In my earlier post this week, I referred to the need for a grown-up conversation on AI. Here, I will focus on what conversations we need to have and what the solutions to AI disruption might be.
The Impact of AI on Individuals
AI is likely to impact people a lot! You might lose your job to AI. Even if it is not that extreme, it’s likely AI will do a lot of your job. And it might not be the “boring bits” – and sometimes the boring bits make a job manageable! IT helpdesk professionals, for instance, are already reporting that AIOps means they only deal with the difficult challenges. While that might be fun to start with, some personality types find this draining, knowing that every problem that ends up in the queue might take hours or days to resolve.
Your job will change. You will need new skills. Many organisations don’t invest in their employees, so you’ll need to upskill yourself in your own time and at your own cost. Look for employers who put new skill acquisition at the core of their employee offering. They are likelier to be more successful in the medium-to-long term and will also be the better employers with a happier workforce.
The Impact of AI on Organisations
Again – the impact on organisations will be huge. It will change the shape and size of organisations. We have already seen the impact in many industries. The legal sector is a major example where AI can do much of the job of a paralegal. Even in the IT helpdesk example shared earlier, where organisations with a mature tech environment will employ higher skilled professionals in most roles. These sectors need to think where their next generation of senior employees will come from, if junior roles go to AI. Software developers and coders are seeing greater demand for their skills now, even as AI tools increasingly augment their work. However, these skills are at an inflection point, as solutions like TuringBots have already started performing developer roles and are likely to take over the job of many developers and even designers in the near future.
Some industries will find that AI helps junior roles act more like senior employees, while others will use AI to perform the junior roles. AI will also create new roles (such as “prompt engineers”), but even those jobs will be done by AI in the future (and we are starting to see that).
HR teams, senior leadership, and investors need to work together to understand what the future might look like for their organisations. They need to start planning today for that future. Hint: invest in skills development and acquisition – that’s what will help you to succeed in the future.
The Impact of AI on the Economy
Assuming the individual and organisational impacts play out as described, the economic impacts of widespread AI adoption will be significant, similar to the “Great Depression”. If organisations lay off 30% of their employees, that means 30% of the economy is impacted, potentially leading to drying up of some government and an increase in government spend on welfare etc. – basically leading to major societal disruption.
The “AI won’t displace workers” narrative strikes me as the technological equivalent of climate change denial. Just like ignoring environmental warnings, dismissing the potential for AI to significantly impact the workforce is a recipe for disaster. Let’s not fall into the same trap and be an “AI denier”.
What is the Solution?
The solutions revolve around two ideas, and these need to be adopted at an industry level and driven by governments, unions, and businesses:
- Pay a living salary (for all citizens). Some countries already do this, with the Nordic nations leading the charge. And it is no surprise that some of these countries have had the most consistent long-term economic growth. The challenge today is that many governments cannot afford this – and it will become even less affordable as unemployment grows. The solution? Changing tax structures, taxing organisational earnings in-country (to stop them recognising local earnings in low-tax locations), and taxing wealth (not incomes). Also, paying essential workers who will not be replaced by AI (nurses, police, teachers etc.) better salaries will also help keep economies afloat. Easier said than done, of course!
- Move to a shorter work week (but pay full salaries). It is in the economic interest of every organisation that people stay gainfully employed. We have already discussed the ripple effect of job cuts. But if employees are given more flexibility, and working 3-day weeks, this not only spreads the work around more workers, but means that these workers have more time to spend money – ensuring continuing economic growth. Can every company do this? Probably not. But many can and they might have to. The concept of a 5-day work week isn’t that old (less than 100 years in fact – a 40-hour work week was only legislated in the US in the 1930s, and many companies had as little as 6-hour working days even in the 1950s). Just because we have worked this way for 80 years doesn’t mean that we will always have to. There is already a move towards 4-day work weeks. Tech.co surveyed over 1,000 US business leaders and found that 29% of companies with 4-day workweeks use AI extensively. In contrast, only 8% of organisations with a 5-day workweek use AI to the same degree.
AI Changes Everything
We are only at the beginning of the AI era. We have had a glimpse into the future, and it is both frightening and exciting. The opportunities for organisations to benefit from AI are already significant and will become even more as the technology improves and businesses learn to better adopt AI in areas where it can make an impact. But there will be consequences to this adoption. We already know what many of those consequences will be, so let’s start having those grown-up conversations today.
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.
In this Insight, guest author Anirban Mukherjee lists out the key challenges of AI adoption in traditional organisations – and how best to mitigate these challenges. “I am by no means suggesting that traditional companies avoid or delay adopting AI. That would be akin to asking a factory to keep using only steam as power, even as electrification came in during early 20th century! But organisations need to have a pragmatic strategy around what will undoubtedly be a big, but necessary, transition.”
After years of evangelising digital adoption, I have more of a nuanced stance today – supporting a prudent strategy, especially where the organisation’s internal capabilities/technology maturity is in question. I still see many traditional organisations burning budgets in AI adoption programs with low success rates, simply because of poor choices driven by misplaced expectations. Without going into the obvious reasons for over-exuberance (media-hype, mis-selling, FOMO, irrational valuations – the list goes on), here are few patterns that can be detected in those organisations that have succeeded getting value – and gloriously so!
Data-driven decision-making is a cultural change. Most traditional organisations have a point person/role accountable for any important decision, whose “neck is on the line”. For these organisations to change over to trusting AI decisions (with its characteristic opacity, and stochastic nature of recommendations) is often a leap too far.
Work on your change management, but more crucially, strategically choose business/process decision points (aka use-cases) to acceptably AI-enable.
Technical choice of ML modeling needs business judgement too. The more flexible non-linear models that increase prediction accuracy, invariably suffer from lower interpretability – and may be a poor choice in many business contexts. Depending upon business data volumes and accuracy, model bias-variance tradeoffs need to be made. Assessing model accuracy and its thresholds (false-positive-false-negative trade-offs) are similarly nuanced. All this implies that organisation’s domain knowledge needs to merge well with data science design. A pragmatic approach would be to not try to be cutting-edge.
Look to use proven foundational model-platforms – such as those for NLP, visual analytics – for first use cases. Also note that not every problem needs AI; a lot can be sorted through traditional programming (“if-then automation”) and should be. The dirty secret of the industry is that the power of a lot of products marketed as “AI-powered” is mostly traditional logic, under the hood!
In getting results from AI, most often “better data trumps better models”. Practically, this means that organisations need to spend more on data engineering effort, than on data science effort. The CDO/CIO organisation needs to build the right balance of data competencies and tools.
Get the data readiness programs started – yesterday! While the focus of data scientists is often on training an AI model, deployment of the trained model online is a whole other level of technical challenge (particularly when it comes to IT-OT and real-time integrations).
It takes time to adopt AI in traditional organisations. Building up training data and model accuracy is a slow process. Organisational changes take time – and then you have to add considerations such as data standardisation; hygiene and integration programs; and the new attention required to build capabilities in AIOps, AI adoption and governance.
Typically plan for 3 years – monitor progress and steer every 6 months. Be ready to kill “zombie” projects along the way. Train the executive team – not to code, but to understand the technology’s capabilities and limitations. This will ensure better informed buyers/consumers and help drive adoption within the organisation.
I am by no means suggesting that traditional companies avoid or delay adopting AI. That would be akin to asking a factory to keep using only steam as power, even as electrification came in during early 20th century! But organisations need to have a pragmatic strategy around what will undoubtedly be a big, but necessary, transition.
These opinions are personal (and may change with time), but definitely informed through a decade of involvement in such journeys. It is not too early for any organisation to start – results are beginning to show for those who started earlier, and we know what they got right (and wrong).
I would love to hear your views, or even engage with you on your journey!
The views and opinions mentioned in the article are personal.
Anirban Mukherjee has more than 25 years of experience in operations excellence and technology consulting across the globe, having led transformations in Energy, Engineering, and Automotive majors. Over the last decade, he has focused on Smart Manufacturing/Industry 4.0 solutions that integrate cutting-edge digital into existing operations.