so far and there are inherent risks in aligning public and private interests.

While organisations like GRESB and The Good Economy are already providing data and benchmarks for measurement, if impact investment continues on its current trajectory more will be needed to support responsible decision-making in the long-term.

What is bold?

This is a question that often causes contention. Indeed, it was no different on a panel at Healthy Cities while discussing the Liverpool Green Lanes Provocation Project, which was awarded the ‘Most Innovative Idea’ at the conference.

The challenge for many cities is to make existing, often outdated, urban neighbourhoods more liveable, resilient and healthy, which this project seeks to address. The overarching ambition is to catalyse large-scale greening, promote healthier living, active mobility, place activation and community engagement, among other interventions to deliver liveable city principles at scale. The outputs will ultimately be used to show how healthy city principles can be developed and applied in major urban regeneration projects, providing a benchmark for future initiatives.

So while some did think it was ‘bold’, referencing all of the regulations, frameworks and stakeholders that had to work together to make it happen, others disagreed. Graham Marshall, director of Prosocial Place, called it an “ordinary” idea. While it is a great and important idea, I feel inclined to agree… This sort of urban innovation should be viewed as standard practice in 2023, not a risky and forward-thinking idea.

Thought

Since the conference, I have been thinking a great deal about what ‘bold design’ means in terms of reshaping an existing part of a city – and particularly the role of digital technologies. We’ve established that radical change needs to happen quickly, for which technology is a fundamental enabler, but it must be done in a way that avoids negative disruption and harm to members of the public. Just think about the process of rapidly imposing e-scooters across our major towns and cities, and the results. Then notice that you don’t actually know ‘the results’ and ask why? In Paris, its citizens recently voted to ban rental e-scooters due to their negative impact on the environment. FYI, I’m not anti-e-scooters at all.

Much more thinking and discussion needs to happen to reach agreement on what constitutes a bold idea, bold design or a bold approach to place-based practice. But, we can address the question, “How can we be bold and innovative in a careful and responsible way?” For me, it all comes down to balancing the vision or the purpose with an ‘ethical by design’ approach and embedding that into projects from the get-go. Rather than seeing ethics as a limit to innovation, rather, we should all actively use ethics as part of a bold approach that considers the impact of our actions on people, place and the planet. We need to be bold by being aware of potential long-term as well as short-term effects of place-based innovation and acting accordingly – all of which swings back to impact investing, discussed earlier.

Scale: small investment or embedded long-term investment

As with the debate around what constitutes bold design, there was a lack of consensus regarding the most effective approach to projects: is it more effective to create a larger scale project or to start quickly with small interventions and scale them up?

A key point of discussion was around maintenance and funding. It was argued that small-scale projects are often seen as quick wins but often lack the means to continue. The message being that whatever you’re doing in the city needs to be embedded in the vision, strategy and objectives if it’s going to be delivered within the city and for the long-term investment required.

This links back to a point Dieter Helm made about maintenance and needing to sustain and improve existing infrastructures now, for the next generation. If there are no well functioning infrastructures in place, and no money for maintenance, then projects are much more likely to fall apart – whether it’s a community service or something physical such as water infrastructure or sewage systems. Ultimately, these things need to be running in tandem.

A delegate also highlighted the critical role of undertaking public impact assessments, as well as social, strategic and economic impact assessments – for both short- and long-term projects/programmes. Just as important, in my opinion, is community engagement and valuing community wisdom to influence where the money goes. These are the people you are ultimately investing in, and listening to them will help to unearth insight that is often missed when taking a top-down approach.

Thought

The winner of the President’s Award at the 2023 Landscape Institute Awards and ‘Excellence in Public Health and Wellbeing’ category is a great model of practice and demonstrates the power and value of community wisdom and collaboration.

Rooted in participatory and co-creation principles, the project from City of Bradford Municipal District Council was praised for challenging public health inequalities across the district, integrating blue and green infrastructures to highlight the role of the environment in health outcomes. I see it as a useful model that shows a bridging approach; one that starts with small investment and leads to embedded long term investment.

Carolin Göhler, President of the Landscape Institute called it a true exemplar of “how landscape can benefit people, place and nature with good community health research, policy making, design, and implementation, with close community liaisons and management of green spaces.” As one of the judges for this category, and who represented Bradford’s submission at the President’s Award deliberation, it was great to see first-hand some of the brilliant work taking place in Bradford. Let’s hope this one sets a precedent for other councils to follow!

Parks as core critical infrastructure

Parks are fundamental aspects of our towns and cities; they are places we walk, run, play and do sport; places that support biodiversity and promote air quality. They are intertwined with many social, economic, cultural and environmental factors that can have a positive impact on both people and place. Yet they are still so often overlooked, neglected, actively avoided or exclusive to certain groups – whether for safety concerns or through their design.

It was great to have a whole session dedicated to parks at the Healthy Cities conference, where I was invited to speak about Calvium’s work to support investment in parks for the City of Edinburgh Council.

The research project involved studying each of the city’s 149 parks to establish which could benefit from investment in sensitive lighting to support active travel. Geospatial and place-based data were analysed for each park in order to inform a ranked table of recommended parks. By lighting paths after dark, the aim is to enhance the perceived safety of parks and encourage their use as sites of connection. In doing so, it is expected more people will adopt active travel and the city will benefit from the associated health, economic and environmental benefits.

This is just one example of how a simple change can make parks more hospitable places for people to move through. Fortunately, there are many global projects underway that showcase how inclusive, collaborative design can promote greater health and wellbeing – from supporting gender diversity and mental health, to boosting safety and biodiversity. Read about them here.

Conclusion

From investing with purpose to ethical design and community wisdom and more, this article has shone a light on many innovative ways that we can create healthier and more resilient cities, today and in future.

Ultimately, the onus is on us – the placemakers, digital innovators, local authorities and citizens – to spot the opportunities where innovation could benefit the health and wellbeing of citizens and nature. It is up to us to identify both the small, quick wins and the longer-term investments to enable better physical and mental health, to improve safety and access to green spaces, to futureproof urban infrastructures…These are not radical ideas, they are simply reasonable and achievable – which is really great news!

Change is happening, and it is inspiring and reassuring to see. But time is of the essence and we need all hands on deck to ensure it happens sooner rather than later. As long as it is done with people, planet and place in mind, we have more than hope.

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In short, “Surviving and Thriving in the Age of AI: A Handbook for Digital Leaders” is specifically designed for busy professionals, Digital Leaders and decision makers wanting to know more about AI’s issues and impact. It is organized to allow the reader to gain valuable insights quickly and on demand. Above all, the content is grounded in real-world experience and practical applications within the context of digital transformation, ensuring its relevance to everyone who is embarking on a digital journey. Most importantly, each topic concludes with thought-provoking questions and actionable steps to guide your personal exploration of AI and its implications.

So, what’s it all about? Here’s a summary of the main elements of the book. I’m looking forward to hearing your feedback. Hope you enjoy it!

Surviving and thriving in the age of AI: A handbook for Digital Leaders 

Opinions on AI are everywhere these days. From news headlines warning us how robots will soon be taking our jobs to academic papers describing the latest image analysis advances, the promise and perils of AI are being described and debated. Yet, with the breadth and depth of AI disruption it’s all too easy to feel lost in the technical details, overwhelmed by the barrage of announcements, and confused by the implications for business and society. How can we make sense of it all? What are the key aspects of AI that will determine its impact on our lives? What does it mean for leaders and decision-makers looking to accelerate digital transformation strategies and redefine their organisations to be ready for the age of AI?

I have written this book to address these questions. “Surviving and Thriving in the Age of AI” cuts through the hype and offers a practical roadmap for navigating the exciting (and sometimes confusing) world of AI. It takes the reader on a journey through the principles and practice of AI to provide a handbook for becoming a digital leader in the age of AI.

While it covers many elements, here I’ll unpack the three key themes that form the core of the book: Separating the AI reality from the hype; Assessing AI’s impact on digital transformation; and Delivering AI-at-Scale.

Separating AI reality from hype: How to stay head with AI

Let’s be honest, much of what we see today makes AI sound like a cross between magic and science fiction. The book encourages us to step back from the sensational headlines and presents the core elements you need to develop a critical eye.

• Critical Thinking is Key: Don’t be fooled by marketing speak. Leaders must learn to evaluate AI technologies for themselves to ask better questions and place AI developments into context. A framework for critical thinking is developed throughout the book. Stay informed about advancements, but be wary of overly optimistic predictions.
• Education is Power: All of us must invest in understanding the basics of AI. Digital leaders have a particular responsibility to educate themselves and their teams. This empowers them to see through the hype and identify how AI can specifically benefit their industry. The book highlights where to concentrate your educational efforts.
• Data is King: Don’t base decisions on empty promises. The book emphasizes the importance of data-driven decision making based on a resilient data engineering infrastructure. Leaders gain deeper insights from a mature approach to data and look for real-world case studies to test, evolve, and scale their AI solutions.

Assessing AI’s impact: A key weapon in your digital transformation arsenal

The evolution of AI brings new capabilities and opportunities to digital strategies already underway. The book positions AI as a powerful tool to extend your digital transformation journey. But only if AI is considered in the context of well-governed change management practices that focus on helping people to be more efficient and effective.

• Integration is Key: AI isn’t a standalone solution. It should seamlessly integrate with your existing digital infrastructure to maximize its impact. The book encourages you to think of it as an upgrade, not a complete overhaul.
• Boost Your Capabilities: AI can supercharge your data analysis, customer service, and operational efficiency. The book provides principles and real-world examples to show how companies are leveraging AI to achieve impressive results.
• Embrace the Experiment: Change can be difficult for organisation with established products, systems, and structures. Fostering a culture of innovation is crucial for success with AI. A key emphasis of the book is to encourage experimentation, learn from failures, and use those lessons to inform future efforts.

Delivering AI-at-Scale: Expanding AI across your organisation

Making the decision to bring in new AI tools and systems is just the start. Taking an AI project from pilot program to company-wide success story requires careful planning and substantial investment. The book provides the basis for you to create a roadmap to make this leap:

• Start Small, Win Big: Adopting AI can have major implications across the organization. Don’t try to boil the ocean. Begin with focused pilot programs in controlled environments. This allows you to test the waters, iron out kinks, and understand the potential impact before full-scale implementation. But the book helps you to recognize this is only a first step.
• Build a Strong Foundation: AI will place pressure on existing systems and skills. Ensure you have the technological and organizational infrastructure to support AI at scale. This includes robust data management systems, powerful computing resources, and a skilled workforce.
• Change Management Matters: People are key to successful AI adoption. The book uses case studies to show how leaders can communicate the vision clearly, address any concerns, and provide proper training to ensure everyone is on board.
• Continuous Improvement is the Focus: AI isn’t a “set it and forget it” solution. The book emphasises the importance of continuous monitoring and improvement. Establish feedback mechanisms to track AI performance, learn from outcomes, and refine your AI applications for maximum effectiveness.

Beyond surviving to thriving with AI

All organisations are now looking to AI to reinvigorate their digital strategy. It is by focusing on these three themes that “Surviving and Thriving in the Age of AI” equips leaders and decision-makers with the knowledge and tools they need to navigate the world of AI with confidence. By understanding the true potential of AI, leveraging it to extend your digital transformation efforts, and scaling AI solutions effectively, you can move from surviving to thriving in the age of AI. This is the handbook that Digital Leaders need to help you to make that happen.

If you would like to learn more about the book, get a signed copy and see me in conversation with Macmillan Cancer Support CIO, Roxanne Heaton you can join us on 23rd July at DLHQ 

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Despite the potential of tools such as data lakes, sensors, and monitoring systems for our built environment and housing stock, both public and private organisations tasked with shaping our physical landscape have been resistant to change. Whether it’s the ballooning budgets of projects like HS2 or the staggering expenses tied to planning documents for the Lower Thames Crossing, it’s evident that the current approach to place and infrastructure isn’t delivering results.

The heart of the problem is how the industry perceives digital transformation as a second thought. This is especially evident when considering technologies like sensors, decision-making dashboards and AI analysis, which enter the picture after policies are set, ground is broken, homes are built or funding is finalised. When these digital initiatives fail to address problems as promised, they’re swiftly abandoned and people fall back to the safety of familiar, traditional ways of working.

But the real power of technology doesn’t reside solely in the data, tech, gadgets and platforms but in purposefully designing and crafting policies, projects, and programs with the digital world in mind from the outset. Only by reconceiving our strategies for designing and executing place, housing and infrastructure projects can we genuinely boost productivity, promote housing growth and cultivate the societal progress we desperately need.

Embracing innovation

Place, housing and infrastructure projects are pivotal to ensuring our society can function. But remain trapped in traditional delivery models f dominated by fields like engineering, surveying, planning, and architecture. Sectors such as banking and healthcare have embraced digital transformation, but when it comes to those working on our built environment, the industry has fallen behind. Without substantial changes, we risk our economic prosperity, continuing our housing crisis, and the well-being of future generations.

To overcome these blockers to innovation, a shift in mindsets is desperately needed. Conventional approaches, marked by inflexible systems and siloed expertise, have resulted in costly setbacks and inefficiencies. We need to embrace agile methodologies that nurture innovation and foster collaboration from the outset. By bringing together design, data, and technology experts into both policymaking and project delivery, we can inject fresh perspectives and create meaningful transformation.

Rethinking policies

One of the fundamental issues with current ways of working is how reactive policy formulation and funding distribution are. Decisions are too often made without considering the real needs and challenges faced by those delivering on the ground or the communities living there. Funds allocated for place and infrastructure projects frequently go unused or fail to achieve their goals due to poorly guided assumptions and a lack of recognition that projects, forecasts and outputs inevitably change. 

To fix this, we need to adopt an end-to-end approach to policy, taking into account implementation challenges right from the start. Collaborative efforts across governmental bodies and greater financial decentralisation are crucial for empowering local stakeholders and optimising the impact of investments.

Data must also play a central role in guiding these decisions. By harnessing standardised digital frameworks and embracing data-driven decision-making processes, we can ensure that resources are directed where they’re most needed and that outcomes are accurately evaluated. To do this, we must provide local authorities and other stakeholders with the necessary tools and skills to collect and analyse data on delivery, policy outcomes and impact throughout the project lifecycle, enabling them to make continuous enhancements and adaptations.

Accepting complexity and uncertainty

In a landscape defined by challenges, complexity and dependencies, outdated approaches founded on false assurances are no longer tenable. Rather than depending on inflexible methodologies, we must embrace a mindset of humility and exploration. Agile principles provide a roadmap for navigating through uncertainty, enabling us to evolve and adapt as we go.  We also need greater transparency and accountability in technological utilisation, including a commitment to investing in ethical AI models that prioritise openness and public confidence.

Working with people

Finally, genuine interaction with the public needs to be at the heart of successful placemaking and housing infrastructure initiatives. Citizen involvement is too often superficial or steered to fit predetermined objectives. To create greater trust and credibility, we must overhaul the structure and methodology of public engagement and focus on democratic principles and subsidiarity. This means offering opportunities for people to give authentic input at regular stages of the policy and project lifecycle. It also means instituting feedback channels to guarantee that decisions are shaped by the community’s needs and preferences.

The obstacles confronting the built environment and infrastructure sectors may seem daunting. However, they certainly aren’t insurmountable. Through design thinking, the adoption of agile methodologies, and citizen engagement, we possess the capacity to construct communities that are not only more efficient and resilient but also more equitable. This transformation hinges on shifting our mindset from regarding digital as a mere assortment of tools to recognising its role as a powerful agent for transformation and change. Only through this shift will we unlock the potential of our place and infrastructure projects and build the future we need today. 

Read More Innovation & Collaboration

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With progress plateauing, in some cases even reversing, and a reported gender pay gap in tech that could take 300 years to fix, how do we move forward?

In tech, women’s journey to the top is often challenging, requiring not only hard work and talent but also support from those already in positions of power. Yet, a troubling trend persists and I hear it myself through our work, where individuals, once they have climbed the ladder of success, often pull it up behind them, preventing others from following. 

This behaviour undermines the collective progress and stifles potential talent.

Here are some of the latest numbers.

The number of women working in engineering and tech has dropped by 38,000 – from 16.5% of the 2022 workforce to 15.7% of the 2023 workforce. In contrast, women made up 56.1% of all other occupations combined. The decrease in the number of women in engineering and tech is largely due to a loss of 66,000 women aged 35 to 64, highlighting retention issues in this age group.2

The TechTalent Charter’s final Diversity in Tech 2023 report, indicated that 1 in 3 women in tech were planning to leave their current jobs. 

The report also revealed that diversity and inclusion strategies are becoming increasingly insular with initiatives being sidelined to focus on other business goals. D&I budgets are being squeezed – and in some cases, cut entirely – whilst the demands of those in D&I roles are constantly expanding to include non-related work.

The British Computer Society has warned that, without intervention, it could take nearly 300 years to close the gender pay gap in tech.

For me this is a wake up call to re-energise our efforts to improve gender diversity in tech and achieve faster progress.

I believe that one key way to boost retention and create more opportunities for women in engineering is for leaders to hire people ‘better’ than you. To clarify, this does not necessarily mean those who you believe could do your job right now, but instead hiring those who possess skills in areas you don’t and to increase your team’s diversity. 

As a leader, it is not your job to be ‘better’ than the rest of your team, in fact, it is the opposite. The best leaders surround themselves with the strongest and smartest they can find. Employees greater expertise and specialised knowledge will not only help business progression but your individual growth. 

These colleagues are a fountain of knowledge, and a good diversity of perspectives can support your continuous learning. They can open your eyes to new approaches and ways of thinking that you would not have been exposed to otherwise. 

Also, In terms of retention, your reliance and appreciation of them as team members allows individuals to feel heard, appreciated and feel they are making a substantial difference in the company. As Steve Jobs said “It doesn’t make sense to hire smart people and tell them what to do; we hire smart people so they can tell us what to do.”

An environment where employees are encouraged to challenge and learn from each other appeals to women in my opinion and will be a key element of the push for greater workplace equality and inclusiveness. 

On top of this good leadership practice, all the evidence clearly shows that organisations with higher gender, socio-economic, disability and ethnic diversity consistently outperform their competitors. 

These orgainsations better reflect social diversity, reach a wider range of potential customers, and incorporate a broader spectrum of perspectives into their strategy and decision making. Inviting more people to the table and ensuring their voices are heard benefits everyone.

If there are so many benefits, then why don’t more employers look to hire ‘better’ and more women? As Cameron Jacox argued in Forbes, the reason is likely rooted in millions of years of evolution. Humans have been programmed to resist threats and protect their own means of survival, in this case their jobs. 

The fear that opening your company’s eyes to the strength of the talent pool will lead to your replacement is very common. For if a new hire could do your job, why would the company keep you? It is perhaps worth remembering that only in a toxic and dishonest organisation would a well-intentioned hire lead to your replacement? 

In a merit-based, capitalist society, self preservation often requires less “defence” and more “offence”? Leaders need to out-compete and innovate rather than simply defend their own position and this alone is a good reason to diversify your team given the evidence. 

In reality, the chances are that hiring smart, diverse and hardworking people will only be a credit to you if you are in the right company – a reflection of your good judgment so please go for it and remind others you work with to lift as you climb.

So support me in highlighting the current worrying trend for D&I initiatives and join me in encouraging leaders to do the right thing for sound business reasons.

Originally posted here

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Regulator intervention and consumers voting with their feet will radically change how companies respond to (or ignore) their customers’ inquiries. Many companies have made a cost-cutting business decision to block customers from reaching a live person for assistance, and others simply lack adequate customer services operations. Either way, customers in search of help are often frustrated by the avoidance tactics or lack of response. These unsatisfactory customer experiences come with tangible costs, such as: negative reviews, customer churn, and missed revenue opportunities. In addition, customer service agents are placed in the uncomfortable position of facing unnecessarily upset customers, while businesses face difficulties in retaining contact center employees.

It’s hard to be human

Stress and unreasonable demands place many agents in an untenable situation. They’re expected to juggle several chats at once and to close calls quickly. Most customers expect agents to know who they are and be aware of any previous issues or interactions. But often, agents can’t easily access that type of information, which adds to the stress of the job. After months of training, many still aren’t equipped to handle the cases they encounter without additional help. Even with more experience, agents are often powerless to solve the business process issues they face. It is no wonder that many leave soon after starting.

Is new technology the answer?

Because generative AI LLMs can feel like the magic bullet, they are capturing the imagination of businesses and individuals alike. Who wouldn’t want an all-knowing entity solving problems as they arise? However, interacting at length with ChatGPT, Google’s Bard, or any of the increasing number of model options now available begins to reveal their flaws. They have a predilection for hallucinating (producing false results), lack contextual understanding, access outdated information, and raise security concerns for many businesses. Despite the risks and pitfalls of these generative AI tools, they’re revolutionizing the customer service space and can, with the correct application, support improved interactions.

Bots alone can’t cut it

Even before generative AI was unleashed on the world, integrated, outcome-based bots had evolved to effectively handle high-volume requests, but were transferred to a human for anything that fell outside their intended purpose. The newest generation of generative AI understands requests better, which can make a significant difference in accomplishing a task correctly. However, because responses can’t always be trusted, companies should avoid allowing generative models to answer customers directly. While they can take a first pass at answers, responses should be validated by humans before being sent to the customer.

Generative models make it feasible for companies to gain full insight into what customers most frequently ask for, how they ask for it, and the corresponding agent responses. Automating the bulk of categorization and labelling work, along with learning the words and phrasing customers are using, enables training of company natural language processing (NLP) for better recognition. In addition, LLMs are great at figuring out alternate phrasings to further improve recognition rates and can modify response style depending on customer sentiment.

Better together

Rather than replacing agents or adding demands that force agents to constantly juggle more work faster, generative AI platforms can help lighten their workload. Agent-assist technologies provide suggestions for how to help customers and shorten agent training and response time, which increases their confidence and performance, and improves overall job satisfaction.

Agents have the deepest insight into where business processes are weakest and, when they leave, their insight leaves with them. Enabling agents to improve their workplace can help retain them and capture the value of their experience. In fact, becoming a bot trainer can be an attractive career progression path that allows agents to shape their workplace into one where they will thrive. Empowering agents to improve bots catalyzes a virtuous circle and demonstrates that there is plenty of work for them for the foreseeable future.

AI technology: a key part of innovative solutions

Contact center interactions can uncover symptoms of underlying business issues. Paying attention to these signals can help you react strategically and take steps to create solutions. The responsible use and thoughtful implementation of new technologies to support staff and provide the analysis needed to reinvent business processes, positions your organization for success, not just survival.

Published with permission from CGI and Cheryl Allebrand.  

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However, in applying AI, organizations face challenges in terms of usability, interpretability, and the end user experience. Such challenges require innovative approaches, and this is where digital triplets come into play.

What is a digital triplet?

A digital triplet extends the digital twin model to enable a decision-maker to use advancements in AI to interrogate the digital twin data. This includes the ability to request more situational information and to simulate and optimize outcomes under different scenarios.

The term digital triplet was first introduced in 2019 as a framework for combining the digital twin with two AI components—one for generating and comparing scenarios and another for explaining and justifying the recommendations. The concept was further developed in 2020 and applied in the context of precision medicine and chronic disease management.

CGI’s digital triplet approach applies AI-driven analysis to increase the usability and interpretability of the insights. By synthesizing data quickly and communicating outcomes clearly, digital triplets help organizations improve decision-making.

The objective is to enhance both the AI capabilities and the human-AI interaction across multiple sectors. This allows for more personalized, evidence-based, and transparent decision-making and recommendations. In this way, the digital triplet serves as a virtual third-party advisor to the digital twin end user or decision-maker.

CGI’s Digital Triplet Approach: Third-party advisor for decision-makers

As shown above, a digital triplet has three core components:

1. The physical entity, the entity being evaluated to improve the function or outcomes.
2. The digital twin, which models the physical entity using data-driven and knowledge-based methods. It integrates real-time and in-context data from various sources (e.g., operations, equipment, devices and media) to help decision-makers better understand the current (as-is) and future (could-be) states of a target physical entity. The digital twin predicts the effects of different scenarios to improve risk mitigation and shape the desired future state.
3. The digital triplet, the intelligent advisor using generative AI (GenAI) and explainable AI (XAI)  to allow the user to interrogate the digital twin to make complex decisions.

Today, digital twins help researchers conduct experiments and test hypotheses via computer simulation, without exposing the real entity to any risks or harm. Therefore, they are powerful tools for predictive maintenance, algorithmic operations, evidence-based decision-making, and more.

However, a digital twin alone may not be sufficient to provide optimal guidance for decision-makers who need to consider the implications of the information provided—not only the current and future state of a physical entity, but also the trade-offs, uncertainties, and preferences involved in making complex decisions related to that entity.

The digital triplet extends the digital twin to compare scenarios and make recommendations

The digital triplet adds another layer of AI to generate and compare multiple scenarios, recommend the next best actions (options/advisor), interpret outcomes of scenarios in multiple states and contexts, and explain the reasoning behind digital twin interpretations. It can also provide more context for any resulting recommendations.

Additionally, the digital triplet enables interactions and scenario evaluations to be conducted in natural language through text or speech. It thus acts as an intelligent assistant, a virtual third-party advisor, that supports the human expert in making the best possible choices for any scenario.

Digital Triplet Interactions

Examples of digital triplets in action

While a relatively new concept, the digital triplet is not merely theoretical. It is a practical solution that has been implemented across multiple environments. For example:

• At Google Cloud AI Live + Labs in Montreal in 2023, CGI demonstrated a digital triplet to extend computer visioning for train, rail, or locomotive alerts, as well as provide context and recommended next best actions to engineers and conductors.
• The European Union’s VirtualBrainCloud project aims to develop a digital twin for patients with neurodegenerative diseases, such as Alzheimer’s or Parkinson’s, and use a digital triplet to support personalized diagnosis, prognosis, and treatment.
• Another example is using a digital triplet to improve the treatment of heart failure. Philips and the Mayo Clinic developed a digital twin of the human heart that can be adjusted for each patient based on data from various sources, such as medical imaging and electronic health records. The digital triplet then uses GenAI to test how different treatments and interventions would affect the patient’s heart, helping doctors to choose the best treatment option. XAI is used to share the results and recommendations with patients and their healthcare providers in a clear and understandable way.
• Waygate Technologies is using the digital triplet to detect defaults in industrial inspections increasing the reliability of equipment such as aircraft.

Limitless possibilities to increase trusted outcomes

The possibilities for using digital triplets to provide expert advice to business users is unlimited. The technology supports the analysis and investigation process and communicates the outcomes in a practical and conversational approach. This allows the user or decision-maker to vary the scenarios and ask validating and alternate criteria questions in natural language. The digital triplet contains the business context and reference information available and synthesizes the information quickly to explore options in partnership with the user.

Opportunities include:

• Improving the quality of operations and service delivery by giving customized, evidence-based, and proactive advice for users and decision-makers.
• Enhancing the decision-making process by considering multiple factors and outcomes, examining different options, and explaining the reasons and trade-offs.
• Strengthening the human expert by boosting their abilities, adding to their expertise, and supporting their independence and creativity.
• Building trust and collaboration between humans and AI by creating a common understanding, encouraging a dialogue, and respecting the values and preferences of both parties.
• Advancing the knowledge of industry experts by creating new hypotheses, testing new interventions, and finding new insights from the data and the models and increasing the value of the digital twin investment.

The power of GenAI is becoming clear as virtual AI-powered assistants are entering the everyday lives of citizens, customers and employees—for example, Microsoft Copilot, image and video generators. CGI’s digital triplet solution extends those capabilities to not just act as an advisor, but to also embody a multi-model ecosystem founded upon responsible AI principles to provide increased functionality and increase the value of existing and new digital twin investments.

A digital triplet presents a great opportunity for organizations to apply human-AI collaboration and create trusted outcomes at scale. Organizations must start envisioning, exploring, engineering and expanding their capabilities with digital triplets.

Published with permission from CGI and Diane Gutiw.

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However, I think that some of the more interesting stories are those which use the concept of relativity and the weird effects it produces. By allowing their fictional vessels to accelerate to near, but not beyond, the speed of light, they create stories in which time moves differently for different characters. In such stories, a character who travels a lot may live a normal human lifetime while millennia pass on the places they visit, and events ripple across the galaxy in lightspeed jumps.

(I realise that I’ve assumed that everyone reading this is familiar with the concept of relativity. Just in case you’re not, the TL;DR version is that, as object A approaches the speed of light relative to object B, then the rate at which time passes for A slows down from B’s perspective but not from A’s perspective, meaning that A could undertake a journey which takes weeks for them, but centuries for B. Many, many people have explained this much, much better than me: a quick Internet search will tell you more. And if you’re genuinely encountering these concepts for the first time, I envy you; you’re in for a fun ride!)

Grasping relativity takes an act of imagination, and crafting stories which follow the rules of relativity takes further acts of imagination. I believe that we need to exercise similar imagination when putting technology to work in our enterprises – and that we can experience our own time-dilation effects.

A science fiction story which ignores relativity often draws from a familiar mode of travel: spaceships are like ocean-going ships. They may take a while to reach their destination, but their journeys take place within human scale spans of time. Such spaceships have bridges, and captains, and other roles which resemble those of a normal ship (you may be thinking of a familiar television series).

Similarly, when we put technology to work in our enterprises, it can be tempting to treat computers as machines which behave like humans, and to organise our systems as if they are subject to the same constraints as humans. Some attempts at business process redesign fall prey to this temptation: I know that my process is inefficient; I am going to invest a large amount of money digitising this process; therefore, my money will be better spent if I redesign the process and remove the inefficiencies.

This is an understandable chain of reasoning, and I have followed it myself, many times, in many projects. However, it is often a mistake and fails to recognise the effects of computing time compression. Why is my process inefficient? If it is because it allows errors, and handles those errors badly, then I probably need to redesign it. But if it is because it has five steps rather than two, why do I care? When I implement the process as code, I will compress the time taken for those five steps from one hour each to a few milliseconds each. Relative to the computer, time stands still: I can take as many steps as I want.

For people who build software, this time dilation effect is particularly apparent when we build automated tests. I remember being shocked when I read a programming guide which said that, when building tests, it was okay to ignore some (but not all!) of the good coding practices that applied to my main code. I could cut and paste and I could repeat myself. The goal was not elegance, but coverage. As a result, some of my test code is not organised in a way that would make any sense to a human manually executing a test script. But no human could ever execute that test script manually: even a simple test script, executed frequently, can represent a level of manual effort that would exceed a human lifetime. The effects of inefficiencies in the script are negated by extreme time dilation.

I am not arguing for bad processes and inefficient code. But I am proposing that, when designing computer systems, and particularly when explaining those systems to people who don’t have a technical background, we emulate those science fiction authors who embrace the constraints of relativity. We should remember that, relative to computers, our world is at a virtual standstill, and that work which saves hours in our world may only save fractions of a second in a computer world. We’ve built a lightspeed engine: let’s use it.

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Plan A – The agile approach

We have recently been working on developing a new government digital service. If you are familiar with government project cycles, then you will know that there are five stages to product delivery; discovery, alpha, beta (split private beta, public beta), live and retire. For simplicity, we haven’t included retire in our diagram below.

We began the project using this approach. The ‘discovery’ and ‘alpha’ phases were supported by research work; exploring the client’s problems, understanding the users and their needs and establishing how the system is going to work. Our plan was to continue with research into ‘beta’ so that we could obtain live user feedback during the product deployment. You’re expected to use agile delivery throughout the lifecycle of a service. It was during the alpha phase that our problems started. We used a scrum approach, delivering in two-week cycles and began to notice that the research was being stifled in favour of delivering something within the sprint. It wasn’t working. We needed a plan B.

When Plan B is needed

Agile became popular in the 2000s, and it felt like a revelation for the industry. Gone were the days of drawn-out projects where you would only see results right at the end of the project. With the agile methodology, success was measured by quality outputs during the delivery phase. An agile approach was great for creating software where the user could access revised and improved versions at a regular cadence. It also put developers in charge of their own workload, and it brought stakeholders closer to the action since they were involved in the day-to-day work.

Industry experts in the UX space saw a big flaw. An agile approach doesn’t work so well for research. With research, the length of time it takes is dependent on so many things. It depends on when the users are available. It depends on how much data you get. It depends on what outputs you need to create. All of these factors are unknown at the beginning and may not fit into the two-week sprint timeframe. So, a new approach was required to help make agile more UX-friendly, and that evolved into what we now know as dual-track agile.

Plan B – Dual-track agile

The spirit behind dual-track agile is that the entire team engages in two distinct work tracks; research and delivery. The research track utilises the team’s expertise to produce, test, and validate ideas that help strive towards a solution. The delivery track takes that validated idea and converts it into a usable product version within that sprint.

Everyone’s a researcher

From a research track perspective, it’s important to be versatile. Not every design cycle has to create something tangible; it may be used just to validate, or even invalidate an idea. Some loops can be short, others need to be larger. The dual-track approach makes space for the flexibility and versatility that the research track requires.

We learned how to focus on what’s viable, not what’s perfect, which helps shorten the iteration cycles. It is important to involve the whole team within the research track. For example, we had our technical architect support us in co-design sessions with users and stakeholders. Developers and testers would join us in user research sessions.

Dual-track agile isn’t perfect

No method is perfect. We encountered some key challenges. There were four large ones:

1. Getting one team into the two-track mindset isn’t easy

With dual-track agile, all the team work on both tracks. This was a tricky mindset for a few reasons; reluctance to donate time, context switching is hard work and there was apprehension around someone critiquing an area that wasn’t their responsibility, such as design or development. We overcame these barriers by introducing some ground rules to encourage constructive ideas and feedback within a safe space. We introduced technical refinement sessions to give everyone time to think and iterate. And we added visual reviews and UX reviews as checkpoints in the development process for the designers to review and discuss the progress whilst ensuring that the planned output still met the desired need.

2. Getting the two tracks to work coherently towards the product vision

Dual-track agile is a fast-paced method. It kept the project and team focused by creating and monitoring a roadmap for delivery. And it empowered us to be disciplined with what was needed now and later.

3. Scope creep

When you involve more people, you generate more ideas, which can lead to scope creep. Consciously sticking to the roadmap helped, but we needed something more. So, we adopted a ‘good enough’ mindset. Every solution was challenged as to whether it was ‘good enough’ to solve the problem. And if it was, we asked, ‘does it fit the roadmap’? If we got two yes’s we knew we could implement it and move on.

4. The ‘good enough’ mindset is hard to get into

Triad has a track record. Clients from ten years ago are still using us today. We work hard to hire brilliant people. We are award-winning. We want perfection. At first, ‘good enough’ felt like we were settling for second best. But we realised that ‘good enough’ isn’t settling for anything at all. In reality, ‘good enough’ is about delivering excellence rather than searching for perfection.

Reaping the dual-track rewards

There were lots of benefits to the dual-track approach. The stand-out benefits were:

1. User-centric design, informed by technical experts, ensures that you create something that is grounded in reality

That’s because you’ve tested something that has been validated by the user needs and built within the constraints of technology.

2. Structured creativity creates stability and confidence

Because the entire team is involved in the research track and delivery track, what you end up with, is the ability to create something that is truly deliverable. This is helped by the ‘good enough’ mentality in that you have solved lots of small problems with simple solutions that collectively end in an intuitive product that isn’t trying too hard. It doesn’t put demands on the user to ‘over-think’. Therefore, everyone has confidence in it.

3. Dual-track agile encourages a forward-thinking approach

Fostered collaboration increases dialogue between your specialists across the team. It creates a better overall decision-making process. It forces the delivery workstream to adopt a forward-thinking approach and be mindful of what problems may be on the horizon rather than waiting for them. It also helps validate the research and prevents developers delivering something that doesn’t solve your client’s problem.

It’s a wrap!

Now, in public beta, we can safely say that the dual-track agile approach worked for us. It was the ideal method for a heavy UX project that kept the scope contained within a tight timeline. We have some great learnings. It’s important to adapt your methods as the project goes on. It is ok to veer from the original Agile principles,or from what everyone else does. You’re unlikely to always get it right the first time. And that’s ok. Agile your agile process. Iterate your iteration process. And go on that journey to let research inform delivery at every sprint.

Originally posted here

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We can attribute many answers to the WHY that fueled the great recalibration, but during that time one central truth started to prevail:

The employee experience could no longer be an afterthought — second, at far too many companies, to the customer experience.

Prior to 2020, the era of customer centricity was at an all-time high. Brands would proudly boast that they were customer-first. Sizeable investments were poured into creating game-changing products, services, and offerings that would WOW customers. Town hall meetings dominated around revenue metrics, Net Promoter Scores, client acquisition costs… the list goes on… sending a signal to employees that the customer was the north star.

But 2020 and the Great Resignation did something to business. With the employees suddenly holding huge power — in terms of if they would stay, or if they would go — the concept of “experience” expanded considerably. Because having a solid approach to customer experience (CX) was no longer enough.

Enter total experience

At the end of 2020, Gartner pushed the envelope forward on the concept of rethinking experience by introducing a new term to the market: “total experience.” Listed among its top 10 strategic technology trend predictions for 2021, Gartner described total experience (TX) as:

“A strategy that connects multi experience with customer, employee and user experience disciplines, Gartner expects organizations that provide a TX to outperform competitors across key satisfaction metrics over the next three years… TX strives to improve the experiences of multiple constituents to achieve a transformed business outcome. These intersected experiences are key moments for businesses recovering from the pandemic that are looking to achieve differentiation via capitalizing on new experiential disruptors.”

Suddenly, companies across the globe were thinking “total,” ensuring they were taking steps forward to equally prioritize, shape, and invest in experiences across employees, customers, shareholders, users and so on.

The concept of TX changed business conversation forever. Instead of companies focusing on being superior at just one experience pillar — e.g. customer experience — pressure mounted for them to master all. Because the Future of Work model in which we were all thrust into made one thing super clear…

If even one of your experience pillars was at risk, the whole of your organization was at risk.

3 Ways to jump-start total experience

Though the term TX might still be fairly new, the concept of winning at the experience game dates back almost 30 years ago.

Coined in a 1998 Harvard Business Review article, “Welcome to the Experience Economy,” the “experience economy” described mounting pressure companies faced to differentiate their approach, as “the next competitive battleground lies in staging experiences.”

But in 2024, the fight to win the experience economy has never been greater.

Experience has become disruptive, personalized, and customized. Individuals expect the same level of experience across all brands — from tailored messaging to robust self-service options to AI-powered recommendations. And macro trends continue to support a move to TX, or a fresh, holistic approach to standardizing and holistically thinking about experience.

So how can you jump-start your TX initiatives? Let’s dive into 3 tips to get started:

• Unlock your why: Just as any initiative needs a north star, total experience needs a compelling reason to get ignited. Consider your organization, team, or department for a moment and reflect on where you think your experience journey might be at risk or falling behind. If you lead a Product team, for example, are you equally prioritizing the experience of your existing users, as well as the desires of future users you need to acquire? If you lead HR/People, have you unintentionally spent more time creating a WOW existing employee experience, but your candidate experience is lagging and precluding you from hiring top talent? Understanding your current state and what is motivating your total experience approach is the first step in building out your strategy.
• Data as your compass: With your why clear, it’s time to put your data to work and leverage it as a compass as to where you’ve been, where you want to head and how you will ultimately measure TX impact. Instead of worrying too much about the data you have available today, consider answering this question as if you had no barriers: What new KPIs should we stand up and track either org-wide or within our department so that we can gain a more multi-dimensional approach on how our stakeholders feel about the experiences we’re creating? Think of what you want to measure, versus what you feel you can measure. Dream! Challenge yourself to think disruptively. And remember that a shift to TX requires an adjustment in terms of how you think about using data.
• Technology as the accelerator: As you zero in on your TX WHY and lean on data to set the direction, now comes the exciting part: introduce emerging technologies to accelerate momentum. When you blend together disruptive technologies with human ingenuity, your ability to create memorable, WOW experiences across your stakeholder base soars. One tech accelerator to fast-track? Artificial intelligence. AI can support everything from new-age customer satisfaction scoring methodologies to predictive new feature demands to optimizing day-to-day workflow processes, allowing you to compete in the experience game like never before

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As a result, there is rapidly rising interest and use of AI in the public sector. The Alan Turing Institute survey of almost a thousand public sector professionals in December 2023 reported that almost half of them were aware of generative AI and more than one in five of them already use it.  

At the same time experience with AI, and particularly generative AI, shows the need for ethical and responsible use of a technology that can produce seemingly endless streams of text, images, or other data using large language models (LLMs) in response to user-defined prompts. 

So the UK Government’s Generative AI Framework, published on January 24, 2024, is a timely and crucial step in guiding the controlled use of this powerful technology. The UK Framework’s straightforward approach is organized as 10 principles that form a foundation for ethical and responsible AI deployment. While quite broad and high level in nature, each of these principles focuses attention on core concerns that must be addressed in any public sector generative AI use case. 

Let’s take a look at the UK Framework’s key points:

• Understanding and limits: Principle 1 rightly emphasizes the need for clear knowledge about generative AI’s capabilities and limitations. Public sector leaders must be aware of potential biases, inaccuracies, and security needs.
• Responsible use: Principles 2 to 5 delve into responsible use, encompassing legal, ethical, and security aspects. Engaging compliance professionals, mitigating bias, and ensuring data security are crucial steps towards building trust and preventing harm.
• Human control and collaboration: Principles 4 and 7 highlight the importance of human oversight and collaboration. Keeping humans in the loop for quality control and decision-making, and embracing transparency, are vital for accountability and public trust.
• Lifecycle management and skills: Principles 5 and 9 address the full lifecycle of generative AI solutions, from procurement and deployment to maintenance and skill development. Utilizing existing government resources like the Technology Code of Practice and investing in acquiring necessary skills will be critical for successful use.
• Right tool for the job: Principle 6 reminds us to choose the right tool for the specific task. Understanding use cases and evaluating tools like LLMs wisely are critical to achieving desired outcomes.

Beyond the Framework: additional considerations

From a conceptual perspective, it is difficult to find fault with the UK Government’s Generative AI Framework. Its 10 principles bring clarity to several concerns that are priorities for those considering generative AI. However, in a rapidly-evolving landscape where digital leaders face considerable daily operational challenges to deliver effective public services, the UK Framework needs elaboration. In my experience, several additional considerations could strengthen its impact:

1. Focus on impact, not technology: While the UK Framework aptly cautions against technology-driven solutions, emphasizing the need for a clear problem statement and user-centric approach would further solidify this point. The UK government’s service manual can be a valuable tool to ensure this focus on solving the right problems.
2. Continuous monitoring and adaptation: The UK Framework does highlight the need for continuous monitoring and review to ensure generative AI solutions remain ethical, unbiased, and effective. However, the costs of providing flexibility and adaptability can be substantial, which is a significant challenge that is frequently under-resourced in the public sector. The Framework needs strengthening with the addition of clear metrics and feedback mechanisms for this ongoing evaluation and adaptation.
3. Public awareness and education: The public needs to be informed and engaged in the use of generative AI in the public sector. There has to be more focus on this, with maximum transparency and accessibility of information about how the technology’s tools are used and their potential impact. Our society is only recently learning about AI’s disruptive impact on our lives and livelihoods: trust and legitimacy depend on us understanding how we may reap the rewards while managing challenges. 

With these updates in mind, I’d recommend an additional principle for the UK Framework: 

Principle 11: You build public trust through ongoing dialogue

You discuss the challenges and opportunities of generative AI openly with the public, through regular town halls, public forums, and interactive platforms.  

You encourage ongoing constructive dialogue and engage all stakeholders, including those from traditionally hard-to-access parts of society.  

With this additional principle, the UK Framework would be more complete as a guide ethical, responsible adoption of this powerful technology in the public sector.

Implementing the principles: From theory to practice

The UK Framework provides a clear roadmap, but translating it into action is the true test. 

Effective implementation starts with embedding the UK Framework’s principles into the DNA of every project. Investment in training public service professionals must cover every stage from understanding the principles to deployment. Guardrails such as checklists and decision-making matrices for every project that generative AI touches could support this.   

Furthermore, robust governance structures are essential. Public sector agencies are already appointing dedicated AI leads and ethics committees to oversee generative AI projects. These should now be tasked with ensuring compliance with the UK Framework and fostering a culture of ethical decision-making. Regular assessments and audits should be conducted to identify potential issues and ensure ongoing adherence to the principles. Transparency becomes paramount here, with clear communication channels established to inform stakeholders about how generative AI is being used and its potential impact.

In practice, the success of generative AI in the public sector requires a proactive, multifaceted approach. The UK Government’s Generative AI Framework is a welcome advance. Embedding its principles into everyday practice, instituting strong governance structures, and prioritizing transparency are key pillars for success. By taking these steps, digital leaders can leverage the power of generative AI for good and build trust with citizens and stakeholders. 

The AWS Institute has recently published an AI/ML Masterclass looking at AI in the public sector. 

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