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A new software era is here. It doesn’t just store your data, it does the work for you.

For decades, enterprise software was built around Systems of Record: authoritative databases where structured information lived. Think ERPs, CRMs, or EHRs. These systems were valuable because they democratised access to critical information. For startups, they were hard to displace. They became the default axis of influence in every organisation: inflexible, centralised, and (mostly) passive.

We believe AI is breaking that model.

We’re entering the age of the System of Work: software that doesn’t just manage data, it does the work. These AI-native systems generate the actual deliverables: the compliance document, the diagnosis summary, the customer reply, and the inspection report. They collapse the distance between insight and action.

This isn’t a UX upgrade. It’s a paradigm shift in how work happens.

What is a System of Work?

A System of Work will autonomously perform productive tasks. It’s not a place you go to see what needs doing, it’s the system that actually does it. That might look like:

• An AI agent that resolves a customer ticket end-to-end
• A generated legal contract tailored to a specific jurisdiction
• A clinical platform that drafts summaries and actions from messy encounter notes

Where legacy tools required human intervention to interpret and act, Systems of Work incorporate agentic behaviour: they act on their own, within guardrails. They represent a new kind of software stack: data, model, workflow and output, all in one loop.

These systems are defined by a few key traits.

• Agentic behaviour: They initiate actions rather than waiting for human prompts.
• Embedded actions: They do not just suggest what to do. They complete the task directly within the system.
• Workflow ownership: They become the go-to place where work is created, reviewed, and shipped.
• Outcome-based value: They are priced and evaluated based on the results they deliver, not just on features or access.

Why the record → work shift matters

In a world of Systems of Work, the traditional axis of influence in software is beginning to lose relevance. The classic “source of truth” may still exist somewhere in the stack. However, the source of productivity, the system where outcomes are created, will shift.

Once a system starts producing core business outputs, it becomes exponentially more valuable and much harder to displace. This creates a new kind of competitive advantage. Systems of Work do not only benefit from access to data. They generate new, proprietary data through use. They encode human expertise, automate repeatable decisions, and improve over time. These effects compound, and become self-reinforcing advantages that deepen with every interaction.

This shift also changes the economics of software adoption.

• From insight to execution: No more offline handoffs. Work gets done where the data lives and the value is immediate and obvious.
• From user-as-operator to user-as-editor: Humans go from clicking buttons and entering data to reviewing AI-generated output.
• From seat-based pricing to outcome-based monetisation: Business models shift toward per-task or per-output models, unlocking new budget lines tied to productivity.
• From “jobs to be done” to “roles to be done”: AI systems take over full professional roles, not just isolated tasks enabling full workflow ownership.
• From software tools to workflow axis of influence: These systems become the gravitational centre of daily operations. Frontline teams feel the value directly, not just IT buyers.

How Vertical AI is powering this shift

This transition is most visible in Vertical AI: AI products built specifically for the needs of one industry. These aren’t generic LLM wrappers. They embed into frontline workflows and solve the hard, boring, expensive problems unique to sectors like healthcare, logistics, construction, and financial services.

The most advanced Vertical AI systems:

• Combine deep domain knowledge with powerful models
• Build proprietary data moats by capturing interaction-specific context
• Deliver real-world value through actionable outputs, not just dashboards

And crucially, they don’t just make software smarter, they replace entire layers of human effort.

Industries will not just adopt AI. Industries will be restructured by AI

Systems of Work will drive the biggest changes in industries where work is still manual, complex, or repetitive.

Winning without replacing

Smart AI startups are using over-the-top (OTT) strategies to enter these workflows. Rather than attempting to displace existing systems of record, they adopt a wedge strategy to layer in value → starting with simple value-additive propositions like:

• A co-pilot layered on top of an EHR system
• A transcription layer that starts automating follow-up actions
• A reporting tool that gradually becomes the system of work for compliance

Once embedded, these tools don’t just assist, they absorb the workflow. Once the workflow is absorbed, the product has direct access to the tacit knowledge that the human uses to complete a task end-to-end. Access to this ‘grey’ data area helps feed and improve an autonomous neural network, which becomes an expert on the specific task required.

Final thought

The System of Record defined the last era of enterprise software. The System of Work will define the next. We don’t know what that means for Systems of Record, maybe some of them will be able to participate in the System of Work revolution as well. We are focused on finding the founders who are willing to tackle the ‘work’ problem from scratch. The pathways to achieving a System of Work are complex and multi-layered. The optimal way to build toward a System of Work will vary by use case, user archetype, and industry.

Join us from the leading edge of Vertical AI startups, as we publish our live thinking on building Systems of Work.

Being in the technology space for over 20+ years, I rarely get excited about the latest ‘tech’ cycle—I’ve found that they’re often more ‘novel-hype’ than substance. But the current wave of AI innovation is different.

I can’t overstate how quickly generative AI is materially changing the world around us and the way we interact with hardware and software. For those of us who witnessed the rise of the internet in the late ’90s or the ubiquity of mobile devices in the 2010s, you’ll likely agree that generative AI has accelerated with a much more meteoric ascent.

At Tidal Ventures, we use an ‘AI stack’ framework to structure our thinking and conversations. I’ll explain how we use it to frame our discussions around AI’s far-reaching investment implications, breaking down the layers that define this technology field and exploring their investment opportunities.

The AI stack

To effectively navigate the AI venture capital landscape, it’s crucial to understand the AI stack— which consists of three key layers: AI infrastructure, AI models, and AI applications.

AI infrastructure layer

At the base of the stack, you’ll find the infrastructure layer. This layer encompasses the essential hardware and cloud platforms that serve as the foundational building blocks upon which AI capabilities are constructed. It includes:

• Hardware infrastructure: Specialised accelerator chips optimised for model training and inference workloads.
• Cloud infrastructure: Comprising various cloud platforms that facilitate the large-scale computing required for AI-related operations.

AI models layer

The model layer plays a pivotal role in the AI landscape—these programs analyse datasets to find patterns and make predictions. The model layer encompasses:

1. Foundational models are large, pre-trained models that serve as the base. Technologies like Google’s Bard and Meta’s Llama are crucial in bridging the gap between raw computing power and practical AI applications.
2. Contextual models are trained on narrowed, industry-specific datasets. They are tailored to fulfil particular needs and act as the driving force behind various AI applications. Looks like BloombergGPT plans to do this for the finance world.
3. Local models are trained on localised, often proprietary datasets, further refining AI’s capabilities for specific applications. LifeLenz and PredictHQ are key proprietary data sets within our portfolio.

AI applications layer

The application layer is at the top of the AI stack, where companies develop AI products and services for end-users (think Chat-GPT). These applications utilise local, contextual, and/or foundational models to provide a wide range of AI-powered solutions.

Tidal Ventures’ AI investment mindset

Tidal believes that the expanding and ubiquitous nature of cloud computing has quietly propelled multiple waves of technological innovation. As cloud technology continues to gain widespread adoption and the cost of computing resources continues to decrease, a transformative shift will not only continue but is set to accelerate. This shift will fuel the rapid expansion of artificial intelligence across diverse industries and applications, impacting the products and systems we use today in our everyday lives, both at work and at home.

Our focus, like always, is to find the very best product, engineering and commercial operators who have identified globally significant problems to solve. We look to zero in on these opportunities, which we’ve seen within Australia’s vibrant, innovative environment and beyond.

At Tidal Ventures, we’ve been investing in predictive AI for the past eight years. Our current portfolio includes companies that are revolutionising various sectors through AI, including:

• BuildBetter: Automating and improving efficiency within workflows, providing insightful summaries, and enhancing product and customer operations.
• FrankieOne: Enabling contextual payment processing and real-time fraud monitoring crucial for regulatory compliance.
• Hone: Using their cloud-based Machine Learning and AI to enable farmers to make real-time decisions on grain segregation, optimising quality, and yield values.
• LifeLenz: Using their proprietary AI-powered forecasting model to establish a global benchmark in labour scheduling.
• PredictHQ: Leveraging proprietary data for improving economic value and enhancing services like Uber’s surge pricing.
• Search.io: Enhancing customer conversion through personalisation and transforming the e-commerce landscape.
• TheLoops: Providing SupportOps to improve customer experience and boost revenue, leading to customer delight.

LLMs

Today’s tech conversations seem to centre around large language models (LLMs). It looks like the obvious way an LLM would or wouldn’t win is by nailing their distribution model. Ultimately, we’re seeing the race to which LLM will be used at scale because the more an LLM is used, the more data it accumulates, which propels its self-learning cycle.

When viewing technology in terms of where we should be investing, new LLMs aren’t the clear choice due to their intense capital requirements and competitive environments. The LLM arms race has been underway for nearly a decade, and we don’t see a specific path to investing early-stage capital in this segment. The one area that may be compelling is the open-source LLM category, which, while early, is showing solid traction. In any case, we’re keeping a close eye on the downstream impacts of LLMs and how they may impact the other layers we are keen to invest in.

Contextual and local models

While LLMs seem to be in the spotlight, we’ve been focused on investment opportunities in contextual and local models. These models leverage rich, industry or topic-specific data and create value around that data while layering it on top of core LLMs. As you can imagine, the contextual relevance of these models is incredibly powerful within specific industries (like wealth management). Of course, you have to define the data set and then get the AI to learn from that data set to create a valuable output for an application or system.

When it comes to AI—primary data, while interesting, is just a ‘feed-source’ for the models. What actually matters is the value one creates around the primary data and then applies to specific situations. This can become incredibly valuable economically and productivity-wise for the end users, making it an intriguing investment area. Consider PredictHQ (from our Seed I fund); they own the world’s best predictive data models around how events impact certain businesses’ demand. These insights can then be used to power pricing systems, labour scheduling, stock ordering, etc. Prominent tech players such as Google, Facebook, and Amazon have built their entire organisation’s revenue models on collecting and optimising their systems to meet customers’ desired advertising outcomes. They have benefited enormously from this. These new contextual models have the potential to do that for all businesses, not just the ‘big tech’ platforms. All of a sudden, proprietary data sets are back in vogue.

Built-for-purpose applications

In the current landscape, the application layer of AI technology is a hotbed of activity. Many individuals and organisations are fervently exploring strategies to capture the attention of particular audiences and entice them to adopt applications that leverage the immense power of AI.

While there will always be a place for general-purpose applications like ChatGPT, StabilityAI, or Midjourney, the prevailing trajectory in the AI landscape is towards specialised solutions tailored to distinct sectors, categories, or teams. As such, we’re keeping a close eye on the emerging built-for-purpose products that utilise foundational models, potentially layer on their local or contextual data, and make them accessible through various applications.

AI in Australia

I recently sat down with Ryan Black, Head of Policy and Research at the Tech Council of Australia, to frame the local opportunity.

Ryan and I both agree that Australia’s power in the global AI ecosystem will undoubtedly lie in the application layer, particularly in generative AI. While it may not be at the forefront of developing foundational AI models or semiconductors, the country’s strengths in product and software development, cloud computing, and software as a service (SaaS) make it well-suited to harness AI’s potential for innovation and job creation—this emphasis on software-driven innovation positions Australia for success in the AI industry.

Companies like Atlassian and Canva have already begun leveraging AI, and Australia’s strengths in specific industries (like agriculture and hospitality) provide fertile ground for AI innovation. Australia’s journey in AI has the potential to entirely reshape the nation’s technology landscape and economy. The companies that are AI-first in their approach are particularly exciting, ushering in a promising era of innovation and growth in the Australian tech sector.

So whether you are a technology founder developing a product or an investor seeking to allocate capital, it’s imperative to consider how AI can substantially enhance products, services, and customer experience. Drawing on an impressive track record of investing in AI-based venture opportunities, Tidal Ventures is well-positioned to consistently identify and champion innovative ventures at the forefront of the AI revolution. Our unwavering confidence in the burgeoning technology landscape means we’re continually uncovering compelling opportunities within both the model and application layers.

We love nothing more than engaging on these topics within the broader business, investment, and technology communities. We believe that these discussions, debates and collaborations lead to some of the most exciting opportunities, so if there is something you’d like to discuss, please don’t be shy and reach out.

The transformative impact of artificial intelligence (AI) on various industries is undeniable. Tidal Ventures Managing Partner Wendell Keuneman recently hosted a panel discussion featuring leaders from portfolio companies—Flagship, Hone, and Lifelenz. This insightful conversation delved into the role of AI in each company, the innovative strategies they are employing, and the potential for AI to enhance the overall value of their products.

Tidal managing partner Wendell Keuneman was joined by:

• Peter Johnston (PJ), Managing Director of Hone from Tidal Seed Fund I. PJ gets to “work in a cool bit of science called spectroscopy where we build chemometric models. We measure stuff, and we’re good at measuring stuff. Rather than having to bring it to a laboratory, we enable measurement in the field. We overcome the problem of distance for many agricultural producers and other industries where they need real-time information to make good decisions”.
• Simon Molnar, the founder of Tidal Seed Fund III portfolio company Flagship—a digital visual merchandising platform, indicates that they “basically help retailers place products in their store and work out the optimum placement of those products”. “So if you have a lot of stores in your store network, you have a lot of products in those stores, then you have to make sure that you’ve got the right products in the right stores and the right products in the right place”.
• Steve Kirkby, founder of Lifelenz from Tidal Follow-On Opportunity fund I highlights their focus on workforce management and labour optimisation, ultimately “getting the right person at the right time for certain industries”. Steve outlined their current focus on quick service restaurants, “If you know someone working at McDonald’s or KFC or Hungry Jacks in Australia, they’ll be using our app’.

In a nutshell: What value AI unlocks

Hone

Hone’s ML engine accelerates the development of chemometric models, transforming what used to take weeks into a matter of hours. Moreover, it consistently produces more complex and robust models. With Hone’s cloud-based Machine Learning and AI, farmers can now make real-time decisions on grain segregation, optimising quality and yielding an average increase of 2% in farm gate values (net value of product after it’s left the farm).

Lifelenz

Lifelenz’s proprietary AI-powered forecasting model has established a global benchmark in labour scheduling, leading to a notable reduction in labour costs and a simultaneous boost in sales. This translates to a significant profitability enhancement, contributing up to 2% of EBITDA within the restaurant industry alone.

Flagship

Flagship’s AI-ready visual merchandising platform has significantly streamlined retail operations, cutting down manual efforts by two-thirds. This efficiency boost allows their workforce to redirect their efforts toward higher-impact tasks. Flagship anticipates substantial benefits for large retailers, estimating potential annual labour savings of 6,000 hours and an impressive 90% or more reduction in time to execution.

What role does AI play in each of your companies?

Hone

When it comes to Hone, Machine learning (ML) is core to what they do. As PJ indicated, “We’ve got these handheld spectrometers that farmers use where the machines collect raw spectrum, and then we run it through an ML engine and create calibrations from that data”. As Wendell astutely pointed out, “It’s always about behaviour change”. Hone has fundamentally changed behaviour around the process of chemical testing. Previously, chemical testing required two weeks to turn around, which resulted in expensive and infrequent testing, but with Hone’s cutting-edge hardware and software, they’re able to take a reading in the field rather than sending it back to the lab. “What that means is that frequency of testing becomes more common, more frequent, and as a result, the testing results in what we call currency—the economic value”, said Wendell.

The currency of Hone is the ability to understand yield right in the field. So they can start doing things like forecasting and uncovering the quality of that yield right then and there.

To illustrate this solution, consider GrainCorp who participated in Hone’s Series A funding round. They’re one of the largest grain traders in Canada and the US Eastern seaboard and faced a challenge due to the rapid growth of farms and machinery in recent years. Quicker harvests caused farmers to grapple with the issue of their headers filling up rapidly and requiring more frequent transportation. Historically, the header served as temporary storage for the grain before being transported to the depot where the machinery you need for grain quality inspections was usually held. To address this challenge, many on-farm storage facilities were constructed, and GrainCorp recognised the need to understand the quality of the grain closer to its point of harvest within these on-farm storage facilities. This became imperative as the traditional practice of testing the grain at the depot was no longer feasible. Enter Hone with their handheld spectrometers that farmers to collect raw spectrum that is then run through an ML engine and create calibrations from that data to help GrainCorp immediately understand the quality of each grain yield in the field.

Flagship

As Simon puts it, “I kind of see two different forms of AI. You’ve got internal AI and external AI”. At Flagship, “there’s a lot of ways that you can use it internally to create efficiencies. It takes a lot of the thinking effort away”, Simon says. Simon lists different uses like “creating outbound sales emails, replying to emails, generating content for us for taglines or LinkedIn posts, or you’ve got the engineering team using generative AI to create a framework for some code that they can then tweak, so a framework that would have taken them a significant amount of time now happens in a few seconds”.

From an external perspective, “we’re excited about how AI will continue to add value to our currency or value proposition”. Simon looks forward to instituting an AI capability that is “able to identify what products are working well in certain areas, what products aren’t working well in certain areas, and provide exceptional reporting or recommendations to optimise the layout of the store”. In the meantime, Simon touches on an AI capability that Flagship has started exploring “through our platform, we have images going back and forth between head office and their stores. We’ve started to run tests to map the directive image from the head office and the execution image from the store, highlight the differences between the images and give it a similarity score to identify if a store has executed correctly”.

Lifelenz

Lifelenz is known for its model that forecasts the “demand of a restaurant in 15-minute increments”; AI is at the core of this forecasting model. “Using our customers McDonald’s or KFC as an example. At every restaurant, 30 different models are running—including weather, local events, historical sales, and so on and AI is choosing from those 30 different models to say what they think the forecasted demand for this particular time is. The AI starts thinking about each 15-minute increment ten weeks out and is consistently self-learning and self-tuning. Steve highlights the true impact—“With precise forecasting, you can match your labour in a compliant manner to that forecasted outcome”. “In Australia alone, we’ve got over 2,000 restaurants with 30 models running every 15 minutes, meaning about 7 million models running a day coming up with these accurate forecasts, and we’ve been building our own with a model that optimises how the labour should be put into market”.

What impact does AI have on your customers?

Flagship

Simon Molnar reflects on the evolution of technology in the retail space: “There were a lot of different functions in the retail space where technology wasn’t an optimisation silver bullet, but I see this changing daily. Something as critical and labour-intensive as product descriptions can be created and optimised using AI. Now, with two clicks of a button, you can have all your content created for you. Or from a customer service perspective—you used to have a team of people at the ready to respond to customers because you needed that human-to-human interaction. Now, AI can formulate a sentence and create a conversation, and in many cases, the customer might not even know that they’re not speaking to a human.”

Simon Molnar challenges traditional notions in the industry: “A lot of people always thought that you needed people for visual merchandising. Humans physically go to each store to curate the layout and optimise the positioning of certain outfits. And as your business, your store network or even the size of your stores grew, you’d need more people—more people for a variety of roles including deciding how stores are going to look”. He highlights the transformative power of AI: “Now what we’re able to do is to create that underlying framework.”

Simon illustrates the practical applications: “So you have a new collection coming. You might know nothing about that collection or how it will perform, but every product has a product attribute. So, we can feed that into a pre-existing model and identify how attributes of each item are likely to perform and, as a result, where they should be placed. Head office no longer needs to wait weeks or months to amass the data to optimise a store; you can do that from the outset using historical comparisons.”

In conclusion, he emphasises the vast possibilities AI brings to Flagship’s offering: “That’s just one example of how Flagship will integrate AI into its offering, but the options are somewhat endless. There are so many different applications. It’s so versatile. And I would probably argue that there’s not a space or an industry that won’t be significantly impacted where some sort of efficiency is ripe for the taking.”

Simon illustrates this concept through a compelling example involving a US retailer with a sprawling network of 800 dispersed stores nationwide, currently employing 770 visual merchandisers, each earning an average of 75 to 85K USD annually. Simon emphasises the game-changing potential of Flagship for this retailer, stating, “By using Flagship, they can create automated directives based on real, localised data. They’ll likely be able to minimise their visual merchandising workforce needs by two-thirds, meaning their workforce can focus on higher-impact work.”

Providing essential context, Simon delves into the challenges businesses face in the vast and dispersed US market. He notes, “It’s near impossible to have people get out to every store because it’s so wide and spread out that you need that human manpower to get out to all the stores.” Simon wants to replace the traditional reliance on human labour for centralising and interpreting data and envisions a future where a self-evolving platform learns from its own contextual model, marking a significant departure from the current labour-intensive practices.

Continuing with his insightful perspective, Simon asserts, “I would say there’s a lot of industries that are going into a lot of roles that are going to be made faster in the same way that Excel would have made accountants faster.” He predicts a new wave of professions becoming more efficient, challenging the conventional belief that specific tasks inherently require a human touch. In Simon’s vision, the era of needing more people to handle increased revenue and store expansion is evolving. He signals a paradigm shift towards efficiency and automation in various industries.

Hone

Wendell asked PJ, “With agronomists and farmers testing more frequently, what is the real outcome when they can measure yield in the field?” To this, PJ responded, “We’ve been keenly watching what happens when technology and humans intersect.”

PJ then delved into the dynamic interplay of machine learning and human expertise, emphasising the unpredictable nature of an ML engine generating novel data. “An ML engine that produces novel data doesn’t have boundaries and can, therefore, be relatively unpredictable,” PJ explained. “But if you layer in a human who specialises in chemistry armed with a PhD, using AI to challenge their own hypotheses or assumptions, that’s a powerful situation to use AI to create a counterargument against the theory because that then gives you validation of whether it’s correct or not.”

Continuing the discussion, PJ highlighted the transformative impact on scientific validation, stating, “Previously, there was no way to do a peer review on a spectroscopy chemometric model across the last 65 years of spectroscopy and turn it around in 2 hours. And because that’s precisely what we do, we’re seeing an acceleration in our internal validation.”

As the conversation pivoted to the practical implications for Hone customers, PJ illustrated the potential for rapid decision-making. “Our customers that measure their grain quality can decide whether they put their grain in silo A, B, or C based on the quality within a short timeframe.” PJ further explored the scenario where widespread insights and AI applications enhance decision-making, stating, “What happens if all the grain on the Eastern seaboard within a district farmers had insights or machine learning and AI was laid over the top to say if farmer A takes grain from silo number one and mixes it with farmer B’s grain out of silo number five, the average value increases by $20 a ton.”

Lifelenz

Lifelenz, with its AI-powered forecasting capability, has garnered attention from executives seeking to reduce costs while adhering to compliant practices. Steve underscores the catalyst behind the increasing demand for Lifelenz, stating, “The C-suite is expressing considerable interest in Lifelenz, with a primary focus on labour optimisation. It goes beyond mere cost-cutting; it’s about ensuring the right person is scheduled at the right time, engaging in activities that ultimately enhance sales. This is a profit and loss driver, making it particularly appealing to the C-suite.” Additionally, The importance of the compliance framework has risen significantly among Lifelenz customers, given the intricate nature of industry compliance laws. Steve articulates, “Our software is gaining substantial attention because it automates processes and seamlessly integrates labour optimisation and compliance. Considering the substantial impact of labour costs, precision in this area is crucial for owner/operators.”

What is the most controversial part of AI for your customers?

Flagship

In the realm of retail, Simon sheds light on prevailing sentiment, noting, “I’ve seen a lot of fear and caution around AI taking people’s jobs. You can sense that wariness when we’re speaking to visual merchandisers.” Underscoring a widely acknowledged fact, he adds, “It’s no secret that when you have an intermediary function between two parties, there’s an opportunity for AI to disrupt. What I believe will be a bit more immune from an AI perspective is the human-to-human perspective. So any role, any service where it’s built on that human-to-human relationship is going to be a lot harder.”

In a nuanced perspective, Simon posits, “I could be eating my words in a couple of years, but I think the biggest piece that AI won’t be able to replace is how you make someone feel and the relationship and what you drive, and that emotion that you drive in someone. It’s why brands like Louis Vuitton, Chanel, and Gucci have built brands at such a premium price point: they’ve created an affinity and emotional connection with the brand. AI may be able to get you from A to B in terms of what you’re trying to achieve, but it’s yet to be seen whether it can evoke that same emotion that another human can.”

Hone

Adding another layer to the discussion, PJ provides an intriguing viewpoint: “Humans are overly protective of their intellectual property—there’s a prevailing sentiment that going against me is not a wise bet; we’ve been doing this for 100 years. It’s about whether people will take the leap of faith and trust the technology. I place more trust in the reliability of science than I trust most people. In the long run, trusting in technology is an investment that will safeguard human error.”

Lifelenz

At the crux of the matter lies the issue of trust, a sentiment echoed by Steve at Lifelenz. The contentious element surfaces at the operational level, where the team needs to trust the algorithms’ outcomes. It becomes imperative for the team to comprehend the value that AI brings to both them and the business. Take generated schedules, for instance; Steve says, “A lot of senior leadership, such as owners, operators, or executives, will instruct their teams not to touch the schedules; they’re perfectly generated. But individuals within their teams think they know better, and we’ve found that they’re wrong 90% of the time.”

Steve further emphasises:

We’ve learned over time that people start trusting the AI when they allow the algorithms to drive the business forward and generate better outcomes. When the staff are getting reliable work, when they’re getting all the right money, when the platform makes sure people are doing the right roles, getting the right training, identifying people who are perhaps not as good managers as they should be because of higher churn in their teams, etc. They’re the outcomes that will change how businesses see the software because it starts running the business well.

Hone, Lifelenz, and Flagship exemplify the versatility of AI applications, from revolutionising agriculture practices to reshaping retail strategies and optimising workforce management. As highlighted by the leaders, the integration of AI enhances operational efficiencies, fosters innovation, transforms traditional processes and improves decision-making. While concerns about job displacement and the challenge of building trust in AI algorithms persist, the overall consensus is optimistic, foreseeing a future where AI becomes invaluable in driving business success and adapting to the evolving landscape of technology and human collaboration.