Tag: Ai

The challenge of AI is that it is hard to build a business case when the outcomes are inherently uncertain. Unlike a traditional process improvement procedure, there are few guarantees that AI will solve the problem it is meant to solve. Organisations that have been experimenting with AI for some time are aware of this, and have begun to formalise their Proof of Concept (PoC) process to make it easily repeatable by anyone in the organisation who has a use case for AI. PoCs can validate assumptions, demonstrate the feasibility of an idea, and rally stakeholders behind the project.

PoCs are particularly useful at a time when AI is experiencing both heightened visibility and increased scrutiny. Boards, senior management, risk, legal and cybersecurity professionals are all scrutinising AI initiatives more closely to ensure they do not put the organisation at risk of breaking laws and regulations or damaging customer or supplier relationships.

13 Steps to Building an AI PoC

Despite seeming to be lightweight and easy to implement, a good PoC is actually methodologically sound and consistent in its approach. To implement a PoC for AI initiatives, organisations need to:

• Clearly define the problem. Businesses need to understand and clearly articulate the problem they want AI to solve. Is it about improving customer service, automating manual processes, enhancing product recommendations, or predicting machinery failure?
• Set clear objectives. What will success look like for the PoC? Is it about demonstrating technical feasibility, showing business value, or both? Set tangible metrics to evaluate the success of the PoC.
• Limit the scope. PoCs should be time-bound and narrow in scope. Instead of trying to tackle a broad problem, focus on a specific use case or a subset of data.
• Choose the right data. AI is heavily dependent on data. For a PoC, select a representative dataset that’s large enough to provide meaningful results but manageable within the constraints of the PoC.
• Build a multidisciplinary team. Involve team members from IT, data science, business units, and other relevant stakeholders. Their combined perspectives will ensure both technical and business feasibility.
• Prioritise speed over perfection. Use available tools and platforms to expedite the development process. It’s more important to quickly test assumptions than to build a highly polished solution.
• Document assumptions and limitations. Clearly state any assumptions made during the PoC, as well as known limitations. This helps set expectations and can guide future work.
• Present results clearly. Once the PoC is complete, create a clear and concise report or presentation that showcases the results, methodologies, and potential implications for the business.
• Get feedback. Allow stakeholders to provide feedback on the PoC. This includes end-users, technical teams, and business leaders. Their insights will help refine the approach and guide future iterations.
• Plan for the next steps. What actions need to follow a successful PoC demonstration? This might involve a pilot project with a larger scope, integrating the AI solution into existing systems, or scaling the solution across the organisation.
• Assess costs and ROI. Evaluate the costs associated with scaling the solution and compare it with the anticipated ROI. This will be crucial for securing budget and support for further expansion.
• Continually learn and iterate. AI is an evolving field. Use the PoC as a learning experience and be prepared to continually iterate on your solutions as technologies and business needs evolve.
• Consider ethical and social implications. Ensure that the AI initiative respects privacy, reduces bias, and upholds the ethical standards of the organisation. This is critical for building trust and ensuring long-term success.

Customising AI for Your Business

The primary purpose of a PoC is to validate an idea quickly and with minimal risk. It should provide a clear path for decision-makers to either proceed with a more comprehensive implementation or to pivot and explore alternative solutions. It is important for the legal, risk and cybersecurity teams to be aware of the outcomes and support further implementation.

AI initiatives will inevitably drive significant productivity and customer experience improvements – but not every solution will be right for the business. At Ecosystm, we have come across organisations that have employed conversational AI in their contact centres to achieve entirely distinct results – so the AI experience of peers and competitors may not be relevant. A consistent PoC process that trains business and technology teams across the organisation and encourages experimentation at every possible opportunity, would be far more useful.

Ecosystm research reveals a stark reality: 75% of technology leaders in Financial Services anticipate data breaches. ​

Given the sector’s regulatory environment, data breaches carry substantial financial implications, emphasising the critical importance of giving precedence to cybersecurity. This is compelling a fresh cyber strategy focused on early threat detection and reduction of attack impact.​

Read on to find out how tech leaders are building a culture of cyber-resilience, re-evaluating their cyber policies, and adopting technologies that keep them one step ahead of their adversaries.​

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The impact of AI on Customer Experience (CX) has been profound and continues to expand. AI allows a a range of advantages, including improved operational efficiency, cost savings, and enhanced experiences for both customers and employees.

AI-powered solutions have the capability to analyse vast volumes of customer data in real-time, providing organisations with invaluable insights into individual preferences and behaviour. When executed effectively, the ability to capture, analyse, and leverage customer data at scale gives organisations significant competitive edge. Most importantly, AI unlocks opportunities for innovation.

Read on to discover the transformative impact of AI on customer experiences.

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Generative AI has stolen the limelight in 2023 from nearly every other technology – and for good reason. The advances made by Generative AI providers have been incredible, with many human “thinking” processes now in line to be automated.  

But before we had Generative AI, there was the run-of-the-mill “traditional AI”. However, despite the traditional tag, these capabilities have a long way to run within your organisation. In fact, they are often easier to implement, have less risk (and more predictability) and are easier to generate business cases for. Traditional AI systems are often already embedded in many applications, systems, and processes, and can easily be purchased as-a-service from many providers.  

Unlocking the Potential of AI Across Industries 

Many organisations around the world are exploring AI solutions today, and the opportunities for improvement are significant: 

• Manufacturers are designing, developing and testing in digital environments, relying on AI to predict product responses to stress and environments. In the future, Generative AI will be called upon to suggest improvements. 
• Retailers are using AI to monitor customer behaviours and predict next steps. Algorithms are being used to drive the best outcome for the customer and the retailer, based on previous behaviours and trained outcomes. 
• Transport and logistics businesses are using AI to minimise fuel usage and driver expenses while maximising delivery loads. Smart route planning and scheduling is ensuring timely deliveries while reducing costs and saving on vehicle maintenance. 
• Warehouses are enhancing the safety of their environments and efficiently moving goods with AI. Through a combination of video analytics, connected IoT devices, and logistical software, they are maximising the potential of their limited space. 
• Public infrastructure providers (such as shopping centres, public transport providers etc) are using AI to monitor public safety. Video analytics and sensors is helping safety and security teams take public safety beyond traditional human monitoring. 

AI Impacts Multiple Roles 

Even within the organisation, different lines of business expect different outcomes for AI implementations. 

• IT teams are monitoring infrastructure, applications, and transactions – to better understand root-cause analysis and predict upcoming failures – using AI. In fact, AIOps, one of the fastest-growing areas of AI, yields substantial productivity gains for tech teams and boosts reliability for both customers and employees. 
• Finance teams are leveraging AI to understand customer payment patterns and automate the issuance of invoices and reminders, a capability increasingly being integrated into modern finance systems. 
• Sales teams are using AI to discover the best prospects to target and what offers they are most likely to respond to.  
• Contact centres are monitoring calls, automating suggestions, summarising records, and scheduling follow-up actions through conversational AI. This is allowing to get agents up to speed in a shorter period, ensuring greater customer satisfaction and increased brand loyalty. 

Transitioning from Low-Risk to AI-Infused Growth 

These are just a tiny selection of the opportunities for AI. And few of these need testing or business cases – many of these capabilities are available out-of-the-box or out of the cloud. They don’t need deep analysis by risk, legal, or cybersecurity teams. They just need a champion to make the call and switch them on.  

One potential downside of Generative AI is that it is drawing unwarranted attention to well-established, low-risk AI applications. Many of these do not require much time from data scientists – and if they do, the challenge is often finding the data and creating the algorithm. Humans can typically understand the logic and rules that the models create – unlike Generative AI, where the outcome cannot be reverse-engineered. 

The opportunity today is to take advantage of the attention that LLMs and other Generative AI engines are getting to incorporate AI into every conceivable aspect of a business. When organisations understand the opportunities for productivity improvements, speed enhancement, better customer outcomes and improved business performance, the spend on AI capabilities will skyrocket. Ecosystm estimates that for most organisations, AI spend will be less than 5% of their total tech spend in 2024 – but it is likely to grow to over 20% within the next 4-5 years. 

It seems for many employees, the benefits of working from home or even adopting a hybrid model are a thing of the past. Employees are returning to the grind of long commutes and losing hours in transit. What is driving this shift in sentiment? CEOs, who once rooted for remote work, have undergone a change of heart – many say that remote work hampers their ability to innovate.

That may not be the real reason, however. There is a good chance that the CEO and/or other managers feel they have lost control or visibility over their employees. Returning to a more traditional management approach, where everyone is within direct sight, might seem like a simpler solution.  

The Myths of Workplace Innovation

I find it ironic that organisations say they want employees to come into the office because they cannot innovate at the same rate. What the last few years have demonstrated – and quite conclusively – is that employees can innovate wherever they are, if they are driven to it and have the right tools. So, organisations need to evaluate whether they have innovated on and evolved their hybrid and remote work solutions effectively, to continue to support hybrid work – and innovation.

What is confusing about this stance that many organisations are taking, is that when an organisation has multiple offices, they are effectively a hybrid business – they have had people working from different locations, but have never felt the need to get all their staff together for 3-5 days every week for organisation-wide innovation that is suddenly so important today.

The CEO of a tech research firm once said – the office used to be considered the place to get together to use the tools we need to innovate; but the reality is that the office is just one of the tools that businesses have, to drive their organisation forward. Ironically, this same CEO has recently called everyone back into the office 3 days a week!

Is Remote Work the Next Step in Employee Rights?

It has become clear that remote and hybrid work is the next step in employees getting greater rights. Many organisations fought against the five-day work weeks, claiming they wouldn’t make as much money as they did when employees worked whenever they were told. They fought against the 40-hour work week (in France some fought against the 35-hour work week!) They fight against the introduction of new public holidays, against increases to the minimum wages, against paid parental leave.

Some industries, companies, unions, and countries are looking to (or already have) formalised hybrid and remote work in their policies and regulations. More unions and businesses will do this – and employees will have choice.

People will have the option to work for an employer who wants their employees to come into the office – or work for someone else. And this will depend on preferences and working styles – some employees enjoy the time spent away from home and like the social nature of office environments. But many also like the extra time, money, and flexibility that remote work allows.

There might be many reasons why leadership teams would want employees to come into the office – and establishing and maintaining a common corporate culture would be a leading reason. But what they need to do is stop pretending it is about “innovation”. Innovation is possible while working remotely, as it is when working from separate offices or even different floors within the same building. 

Evolving Employee Experience & Collaboration Needs

Organisations today face a challenge – and it is not the inability to innovate in a hybrid work environment! It is in their ability to deliver the employee experience that their employees want. This is more challenging now because there are more preferences, options, and technologies available. But it is established that organisations need to continue to evolve their employee experience.

Technology does and will continue to play an important role in keeping our employees connected and productive. AI – such as Microsoft Copilot – will continue to improve our productivity. But the management needs to evolve with the technology. If the senior management feels that connecting people will help to solve the current growth challenges in the business, then it becomes the role of managers to better connect people – not just teams in offices, but virtual teams across the entire organisation.

Organisations that have focused their energies on connecting their employees better, regardless of their location (such as REA in Australia), find that productivity and innovation rates are better than when people are physically together. What do they do differently?

• Managers find their roles have moved from supporting individual employees to connecting employees
• Documentation of progress and challenges means that everyone knows where to focus their energies
• Managed virtual (and in-person) meetings mean that everyone has a voice and gets to contribute (not the loudest, most talkative or most senior person)

Remote and hybrid workers are often well-positioned to come up with new and innovative ideas. Senior management can encourage innovation and risk-taking by creating a safe environment for employees to share their ideas and by providing them with the resources they need to develop and implement their ideas. Sometimes these resources are in an office – but they don’t have to be. Manufacturers are quickly moving to complete digital development, prototyping, and testing of their new and improved products and services. Digital is often faster, better, and more innovative than physical – but employees need to be allowed to embrace these new platforms and tools to drive better organisational and customer outcomes.

What the pandemic has taught us is that people are good at solving problems; they are good at innovating irrespective of whether their managers are watching or not.

While there has been much speculation about AI being a potential negative force on humanity, what we do know today is that the accelerated use of AI WILL mean an accelerated use of energy. And if that energy source is not renewable, AI will have a meaningful negative impact on CO² emissions and will accelerate climate change. Even if the energy is renewable, GPUs and CPUs generate significant heat – and if that heat is not captured and used effectively then it too will have a negative impact on warming local environments near data centres.

Balancing Speed and Energy Efficiency

While GPUs use significantly more energy than CPUs, they run many AI algorithms faster than CPUs – so use less energy overall. But the process needs to run – and these are additional processes. Data needs to be discovered, moved, stored, analysed, cleansed. In many cases, algorithms need to be recreated, tweaked and improved. And then that algorithm itself will kick off new digital processes that are often more processor and energy-intensive – as now organisations might have a unique process for every customer or many customer groups, requiring more decisioning and hence more digitally intensive.

The GPUs, servers, storage, cabling, cooling systems, racks, and buildings have to be constructed – often built from raw materials – and these raw materials need to be mined, transported and transformed. With the use of AI exploding at the moment, so is the demand for AI infrastructure – all of which has an impact on the resources of the planet and ultimately on climate change.

Sustainable Sourcing

Some organisations understand this already and are beginning to use sustainable sourcing for their technology services. However, it is not a top priority with Ecosystm research showing only 15% of organisations focus on sustainable procurement.

Technology Providers Can Help

Technology has been reshaping the Real Estate industry landscape. Advancements in manufacturing technologies, digital tools, AI & analytics, and IoT – coupled with customer and employee expectations – are revolutionising how properties are built, bought, sold, managed, and experienced.

The evolution of RealTech and PropTech has a far-reaching impact on the industry, streamlining processes, improving customer experiences, and driving innovation across the entire sector.

Technology is reshaping the Public Sector worldwide, optimising operations, improving citizen services, and fostering data-driven decision-making. Government agencies are also embracing innovation for effective governance in this digital era.​

Public sector organisations worldwide recognise the need for swift and agile interventions. With citizen expectations resembling those of commercial customers, public sector organisations face mounting pressure to break down the barriers to provide seamless service experiences.​

I have spent many years analysing the mobile and end-user computing markets. Going all the way back to 1995 where I was part of a Desktop PC research team, to running the European wireless and mobile comms practice, to my time at 3 Mobile in Australia and many years after, helping clients with their end-user computing strategies. From the birth of mobile data services (GPRS, WAP, and so on to 3G, 4G and 5G), from simple phones to powerful foldable devices, from desktop computers to a complex array of mobile computing devices to meet the many and varied employee needs. I am always looking for the “next big thing” – and there have been some significant milestones – Palm devices, Blackberries, the iPhone, Android, foldables, wearables, smaller, thinner, faster, more powerful laptops.