Many of us have been interacting with AI without even realising it. When we use a navigation app to find the quickest route to our destination, we benefit from advanced algorithms that process vast amounts of data in real-time. These applications leverage computational statistics to predict traffic patterns, estimate travel times, and provide accurate directions. This same underlying technology is now being harnessed to revolutionise the way local authorities operate and serve their communities and stakeholders.

IEG4’s Vision: Enhancing our digital experience platform with AI

At IEG4, we are at the forefront of integrating AI into our digital solutions for local government. Our commitment to innovation is driven by the potential of AI to transform public services, making them more efficient, responsive, and user-friendly.

We are exploring several AI-driven enhancements across our product offerings:

Generative Content in Web CMS: One of our key initiatives is incorporating AI to generate content within our headless web content management system (CMS). This capability will enable local authorities to maintain up-to-date and relevant content on their websites effortlessly. Using natural language processing (NLP), the AI can draft articles, announcements, and other web content, ensuring accuracy and relevance while saving time and resources.

AI Assistants in Customer Portals & Websites: We are also developing AI assistants for our customer portal and website solutions. These assistants allow customers to interact with council services using natural language queries. Instead of navigating complex menus, users can ask questions or describe their issues in plain language, and the AI assistant will guide them to the appropriate services or information. This enhancement promises to make council services more accessible and user-friendly.

Knowledge Systems within CRM: For the internal operations of local authorities we are integrating AI into our customer relationship management (CRM) system. This integration will create a robust knowledge system that staff can utilise to quickly find information, answer queries, and resolve issues. By leveraging AI, staff will be able to access a wealth of knowledge in real-time, improving their efficiency and effectiveness.

Image Recognition in Forms: AI’s capabilities in image recognition are also being leveraged in our solutions. When customers submit forms with attached images, the AI can automatically detect and categorise items within the images. This feature can streamline processes such as reporting and verifying information, reducing the need for manual intervention and speeding up service delivery.

Natural Language Reporting with Microsoft Copilot: Another exciting development is the integration of AI into our reporting databases. Using Microsoft Copilot, we enable natural language queries to generate reports and dashboards. This functionality allows users who may not be experienced with tools like Microsoft Power BI to create insightful reports and visualisations easily. By simply describing the data they need, users can generate complex reports, making data analysis more accessible across the organisation.

As we continue to explore the potential of AI, we remain focused on creating solutions that are not only technologically advanced but also user-centric. We aim to empower councils with the tools they need to meet their communities’ evolving needs, ensuring that public services are accessible, efficient, and effective for all.

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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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As I think back on my time leading fundraising for a nonprofit, I’m not sure I considered myself much of a data leader. But, in truth, I was utilizing data every day to steer decision-making that would ultimately positively impact our organization’s health and growth. In fact, I was likely the heaviest user of organizational data.

Data powered everything in my day-to-day, from helping me segment and send targeted messaging to donors based on preferred channels, to analyzing past giving patterns to predict when they’re likely to give again, to predicting future grant funds and event revenue so that I could plan accordingly. Data helped me track campaign performance and donor engagement, as well as show donors and foundations how their funds were impacting service population outcomes. I was able to tell incredible stories of long-term impact because of data collection and analysis. And, had I ignored what my data was telling me, I would have been aimless—instead defaulting to random gut-based decision making, unable to back up my decisions or make a case to shift away from long-held strategy beliefs.

No matter your job, data plays a critical role in both your individual success, and in the success of the organization as a whole. From influencing day-to-day decisions to informing long-term strategy, data is an essential resource to tap. And in today’s world, embracing data leadership is no longer optional — it’s a necessity.

The imperative of data leadership

In today’s digital age, data is generated at an unprecedented rate. Every interaction, transaction, and process produces valuable data points that, when analyzed correctly, can provide incredible insights, including opportunities for cost savings and new revenue streams. A McKinsey Global survey found that respondents at high-performing companies “are three times more likely than others to say their data monetization efforts contribute more than 20 percent to company revenues.”

Organizations that leverage data effectively are better positioned to outperform their competitors. A recent found that data-driven organizations are 23 times more likely to acquire customers, six times as likely to retain those customers, and 19 times more likely to be profitable. In an era where competitive advantage is fleeting, the ability to swiftly interpret and act on data can be the difference between success and failure.

Data leadership across business functions

Let’s dive into three business functions and how they benefit from harnessing their data.

• Marketing: Personalization is no longer a luxury; it’s an expectation. By analyzing data from various touchpoints, marketers can gain a 360-degree view of their customers. This includes understanding their preferences, behaviors, and pain points, which in turn enables the creation of highly targeted and relevant marketing campaigns. Data allows marketers to tailor their messages to individual customers, improving engagement and conversion rates. According to McKinsey, companies that thrive at personalization earn 40% more money from these activities than the average competitor. What’s more, data-driven marketing provides clear metrics to measure the return on investment (ROI) of marketing campaigns. This allows leaders to allocate resources more effectively, optimizing marketing spend for maximum impact.
• Sales: In sales, data is a powerful tool for identifying opportunities, improving customer interactions, and forecasting future performance. Data can help sales teams prioritize leads based on their likelihood to convert. By analyzing past interactions, demographics, and behavioral data, sales leaders can create predictive models that identify high-potential leads. Data-driven forecasting models can provide sales leaders with insights into future sales trends, helping them make informed decisions about resource allocation and target setting.
• Strategy: Strategic decision-making is inherently data-driven. Leaders who incorporate data into their strategic planning can make more informed, forward-thinking decisions that drive long-term success. Data provides valuable insights into market trends, competitor activities, and customer needs. Strategic leaders can use this information to identify opportunities for growth and innovation. When it comes to team performance, data-driven performance measurement allows leaders to track progress against strategic goals. Key performance indicators (KPIs) and dashboards provide real-time insights into how well the organization is performing and where adjustments may be needed.

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Technology and sustainability were among the top 10 priorities of CEOs in 2023, according to Gartner. Business leaders say they view both as important drivers of growth and innovation, and increasingly they’re using technology to achieve their sustainability goals.

Cloud technology has an important role to play here, and has the potential to address, mitigate, and  help solve some of the biggest sustainability challenges. The scope of the cloud’s role in sustainability is highlighted in a recent series of AWS Institute masterclasses.

If companies start out on the right trajectory with their cloud deployment plan, they can accelerate their sustainability improvements and progressively achieve four gains:

1. More sustainable IT delivery
2. More sustainable business operations
3. Creation and delivery of more sustainable business models, services, and products
4. Thorough organizational transformation, with sustainability at the core of their operation

More energy-efficient IT delivery

AWS data centers are 3.6 times more energy efficient than a typical US enterprise data center and up to five times more energy efficient than the average in Europe.

So, when customers deploy technology using AWS Cloud infrastructure, they not only harness benefits but also pass them on to their customers in the form of measurable supply-chain sustainability improvements.

Amazon overall has committed to reaching net-zero carbon by 2040, 10 years ahead of the Paris climate agreement. Net-zero carbon is when you take the same amount of CO₂ from the atmosphere as you put in.

We lead this drive with the following aims:

• Match all of our electricity used in  our operations with 100 percent renewable energy  by 2025, five years ahead of our original 2030 target.
• Enable more than 400 renewable energy projects to generate enough renewable electricity to power the equivalent of 20.8 million European homes.
• AWS will become water positive by 2030, returning more water to communities than we use in our direct operations.

And Amazon has been able to decouple business growth from CO₂ emissions, as I explain in IT operations and efficiencies, the second part of the AWS Institute Sustainability Masterclass series. In 2022, for the first time, Amazon reduced CO2 emissions by 0.4 percent even though growth was 9 percent year-over-year.

Digital asset monitoring helps cut carbon footprint

Most companies’ carbon footprints are not generated predominantly by IT operations but through their wider use of resources. The cloud can help target improvements here, too—for example, by creating efficiencies through digital asset monitoring.

When a Coca-Cola bottling plant in Turkey created a digital twin (virtual representation) of the facility, it could model its entire bottle-washing process and then simulate and compare different settings. Once it implemented optimal settings at the physical plant, the company saw a 20 percent annual energy savings and a 9 percent reduction in water consumption. It’s also saved an estimated 34 days in processing time annually.

The cloud can help companies pinpoint and drive all kinds of new sustainability-related efficiencies through the capture and analysis of data from digital sensors, the use of machine learning (ML), and the implementation of efficient building management, to name a few.

The AWS Sustainability Insights Framework further allows companies to analyze data from across their various resource-management systems, utility data, and more, so they can devise new targets and include findings in corporate sustainability reports.

Innovation

Achieving sustainability offers an economic opportunity worth $12 trillion by 2030, according to McKinsey. The potential starts with imagining the future—because much of the innovations that the world needs to transition to a net-zero economy from 2030 to 2050 do not yet exist. We must then work to overcome the constraints that businesses face today.

These constraints are a powerful focus for innovation. There are significant opportunities to drive innovation through the reuse of existing resources in a circular economy.

Several companies are working on recycling programs for batteries. Other innovative projects use geospatial data from satellites to optimize vegetation, water flow, biodiversity, and soil health across regions.

Transformation

Beyond targeted innovation, modern cloud technology presents opportunities for organizations to reinvent themselves with sustainability at the core of their operation. Arup, an engineering group, promotes environmental regeneration, biodiversity, and conservation of resources in its projects to achieve “more sustainable development in the built environment.”

Research suggests that $44 trillion of annual economic output depends on the natural environment, such as clean air and water, pollination, and forest cover. And while companies typically do not directly pay for them, they cannot be taken for granted, so organizations need  to consider the materiality of biodiversity in their operations.

Islands make good laboratories for innovation, and the Naxos Smart Island project in Greece targets not just a more sustainable  environment but also smart  solutions linked to mobility, primary healthcare, and the transport of goods. The project—backed by the Greek government, local authorities, and the U.S. Embassy—will upgrade existing infrastructure, such as the local marina, energy grid, and water management systems to support smart infrastructure management.

Progress toward greater sustainability, aided by the cloud, begins with understanding an organization’s starting point and its scope for improvement. The potential for transformation is extensive once companies have set their course and are able to measure and interpret existing sustainability data, identify ways to optimize everyday operations, and introduce new innovation, business models, and mission statements.

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AI is a hot topic. There is a lot of excitement about its potential to change just about all the products and services we use every day. However, large-scale adoption of AI solutions, what I have started to call AI-at-scale, faces challenges familiar to anyone who has been involved with digital transformation efforts over the past decade. While small-scale experiments and limited use cases abound, expanding the range, application, and resilience of those solutions is proving to be a much harder nut to crack. Obtaining sufficient high-quality data, integrating AI with existing systems, overcoming talent shortage, and managing ethical considerations are just a few of the many key hurdles faced by large established organizations (LEOs) in the public and private sector as they take on this task.

Digital leaders attitudes to AI survey

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But while this seems to be an almost universal experience, chatbots are now popping up everywhere. From customer service and healthcare to banking and security – there’s a bot for that. How did we end up with such a disconnect between business expectation and user reality?

A chatbot, for chatbot’s sake

The galloping excitement around AI means organisations are falling over themselves to add a chatbot as fast as possible. The only question that gets asked is “how long till it’s set up?”

After all, most organisations are battling with how to get customers the information they need at the right time and in the right way. A chatbot feels like a magic bullet, an easy fix. An AI assistant that never sleeps and has all the information at its (virtual) fingertips – hooray! In all the rush, the focus ends up on the platform itself instead of on our users.

Chatbots and AI

Chatbots have long suffered from being set up with little thought. The old scripted versions had a bad reputation for simply regurgitating corporate FAQs. People ran the chatbot gauntlet only in the (often vain) hope that a human was on the other side.

Modern chatbots don’t follow a predetermined script. Instead they’re based on a type of AI known as a Large Language Model (LLM). This allows them to process and predict what was previously an exclusively human to human interface: language.

As such they are a type of “generative” AI: AI that can generate its own original content. Most are “retrieval augmented”. That simply means they retrieve their answers from a set content source.

The addition of AI theoretically makes these modern bots much more powerful. But when they are poorly thought through and poorly implemented, it mostly makes them more powerfully irritating.

The impact of bad chatbots

We do not need to imagine the impact of poor chatbot implementation. Right now the media is full of delighted stories of people getting the better of bots.

One bot offered a great deal on a car. Another composed a haiku about how bad it was and we’re even starting to see court cases. But that’s just the tip of the proverbial iceberg.

In the face of so many bad experiences, users are increasingly exasperated and disappointed by bots. The impact of this is corrosive. When chatting to the team about chatbots, I found a common thread to our research insights that users increasingly:

• avoid chatbots where they can or deal with them only reluctantly
• start with the expectation of a bad experience – and get irritated all the faster when it inevitably is
• are not certain of the accuracy of responses, so want to verify with a human anyway
• worry about data security and privacy

Good design takes time

An AI chatbot is not separate from your content. It’s a part of your content. It cannot fix your content problems because it pulls what it “knows” from your content.

Think of a chatbot as a glorified search engine attached to a very fancy version of the autocomplete function you have on your phone. It does not understand the words it generates. It only understands how words relate to each other statistically. And that means that when you ask a question, your bot does not know the right answer. Rather it predicts the most likely answer. From your content.

I think you can see where I am going with this. If your content is a mess, the chances are that your chatbot won’t help. It may even make things worse. I’m not saying that chatbots are bad, or are never the right solution. But I am saying let’s be duly wary of “one size fits all” answers and quick fixes. Let’s take the time to properly map and fix our content problems because being irritating is bound to be bad for business.

As Dr Ralf Speth, CEO of Jaguar Land Rover, succinctly put it, “If you think good design is expensive, you should look at the cost of bad design”.

Creating a chatbot that’s user-centred

But there is stuff you can do. Here’s some considerations if you’re about to start a chatbot project.

An AI bot is a solution: what’s the problem?

It’s never a good idea to jump straight to a solution. What problem are you trying to solve? If your bot does not meet an existing user need, it will not add value for you or your users.

An AI exists to serve content 

This is all too easily forgotten in the rush to create a chatbot. Good content design is fundamental to implementing a product that meets user needs. With a bot the best content answers a specific need, is short, conversational and makes any next steps clear. Introducing a bot and making it responsive and pretty will do nothing if the content behind it doesn’t help users.

AI generated content needs human review

AI generated content may not be accurate or appropriate. Consider the risks to your organisation carefully. How will you know that your bot is delivering the right answers? How might you introduce human reviews? How will AI and user content be retained?

AI bots cannot solve problems caused by poor content design and governance

If documents and data sources are poorly managed and maintained, AI will struggle to provide quality responses. If knowledge bases are not reliable, a bot will likely simply add to the confusion.

Originally posted here

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It sounds so easy! As a chief executive, I would be reading this stuff and thinking to myself, ‘Why the hell aren’t my people doing this already?’.

In fact, I am hearing from digital and technology practitioners in councils all over the country saying that this is happening. That the AI hype is putting pressure on teams to start delivering on some of these promises, and to do so quickly. I find this troubling.

It’s always worth referring to my 5 statements of the bleedin’ obvious when it comes to technology in organisations:

1. If something sounds like a silver bullet, it probably isn’t one
2. You can’t build new things on shaky, or non-existent, foundations
3. There are no short cuts through taking the time to properly learn, understand and plan
4. There’s no such thing as a free lunch – investment is always necessary at some point and it’s always best to spend sooner, thoughtfully, rather than later, in a panic
5. Don’t go big early in terms of your expectations: start small, learn what works and scale up from that

How does this apply to using AI in public services? Here’s my take on the whole thing. Feel free to share it with people in your organisation, especially if you think they may have been spending a little too long at the Kool Aid tap:

• The various technologies referred to as ‘AI’ have huge potential, but nobody really understand what that looks like right now
• Almost all the actual, working use cases at the moment are neat productivity hacks, that make life mostly easier but don’t deliver substantial change or indeed benefits
• Before we can come close to understanding how these technologies can be used at scale, we need to experiment and innovate in small, controlled trials and learn from what works and what doesn’t
• Taking the use of these technologies outside of handy productivity hacks and into the genuinely transformative change arena will involve a hell of a lot of housekeeping to be done first: accessing and cleaning up data, being a big one. Ensuring other sources for the technology to learn from is of sufficient quality (such as web page content, etc) is another. Bringing enough people up to the level of confidence and capability needed to execute this work at scale, for three – and there’s a lot more.
• The environmental impact of these technologies is huge, and many organisations going ham on AI also happen to have declared climate emergencies! How is that square being circled? (Spoiler – it isn’t.)
• The choice of AI technology partner is incredibly important and significant market testing will be required before operating at scale. There’s an easy option on the market that is picking up a lot of traction right now, because it’s just there. This is not a good reason to use a certain technology provider. Organisations must be very wary of becoming addicted to a service that could see prices rocket overnight. More importantly perhaps is whether you can trust a supplier, or those that supply bits of tech to them, to always do the right thing with your data. There’s always going to be an element of risk here: but at least identify it, and manage it.
• Lastly, the quality of the outputs of these things cannot be taken on trust, and have to be checked for bias, inaccuracies and general standards. Organisations need to have an approach to ensuring checks and balances are in place, otherwise all manner of risks come into play, from the embarrassing to the potentially life-threatening.

This ended up being a lot longer than I first imagined. But I guess that just shows that this is a complex topics with a whole host of things that need to be considered.

Just remember – any messages you see claiming that AI is a technology that takes hard work away for minimal investment or effort, is at best just guesswork and at worst an outright lie.

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In the organisations we are advising about AI adopting these tools is leading staff to ask a host of other questions. Do we have the right governance? Is our data ready, and secure enough, to be used by AI tools? Will our culture help or hinder how we adopt AI? 

AI and how it is changing organisations is on my mind at the moment. We are gathering data about how charities are adopting AI as part of the survey to build this year’s Charity Digital Skills Report. We want to hear from more charities how they’re using AI, how they are learning about it and whether there are any barriers they face in adopting it further, so we can make the case to funders and sector decision makers about the resources and support they need. 

What I’m observing in my day job, and am excited about gathering data on as part of our survey, is the ripple effect that AI is creating in organisations, beyond the tools. The organisations who I see making progress with AI are the ones who are looking at how they can make changes in areas from skills to leadership to strategy, in order to make the most of AI. 

How are organisations using AI? 

Organisations such as banks and energy suppliers have been using AI as part of their services for some time. What’s been exciting over the last year is seeing how charities are beginning to incorporate AI into their service delivery, meaning that it can help them increase their impact. 

Citizens Advice Stockport, Oldham, Rochdale and Trafford are using AI to manage the demands they are facing due to the cost of living crisis. AI tools means that they can help their advisers get the information they need quickly and easily, and support more individuals. 

Stuart Pearson, their Head of Innovation, says, “We created an AI advice-service “co-pilot” tool powered by LLM and RAG technology. This tool assists advisors by rapidly locating and sharing pertinent data from dependable sources like GOV.UK and Citizens Advice resources.

This tool has accelerated response times, resulting in quicker assistance for callers. It has also facilitated faster training for new advisors, empowering them to provide accurate answers promptly.”

These developments have led Stuart and his team to collaborate with Citizens Advice and the Incubator for Al to refine their prototype and develop their tool Caddy, which will be tested as part of a wider pilot phase in many local Citizens Advice offices this month. 

Over at the charity Dementia UK, Victoria Lyons, their Head of Digital and Dementia at Work, has been exploring new ways to use AI. Her organisation is now on the second phase of this work. Lyons explains, “We have plans to develop our use of AI in the coming year and I am looking at a number of tools that will allow us to provide our unique support to families that need our help in an efficient digitally agile way.

Some of this is providing staff with useful tools to speed up some of the parts of their jobs.Some of this work may also be about creating externally facing resources powered by or using AI.” 

How are organisations changing in response to AI? 

It is impossible for organisations to make the most of AI without growing skills.  Victoria Lyons and her team at Dementia UK  have invested in this area. “All staff have been given the opportunity to attend drop-in sessions with myself and another colleague, “she says. “At these sessions we highlighted some of the uses of AI and the issues and risks with AI as well as showed people how to use Ai to support them with their work. We taught  people how to write a prompt and got people interacting with Co-pilot in real time as part of these sessions.”

Whilst offering staff skills development and guidance is vital. AI is also forcing organisations to consider their ways of working. Pearson’s team at Citizens Advice Stockport, Oldham, Rochdale and Trafford committed to working transparently through the design, development, and implementation process for Caddy, and has also prompted a collaboration with Citizens Advice Manchester to establish an Innovation Hub. The hub will help their organisations pilot more ways to use Caddy together. 

Yet one of the most important lessons to emerge from this process is how important it is to adopt AI responsibly. Pearson says,”we have approached this work with the unwavering belief that doing so in an ethical and responsible manner is non-negotiable. Therefore, transparency, accountability, and security have been the core principles guiding our development. To ensure this, we have begun developing an ethical framework for AI for the organisation, it will focus not just on development but also procurement rules.” In addition, Pearson’s team will be beefing up their existing governance with an AI oversight group. 

What should organisations do next? 

The pace of change that we are already seeing in AI indicates that we could see a lot of things happen quickly. That’s why it’s so important to develop a robust approach to issues such as data security now rather than later. Richard Seiersen, Chief Risk Technology Officer at Qualys, a company focused on cloud security and compliance solutions, warns that, “we are at the start of building AI projects. We can try to make those projects secure by default through collaboration, or we can try to implement security later, at additional cost and in longer timeframes to deliver. I know which approach I would rather be part of, both for the security team and for the AI side as well.”  He encourages organisations to make following best practices around security when working with developers part of how success is measured. 

This points to how critical humans are to successful adoption of AI- for now. Pearson points out that AI is not a substitute for people, and this needs to be signaled loud and clear through your ways of working. “Organisations should prioritise this collaborative approach to maximise the benefits of AI. Involve all your teams in the discussions, everyone is going to need to understand and navigate this new AI powered future,” he advises. 

AI will create wholesale changes in how we live and work. We are only at the start of this journey. Getting to grips with these tools can feel daunting, as can thinking how your organisations might have to change course to accommodate their successful adoption. Yet this is an opportunity. It’s a chance to consider why and how your organisation does what it does- and whether this needs to change so you can keep adding value in an age of rapid technological advancement. 

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Today, manufacturers want to transform the way they operate to sustain growth, reduce costs, improve product quality and achieve operational excellence—all in a manner that supports their sustainability goals and transition to net zero. But achieving this is grounded in the ability to make informed decisions, at the right time, based on the right data.

What does it mean to be data-driven? Is there a blueprint for becoming a data-driven organization? In fact, is it even achievable?

The answer is yes.

While there is no one-size-fits-all mantra, below I share the 4 key steps to advance your data journey.

1. Harness the full potential of data with a robust and holistic end-to-end data strategy that underpins the entire data journey—from collecting and storing data to putting it into action.

Building a comprehensive data strategy starts with your strategic imperatives. In other words, identifying and understanding your priorities, the positive changes you seek, and what is possible. For a data strategy to be effective, it must always be tied to the desired business value that an organization intends to achieve.

In the case of manufacturing, there are several data use cases:

• On the shop floor, the most well-known use of data is for the predictive maintenance of machinery and quality analytics of raw materials or parts. Similarly, using machine learning can support process improvement.
• Digital twins is another use case. For instance, in product design, digital replicas can be tested and improved before spending money on production. Digital models can also be used to build and track performance over the entire life cycle of the product.
• Lastly, using relevant and actionable data to achieve sustainability and carbon neutrality targets and address stakeholder expectations is a key topic for the industry. Lately, data use cases on energy consumption have increased due to the energy crisis.

In short, developing a data strategy starts with asking (and answering) the overarching question: What can data do for my organization?

2.   The second step in the data journey is data management, or how data is treated across its entire lifecycle, including access rights and user management, quality, security and integrity.

Taking a holistic, enterprise-wide data management approach that includes a semantic model to create clean data sets, structure, and elicit meaning is critical to realizing value from data. It is also a prerequisite to creating digital twins.

The holy grail for a data-driven company is creating a “digital continuum,” or seamless and integrated data flows across business units, processes and systems. Adopting industry-specific data standards helps significantly in reaching this interoperability.

Yet, data quality is not tied to standards alone. Ensuring data is fit for purpose requires an informed approach across the entire data life cycle, which includes:

• Collecting and capturing relevant data from assets and across the business value chains
• Understanding and defining data ownership, such as when data can and needs to be used and the different levels of access and rights within your company (Of course, data security, integrity and classification are critical when documenting data ownership, especially for those organizations dealing with enhanced safety protocols, such as those within the aerospace industry.)
• Building a clear plan for data’s end-of-life and developing protocols for archiving and destroying data

3. As organizations evolve, it becomes increasingly important to leverage enterprise intelligence to take data to the next level.

Enterprise intelligence (EI) is your organization’s ability to turn context-relevant data into actionable insights that drive business value. Once harnessed, you can scale data up the knowledge pyramid—moving from data to wisdom by employing increasingly complex analytics and AI.
While every organization’s path is different—both in terms of pace and legacy constraints—there are some fundamental stages to climbing this knowledge pyramid:

• Deploy basic reporting.
• Build in basic automation and simple logic to interact with your data
• Employ intelligent process automation or digital twins to blend the boundaries between the digital and real world
• Apply cognitive computing, such as analytics, machine learning and pattern recognition, to enable machines to sense and infer
• Embrace artificial intelligence so technologies such as neural networks or genetic algorithms can be incorporated into processes

4. Equally important (and some might even argue most important) is managing the human side of change effectively. When it comes to becoming a data-driven manufacturing organization, organizational readiness is paramount.

A data-first mindset is critical to building trust and ensuring ROI on your data investment. Human change does not happen overnight; it requires patience and enduring willingness. Transforming into a data-driven organization calls for a shared vision and roadmap that is clearly communicated to all members of the organization. Conducting skill gap assessments for the entire organization and specific departments can help evolve from the status quo to the desired future state.

In addition, successful transformation requires champions of change and a clear methodology. And, at the helm, a committed leadership team must accompany change and constantly reflect and adapt.

Originally posted here

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