??BEYOND DATA & AI TO VALUE: Now and the Future (PART 3) Extracting Value from Data & AI — MACHINES (generation) + HUMANS (validation)?

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This article is third in a 5-part series on BEYOND DATA & AI TO VALUE: Now and the Future.

BEYOND DATA & AI TO VALUE: Now and the Future (5-part series)

(PART 1) Data-Driven Enterprises and Organisations Outperform

(PART 2) From Data-Driven to Value-Driven

(PART 3) Extracting Value from Data and AI — MACHINES (generation) + HUMANS (validation)? ???

(PART 4) Future-Built Enterprises and Organisations — 'Value Makers' — Beyond Digital, Data and AI

(PART 5) Future-Built Enterprises and Organisations — The Importance of Trust

In this third part of the 5-part series on BEYOND DATA & AI TO VALUE: Now and the Future — (PART 3) Extracting Value from Data and AI — MACHINES (generation) + HUMANS (validation)?? — given all the data, AI and generative AI (genAI) hype, CEOs, Boards and executives are becoming impatient to see returns on their investments, yet many organisations are still struggling to prove and realise value. Actually demonstrating how your data and AI initiatives drive value still remains a major challenge for many. So, how can you overcome this challenge and realise the full potential of data and AI for your organisation? Extracting value from data and AI will be explored. Want to see value from data and AI? Start with business value.

Discover who are the AI Leaders and what they are doing differently. What are their secrets or secret sauce to data and AI success? Examples of AI Leaders will be provided who are generating value in core functions and business processes. The article further highlights the need for organisations to progress beyond incremental moves and embrace comprehensive data and AI strategies to U N L O C K transformative value — become more value-driven, Value Makers. Discover how high-quality data is outpacing large datasets to improve data and AI performance, ensuring accuracy, reliability, better outcomes and V A L U E. Understand the differences between Assistive AI and Agentic AI, along with AI Agents vs Agentic AI — agentic AI is one of the most transformative forces set to reshape enterprise technology landscapes — more importantly CEOs, Boards and executives need to understand these distinctions and why it matters, along with their associated risks, impacts and value. Both are valuable AI tools that are profoundly changing the world and shaping the future of business, industry, technology, society, humanity and our lifestyles — the way we live, work, rest and play. As AI continues to develop, the line between AI Agents and Agentic AI will blur even further as we head towards artificial super intelligence (or ASI) over the coming decade(s).

You will see that extracting value from data and AI — it's about MACHINES + HUMANS — or HUMANS + MACHINES working together — MACHINES + HUMANS symbiosis — to harness value from data and AI — MACHINES (generation) + HUMANS (validation)?. The key takeaway, the single most critical driver of [impacts, risks and] value from data and AI is NOT algorithms, or technology — it is the humans in the equation. Key to extracting value from data and AI and tap into its fuller potential is the ability to strike that perfect balance between HUMAN knowledge and MACHINE capabilities.

INTRODUCTION

Extracting Value from Data & AI

Modern organisations are collecting higher volumes and wider varieties of data than ever before in the history of business. Many enterprises and organisations have ambitious data and AI visions and goals for becoming data-, insights- and value-driven but struggle to translate them into concrete and measurable business value that aligns with their strategic objectives. This can lead to frustration, confusion and wasted resources.

As the scope of data and AI initiatives widen, the financial burden of preparing data, developing and deploying predictive and generative AI models is increasingly felt. Linking data and AI initiatives to business value is a critical challenge for many organisations, but it’s also a great opportunity to realise the full potential of data and AI.

CEOs have the capacity to harness AI for the growth of their organisations, as they have led multiple other growth strategies and programs. Whilst it follows fundamental business principles that apply to most growth or efficiency initiatives, implementing data and AI strategies does call for a mindset shift, changes in leadership, business model, operations, ways of working and organisational culture.

The sudden explosive growth of genAI, AIagents and agentic AI over the last 12+ months made it inescapable and ignited expectations about the power and potential of data and AI for business growth. CEOs and Boards view AI as the top technology that will impact businesses now and in the near-term and long-term future. Ignoring AI is not an option – but how to get through the hype and harness value from data and AI for corporate and organisational growth, innovation, value and success?

WHY

Why do you need to prove the value of data and AI?

Demonstrating that your data and AI initiatives drive value for your organisation is important for a number of reasons:

1. Strategic Alignment:?Firstly, to ensure your data and AI initiatives are relevant, impactful and aligned with your business priorities. Without a clear alignment to a strategy, data and AI initiatives can become disconnected, siloed and ineffective.
2. Winning over Stakeholders:?Secondly, to help to communicate the value proposition of data and AI to stakeholders, and to secure their confidence, buy-in and support.
3. Delivering and Measuring Value:?Finally, showing clear and measurable return on investment (ROI) for each initiative helps ensure you’re actually focusing on the right metrics. Too often, data and AI initiatives are measured by technical or operational metrics, such as data quality, data availability, or data processing speed, or how many AI agents, models and algorithms were created. While these are important, they don’t necessarily reflect the business value or the outcomes that the initiatives are supposed to deliver. By defining and tracking the ROI of your data and AI initiatives, you can ensure you’re investing in the right products and services and delivering value to your business and customers.

WHERE

Where's the value in Data and AI?

After all the hype over data and AI, the value is hard to find. Boards and CEOs have authorised investments, hired talent and launched pilots — but only 22% of companies have advanced beyond the proof of concept stage to generate some value, and only 4% are creating substantial value, according to the BCG research (2024).

As markets become more competitive, leveraging data and AI effectively to build strategic advantage can make all the difference. McKinsey (2022) estimates that leveraging internal data for sales and marketing insights can result in above-average market growth and increases of 15% to 25% in EBITDA. Generative AI (GenAI) and Large Language Models (LLMs) offer a new and unique way to extract this value, and training them on proprietary data to achieve specific business objectives could transform many organisations.

In stark contrast, AI Leaders expect to generate significant value — 45% more in cost reduction and 60% more in revenue growth than other organisations. They expect their ROI from data and AI initiatives in 2025 to more than double what other organisations expect from theirs.

AI Leaders are generating 62% of the value (cost reduction and revenue gains) in core functions and business processes (see Figure 1 below).

The Challenge

Actually demonstrating how your data and AI initiatives drive business value remains a major challenge for many CEOs, Boards and their organisations. So, how can you overcome this challenge and realise the fuller potential of data and AI for your organisation?

A major challenge for organisations arises in justifying the substantial investment in data, AI and GenAI for productivity enhancement, which can be difficult to directly translate into financial benefit. According to Gartner (2024) many organisations are leveraging GenAI to transform their business models and create new business opportunities. However, these deployment approaches come with significant costs, ranging from $5 million to $20 million+ (see Figure 2 below).

Unfortunately, there is no one size fits all with AI and GenAI, and costs are not as predictable as other technologies. What you spend, the use cases you invest in and the deployment approaches you take, all determine the costs. Gartner (2024) highlights, "whether you want to infuse AI everywhere, or you have a more conservative approach with a focus on productivity gains or extending some existing processes, each has different levels of cost, risk, variability, impact [and value]".

Regardless of AI ambition, Gartner (2024) research indicates AI requires a higher tolerance for indirect, future financial investment criteria versus immediate return on investment (ROI). Historically, many CFOs and Boards have not been comfortable with investing today for indirect value in the future. This reluctance can skew investment allocation to tactical versus strategic outcomes.

The Barriers

The Chief Executive Group and Cherry Bekaert (2024) convened a roundtable discussion of CEOs in November 2023. The roundtable participants represented a range of industries, from consulting and professional services to software, to insurance, to the manufacturing of auto parts and electronic components, and more. They talked through their expectations, experiences and challenges with data and AI.

The conversation revealed that when facing, evaluating and implementing data and AI, the CEOs’ top barrier is the lack of AI strategy (see Figure 3 below).

WHAT

Want to see Value from Data and AI? Start with Business Value

Like all investments and projects, data and AI pilots and implementations should start with a business value case, and asking many questions. The first and most important question to ask is 'what business problem or challenge are we trying to solve and why?', along with the use case(s), feasibility, solution evaluation, timelines and resource allocation that can be tracked through key metrics and milestones, which allow CEOs to decide to move forward and scale or move on to the next opportunity.

“CEOs need to start with the business value they want to achieve and reverse engineer through all the possible roads to take to get to that value,” says Cherry Bekaert (2024). All viable options should be considered, including not changing the current method. Whilst AI has become the top approach and technology, it may not be the only option to execute the program. Decide whether you need to be on the leading edge with the program in question, and if the answer is yes, select the AI option if it is feasible and will take you there.

Weigh the Importance of Urgency and Stakeholders’ Expectations

While the basic playbook for data and AI-driven programs may be similar as for other growth or efficiency initiatives, AI stands out as being one of the applications and technologies that consumers can experience themselves. This puts additional pressure and urgency on organisations, as consumers, B2B partners and employees expect companies to provide them with the type of experiences that keeps up with what they encounter in their everyday lives.

Make Decisions in the Context of Your Business

While the overall paradigms of decision-making are the same, each situation is specific not only to the organisation but also to its organisational maturity, ability to adapt and handle change, available resources (both budgets and talent), existing competitive pressures and organisational size and culture.

So often AI journeys are talked about in the context of a big enterprise. Also need to consider what AI means for mid-market or small-to-medium businesses (SMBs) and government / public services organisations.

Good Data: A Key Ingredient in AI Success

AI success stories share one common ingredient: good data. Enterprises and organisations have spent years capturing operational, customer and market data, mindful that it could later be analysed for business insights. As AI entered mainstream conversations, organisations with good quality data were well-positioned to achieve quick wins because they had strong data foundations in place.

When ChatGPT entered the public domain, many AI use cases organisations had trusted for years were overshadowed by the genAI hype. Suddenly, organisations that had not previously used AI were wowed by auto-generated text, auto-generated images and customer service chatbots. They and their boards quickly realised that their organisations needed to leverage AI. However, all they knew was ChatGPT, and they have struggled to understand how else AI could help their organisations grow.

The truth is, without good data, AI can't help much.

Standardise AI Costs and Value Metrics: AI Pricing is different

As AI adoption continues to grow, there’s an increased risk of costs negating the technology’s ROI and business value. AI Pricing is different from regular software or platform pricing.

AI Pricing uses metrics like tokens, character counts or user-based fees, whereas most other software is available via simple subscription or one-time fees. This makes AI pricing more complex and harder to predict. Understanding these distinctions helps inform better AI cost estimation and avoidance of unexpected or hidden costs.

AI Pricing fundamentals - some simple steps to help enterprises and organisations manage AI platforms, software and tools costs:

1. Clarify AI pricing metrics.

2. Standardise AI cost assessments.

3. Negotiate for AI pricing scalability and transparency.

4. Negotiate AI price holds and cost ceilings.

5. Understand GenAI pricing complexities.

6. Mitigate AI pricing financial risks.

There are many other AI costs to factor in, some may be hidden costs for predictive and generative AI such as: expenses for data labeling, ongoing training, maintenance and more. Whilst software, infrastructure and compute costs are often taken into account, organisations must carefully navigate hidden costs beneath these visible expenses. Doing this helps create a well-planned budget and ensure sustainable implementation of data and AI - predictive and GenAI. Learn how to protect your organisation from cost overruns - for further information see link from Gartner CFO & Finance Executive Conference, Sept 2024, London https://lnkd.in/gAVcMcUY.

WHO

Who are the AI Leaders?

Although we live in an increasingly data and AI-driven world, most organisations do not operate data and AI-driven business models. The virtuous circle of network effects driving the success of enterprises, examples of AI Leaders, like Amazon, Alphabet, Meta, Google and Microsoft — who are considered tech giants — are not available to organisations selling traditional products and services. However, the tools to get more from the proprietary data you generate from everyday business processes are becoming widely accessible, and could help your organisation develop a competitive edge.

For more on the tech giants, to get a more detailed view of Alphabet + Google and Microsoft and how they are using AI to create better products and services, as well as supercharge their market share and profits - click on the Article link — THE VALUE OF AI: now and the future (PART 2) AI Failures, Pitfalls, Key Learnings and Success — and scroll down to the article sub-heading 'AI Success - Learning from AI High Performers'.

Here’s how one of the AI Leaders and high performers — Amazon — is using artificial intelligence to create better products and services, as well as supercharge its market share and profits.

Amazon and Artificial Intelligence

Amazon is a trillion-dollar company, thanks to artificial intelligence. Jeff Bezos and Amazon was one of the first companies to build their business around AI and machine learning. Amazon has always had a significant competitive advantage. Amazon significantly invested, adopted and embedded narrow AI, ML and robotics early on across its entire business and business model and has not stopped its growth, innovation and sky-rocketing profits and market domination ever since - all powered by AI. Amazon is a company that has reorganized and restructured itself to leverage artificial intelligence in every part of the organisation — integrating AI from Top to Bottom. Amazon’s recommendation engines are now driving 35% of total sales. Not only has it been using AI to enhance its customer experience but has been heavily focused internally. From using AI to predict the number of customers willing to buy a new product to running a cashier-less grocery store, Amazon's AI capabilities are?designed to provide customized recommendations to its customers.

"I would say, a lot of the value that we’re getting from machine learning is actually happening beneath the surface. It is things like improved search results. Improved product recommendations for customers. Improved forecasting for inventory management. Literally hundreds of other things beneath the surface"

- Jeff Bezos, Amazon Founder and Executive Chairman

So, what is Amazon’s secret sauce, besides being an early adopter and being powered with AI across the entire business? How have they integrated artificial intelligence into their business so successfully? Here is the key: Amazon uses a “flywheel” approach to artificial intelligence. “Flywheel” is an engineering term that describes the way companies can conserve energy and keep up momentum. Flywheels are similar to potter’s wheels — we can press a pedal and keep them running consistently by adding a tiny amount of energy at a time. Like a flywheel, using artificial intelligence takes a lot of energy to get started — but once the wheel begins turning, it is far easier to keep it going by giving it continuous smaller boosts. Artificial intelligence has its own momentum and Amazon created a plan to keep up that momentum within that organisation, so their efforts never lagged. This is a long-term strategy that ensures maximum benefit from AI efforts.

Amazon’s entire organisation is constantly humming with artificial intelligence, and founder Jeff Bezos mandated that data is shared across the organisation, not hoarded in one department or process. Datasets are always connected to other data in the organisation, to make sure they can be externalized from the ground up. The flywheel of continuous data and AI keeps different parts of the Amazon engine going, and innovations that take place in one department or team can be transferred to other parts of the organisation.

Amazon also has an impressive 97.26% grant rate of its patents. Amazon’s biggest patent share can be found in Logistics and Artificial Intelligence.

"Amazon has filed 14,316 patent applications at USPTO so far (Excluding Design and PCT applications). Out of these 12,764 have been granted leading to the grant rate of 97.26%. Not only this, the application Abandonment/Rejection rate of Amazon is meager at 2.47% and only 299 applications have faced abandonment or rejection".

– Insights by GreyB

Amazon's AI Products

1. “Anticipatory shipping” is Amazon’s patented feature that allows products to reach your nearest possible location before you actually place the order to purchase the product. This forecasting is powered by AI which is also the underlying technology for its Prime Now service that facilitates one-hour deliveries. Amazon calculates the number of drivers required to make deliveries using an app called Flex that depends on AI. The app considers many factors like the number of packages in the same locality, the weight of the package, etc to make every minute count.

2. Amazon also uses an AI-powered dynamic pricing algorithm to get an edge over competitors. The algorithm uses AI to enable optimal sales and revenue automatically by decreasing the prices of the products to increase sales when it’s needed and vice-versa.

3. Amazon’s AI-backed recommendation engines generate 35% of the total revenues. This algorithm uses data from customer’s previous purchases, preferences, browsing history, search history to create a list of personalized products that a customer will likely buy.

4. The company’s AI-powered sampling strategy uses infrastructure and product purchase data to identify products that each customer is likely to buy. The company then sends samples of new products to customers that chosen by machine learning models. This has been implemented on Prime subscribers.

5. Amazon’s check-out free physical stores have AI cameras and sensors that charge a customer automatically when they walk out of the store with the product using the Amazon Go App.

6. Alexa is also powered by AI and has helped many companies add value to their customer service.

Jeff Bezos founded Amazon in 1994 out of his garage with a US $10,000 investment which he borrowed from his now ex-wife.

Jeff Bezos became a millionaire in 1997, at the age of 33, when Amazon's IPO raised US $54 million. Two years later, he became a billionaire, at the age of 35.

Jeff Bezos net worth in 2009: US $6,800,000,000 (US $6.8 Billion)

Jeff Bezos net worth in 2021: US $191,300,000,000 (US $191.3 Billion)

Jeff Bezos net worth in 2025: US $250,100,000,000 (US $250.1?Billion)

Founder and former CEO Jeff Bezos is the largest Amazon shareholder, with more than 937 million shares, which represent a stake of about 9% stake in the company. Bezos' shares are worth an estimated US $166 Billion (as at end Jan 2025).

Other AI Leaders Examples

The following AI Leaders are examples of those who got in early, digital natives, starting off small(er) and then accelerating their growth, putting AI at the center of their business model and operations - they are innovative, data and AI-driven. Whilst they are both defined by their early successes, and they have experienced significant challenges along the way and more recently, despite this, they are today still considered AI pioneers and are excellent data and AI case studies and real world business examples that some may not be as familiar with that all organisations can learn from.

• ANT Group – formerly known as Ant Financial, is an affiliate company of the Chinese conglomerate Alibaba Group. The group owns the world's largest mobile (digital) payment platform Alipay, which serves over 1.3 billion users and 80 million merchants, with total payment volume (TPV) reaching CN￥118 trillion in June 2020. It is the second largest financial services corporation in the world, behind Visa. A subsidiary of Alibaba (founder: Jack Ma) transforming the financial industry, Ant Financial (2014-2020) utilised data and AI to target individuals and small businesses initially in regional China, and beyond China. Ant Group traces its roots back to Alipay, which was established in 2004 to create trust between online sellers and buyers. Ant Financial's operating model is legendary, the 3-1-0 lending model:?borrowers can complete?online loan applications in 3 minutes, obtain approval in 1 second, with 0 human interactions. In October 2020, Ant Group was set to raise US$34.5 billion in the world's largest IPO at the time, valuing the company at US$313 billion. On the eve of the IPO, China stopped the process from moving forward. On 12 April 2021, The Wall Street Journal reported that under the pressure from the Chinese government, Ant Group would be transformed into a financial holding company overseen by the People's Bank of China. Over the years, Ant Group has grown to become one of the world's leading open Internet platforms. Today, Ant Group has developed a comprehensive ecosystem that integrates various financial services, including payments, insurance, investment platforms and lending. Services like MyBank and Alipay are interconnected, providing a seamless financial experience for consumers and small businesses alike. This ecosystem supports a wide range of activities from daily transactions to more complex financial needs, facilitating global payments, food delivery and access to microloans.
• Stitch Fix – is an online personal styling service in the US and UK.? It uses recommendation algorithms and Machine Learning (ML) to personalize clothing items based on size, budget and style. The company was founded in 2011 and had an initial public offering in 2017 with a valuation of $1.6 billion. The company was built on data and AI from the ground up, allowing it to provide a personalized styling service and ship clothes directly to customers’ doors – clothes that they know clients will love based on their style preferences and previous choices. Stitch Fix is sitting on a goldmine of nearly 4.5 billion text data points that clients have shared with the company. GenAI helps Stitch Fix leverage this complex, messy data because generative AI excels at quickly making sense of and summarizing vast amounts of text data. But what makes Stitch Fix really special is its ability to strike that perfect balance between human knowledge and machine capabilities. Stitch Fix is also harnessing genAI to streamline processes and provide a better service to customers. Stitch Fix uses LLMs from OpenAI, combined with their own deep learning recommendation algorithms, to interpret the feedback that clients share and then use that information to inform future recommendations. Another interesting use is Stitch Fix’s Outfit Creation Model (OCM), which uses genAI to generate millions of new outfit combinations per day. OCM, which was trained on millions of outfits created by stylists, picks items from Stitch Fix’s current inventory, as well as from customers’ previous purchases, to compile personalized outfit suggestions for clients. Whilst its net worth today has reduced as it has increased its operations and employees, it is estimated at $597.43 million as of January 31, 2025. Stitch Fix today navigates a more challenging retail landscape with a transformation strategy designed to revive its financial performance and client engagement, its newer CEO Matt Baer is confident the company will return to revenue growth by the end of fiscal 2026. If you want to learn more about their data and AI foundations and landscape, how they operate and what is 'under the hood', refer to Stitch Fix Engineering.

Other Data & AI Value Examples

Large enterprises and organisations have achieved significant gains with AI and data.

• Walmart, for example, uses AI to analyse 850 million product data points to improve both the worker and customer experience. Walmart recently unveiled its strategy to accelerate Adaptive Retail, the new era of retail defined by profoundly personal experiences that brings shopping to customers in exactly the ways they want and need. The company revealed proprietary AI, GenAI, Augmented Reality (AR) and Immersive Commerce platforms it is leveraging to create hyper-personalized, convenient and engaging shopping experiences across Walmart stores, Sam’s Clubs, apps and other virtual environments. Walmart has developed an AR platform called Retina, which leverages AI, GenAI and automation to create tens of thousands of 3D assets, along with Immersive Commerce APIs. These technologies enable the company to bring the Walmart shopping experience into new virtual social environments, unlock new revenue streams and be at the forefront of Adaptive Retail.
• Bank of America uses AI to help detect fraud, which it estimates saves both the bank and customers about $100 million in fraud per year. AI Patents at Bank of America increased 94% since 2022. The company’s patent portfolio includes nearly 1,100 AI and ML patents. Overall, the bank holds nearly 7,000 granted patents and pending patent applications, and the most granted patents of any financial services company. This is thanks to the creativity of its more than 7,500 talented inventors based in 14 countries and 42 U.S. states, and a culture that empowers teammates to explore and develop innovative solutions for clients and businesses around the world. Bank of America spends over US $12 Billion annually on technology, of which approximately US $4 Billion will be directed to new technology initiatives in 2024. These ongoing investments continue to enhance client experiences and to drive operational efficiencies. Bank of America’s approach to AI includes human oversight, transparency and accountability for all outcomes. A few examples of how AI and machine learning are being used responsibly across the company to benefit clients and employees include:? Erica??– More than 45 million clients use Erica, the most advanced and first widely available AI-driven virtual financial assistant. This massive adoption has led to 2.4 Billion interactions?with Erica since its launch in 2018. Over the last 6 years, Erica’s capabilities have expanded to support individual and corporate clients, including within Merrill, Benefits OnLine?, and the company’s award-winning CashPro? platform.??CashPro Chat?– CashPro is a digital banking platform used by 40,000 corporate and commercial clients around the world to manage their treasury operations. As the platform’s virtual service advisor, CashPro Chat uses the same AI and ML capabilities powering Erica to help clients quickly view transactions, find information about accounts, and more. The questions CashPro Chat can assist with have doubled since its launch last year, and the capability now includes “intelligent advisor routing” – an AI-enabled feature that quickly recognizes complex requests and routes clients to a live specialist with expertise in that area when needed and with a single click.
• AI gets real as big business finds its feet — Salesforce, ANZ, Telstra, Zurich Insurance — at some of Australia's biggest companies say the rapid advances in data and AI are beginning to bring tangible changes in their business and their operations, as an era of pilot programs and low-stakes experimentations gives way to?serious business applications (AFR, 2024) www.afr.com/technology/ai-gets-real-as-big-business-finds-its-feet-20241106-p5kois.
• ANZ, also referred to as ANZ Bank, is an Australian multi-national banking and financial services companies (one of Australia's big 4 banks), uses AI to speed up the work of its software developers, by converting their code into multiple programming languages, and is looking at ways it can speed up loan approval processes by “understanding” the intent in documents. ANZ has deployed Github Copilot to 3,000 of their software developers and engineers to assist with generating code. Early tests measured engineers completing some programming tasks 40% to 55% faster, leading to big increases in the amount of code produced and the quality of that code.? Another example of AI at ANZ is the integration of their recent acquisition of Suncorp Bank. The transaction was completed at the start of August 2024 and they are using AI to help integrate Suncorp Bank and ANZ policies and procedure by reducing the time to compare, contrast and harmonize thousands of documents, terms, conditions and contracts. Using this method to scan customers transactions and financial data, ANZ can identify customers at risk of distress about 40 days earlier and then proactively reach out to assist them. ANZ are also using AI to make high-value transactions even more secure. AI is helping to combat fraud and detect advanced threats – including when a fraudster tries to access an account or when criminals try to launder money. ANZ's well-known “Falcon" fraud-detection system is fuelled by AI.
• CBA, also known as Commonwealth Bank or simply CommBank, is an Australian multinational bank with businesses across New Zealand, Asia, the United States, and the United Kingdom — Australia's largest bank (one of Australia's big 4 banks) — rolls out AI agent for tens of thousands of its business customers as it extends a cloud deal with AWS and show its might in the AI race. CBA is using a genAI tool built by AWS to streamline and automate regular reviews of its cloud-based workloads that aid resiliency, security, efficiency and other architectural improvements. CBA plans to use AI across ‘entire software delivery’. The strategy is being driven by CBA’s AI-powered engineering team. The bank has been working towards amplifying its generative AI efforts with the emerging technology already embedded into a number of its customer-facing capabilities. Having started experimenting in May 2023, the bank already has over 50 GenAI use cases.?Other projects that have come to fruition include the bank's IT support chatbot, ChatIT, and a number of?customer-facing?AI-powered features.? CBA has been recognised in the top five banks globally for artificial intelligence (AI) maturity, moving up one place, and maintained #1 position in APAC in the Evident AI Index (2024). AI is transforming CBA customer experiences (~ 10 million customers): - 50% reduction in customer scam losses, aided by the implementation and use of?safety and security features?which use AI, including NameCheck, CallerCheck and CustomerCheck. - A 30% drop in customer-reported frauds using Gen AI-powered suspicious transaction alerts. - AI-powered app messaging - reduce call centre wait times by 40% over last financial year. - AI Chatbot built on OpenAI ChatGPT4 - IT support chatbot, ChatIT, and a number of?customer-facing?AI-powered features. - AI Factory with AWS. - 50 plus GenAI use cases support CBA frontline to serve customers – H2O.ai powered environment for LLMs. - CBA partnered with Microsoft (GenAI partnership), AWS, NVIDIA, University of Adelaide (boost foundational AI research – ML and fraud detection).

• Coles, Australian supermarket retail giant has been pioneering the use of data and AI with a visionary approach for nearly a decade. Its AI journey, which started with an ambition to transform operations, has matured into an impressive suite of AI models that now drive the day-to-day engine of its sprawling operations. This AI-backed operational efficiency helps Coles predict the flow of 20,000 stock-keeping units (SKUs) to 850 stores nationwide with astonishing accuracy, harnessing insights from over 2,000 diverse datasets to make 1.6 billion predictions each day. Much more than mere numbers, these are the heartbeats of a system ensuring that every Coles customer finds exactly what they’re looking for, exactly when they need it (Coles and Microsoft, 2024). Coles has also recently launched an all-in-one AI-powered Smart Trolley trial designed to enhance in-store shopping experiences through technology. "Coles share price rise is partly due to its ability to take advantage of tech like artificial intelligence to stop thieves and appeal to more shoppers" (AFR. 2024).

With examples like these, CEOs, Boards and executives are right to be excited about data and AI. Yet, many data and AI projects fail. One of the key reasons, according to a Rand report (2024), is that organisations lack the necessary data. That’s only partially true. The data exists; the challenge lies in how organisations value and manage it.

What do AI Leaders do differently?

AI Leaders differentiate themselves from other organisations in the following ways:

1. They have an 'insights-' and 'value-driven' Data and AI vision, strategy, mindset, culture, leadership, ways of operating and ways of working.
2. They know to progress beyond incremental moves and embrace comprehensive data and AI strategies to U N L O C K transformative value.
3. They focus on core business processes and functions, seeking to deploy data and AI for productivity, to reshape processes and functions, and to invent new revenue streams.
4. They are more ambitious, setting big targets ($1 Billion in productivity improvements at a financial institution, for example, or $1 Billion in combined revenue increases and cost reductions at a biopharma firm) and investing in data, AI and workforce enablement.
5. They invest strategically in a few high-priority opportunities to scale and maximise data and AI value.
6. They integrate data and AI in both cost reduction and revenue generation efforts.
7. They focus their efforts on people and processes over technology and algorithms.
8. They have moved quickly to focus on GenAI, which opens opportunities in content creation, qualitative reasoning and connecting other tools and platforms.
9. They get that its about MACHINES + HUMANS — or HUMANS + MACHINES working together, and the single most critical driver of value from data and AI is NOT algorithms, or technology — it is the human(s) in the equation.
10. They know that good data and data quality is key, along with translation and understanding of the data. To enable this they get that it is important to have cross-functional teams working together — business architects, business analysts, business subject matter experts (SMEs), working together with data analysts, data scientists — instead of working in silos.
11. They know the importance of having a 'value-driven' Data and AI Leader — Chief Data and AI Officer (CDAIO) — along with a value-driven data and AI-savvy CEO, Board and senior executives. They understand that whilst their Head of Data and Head of AI or CDO, CAIO, Chief Data Scientist and Data Scientists are a core part of the team, they should not and do not drive the data and AI vision, strategy and value; instead they report into the CDAIO; the CDAIO reports into the CEO and Board.
12. They get that they are 'Value Makers' (not just data-driven) and focused on 'Value' with value-driven Data and AI Strategy for their future-Built Enterprises and Organisations.

PREDICTION

Increasing emergence of Chief Data and AI Officer (CDAIO); and eventual convergence with CEO role in future-built enterprises and organisations

Today we have many Chief Digital Officers (CDOs), Head of Data or Chief Data Officers (CDOs), Head of AI or Chief AI Officer (CAIOs) and Chief Data Scientists, where these digital, data and AI leadership roles, accountabilities and value are separated.

In the near future the increasing emergence of the Head of Data and AI Strategist and/or Chief Data and AI Officer (CDAIO) role. In addition, as we progress over the next decade in future-built enterprises and organisations, the Chief Data and AI Officer (CDAIO) and the CEO role will eventually converge and become one - where the data- and AI-savvy CEO will be the one driving the data and AI vision and grand strategy. Most of our organisations are not even close to being there yet. Most CEOs and Boards of modern business today (with the exception of the few AI Leaders/bigtech/digital natives) most at the CEO and Board or more traditional organisations still do not have in-depth data and AI strategy understanding, experience and hands-on skills and capabilities.

HOW

How to get more value from data and AI

Everything enterprises and organisations do generate are data point(s) that can be tracked, organised and stored for future analysis. Data, as we know, is everywhere, and it holds incredible potential to inform business strategies and deliver value.

When it comes to AI, the issue isn’t a lack of data but the quality and management of it. The organisations best insulated against data and AI "disillusionment" are those whose data was organised, cleansed and ready to quickly deploy AI applications that could drive business outcomes.

Others can find similar success, but they must take critical steps:

1. Identify the business problem(s) or challenge(s) that you are trying to solve

Using technology for technology’s sake never works. That sort of tech-first mindset is a trap that leads to solutions in search of problems, which is a waste of both time and resources. By focusing on the business challenge(s) and the outcome(s) you hope to achieve, you can begin to sift through the technologies at your disposal to see which offers the best and fastest path to success.

2. If AI is part of the solution, view GenAI as just one component in a broader 'value-driven' data and AI approach

This was noted by Gartner (2024) in its AI Hype Cycle. ChatGPT opened many organisations eyes to the world of AI, but data analytics and AI have been around for decades. "Classic AI", for instance, is a powerful tool for making data-based predictions, yet some companies may overlook it amid the GenAI hype.

However, in my experience, forecasts are largely what organisations want, at least initially, as they seek to make more informed and faster business decisions. Where GenAI might play a role is in making that analysis or those troves of data more accessible through natural-language queries. GenAI alone won’t save the day, but it can accelerate the outcomes of a comprehensive AI solution - AI Agents, Agentic AI, hybrid AI, multi-modal AI.

Hybrid AI is a cutting-edge technological approach that combines different types of AI technology to enhance their abilities, performance, versatility and problem-solving.

Rather than relying on a single method, hybrid AI integrates various systems, such as rule-based symbolic reasoning, machine learning and deep learning, or predictive and generative AI, to create systems that can reason, learn, and adapt more effectively than AI systems that have not been integrated with others.

3. Treat data as the vital resource it is

Any AI output is only as strong as the data that informs it. With both predictive AI and generative AI, if a model is trained on bad or incomplete data, how can users expect the eventual output to be any better? As they say: garbage in, garbage out. For an AI initiative to succeed, organisations must identify the data required to deliver the most informed decisions.

4. Avoid the common Pitfalls, learn from (yours and others') Data and AI Failures, Key Learnings and Success

Part of the hesitation when embarking on data-, insights- or value-driven AI programs are the misgivings that CEOs and Boards have about the quality of data. During the Cherry Bekaert (2024) roundtable, CEOs discussed data silos, inconsistent formats and overall data integrity. Data-related issues, such as quality and availability of data and data management ranked high on the list of top barriers to AI implementation - see Figure 2. While stressing the need for long-term strategic planning in data management, Cherry Bekaert (2024) also advocate for potentially using AI to tackle unstructured data. AI proficiency with unstructured data opens new avenues for data utilisation that traditional data management systems may not fully exploit.

For some further information on fundamental pitfalls and key learnings to consider around data and AI, when adopting and building AI to avoid some of the early and major pitfalls [that often lead to failure] in your journey towards realising data and AI potential and value that can positively revolutionise your organisation — refer to article link on THE VALUE OF AI: now and the future (PART 2) AI Failures, Pitfalls, Key Learnings and Success. Whilst it is not meant to be a definitive list of data and AI pitfalls and key learnings, these however do cover some of the most common and major pitfalls - providing a detailed list and insights on the top 20 major (data and) AI Pitfalls to avoid and key learnings.

5. Value-Driven: Think more like a Value Maker

Whilst most are aware of AI and genAI’s risks - i.e. AI-suggested outputs, there is potential for contextual misunderstanding, biased results, or hallucinations “hallucinating” facts and figures, this has meant organisations proceeding with caution has been the norm. ?Whilst Proof-of-Concepts (POCs) can be useful, they are still today too often developed in tech-related functions, not core business. POCs are not enough. By focusing on proving the tech, organisations are not addressing the bigger question - what would it take to have business impact?? The models at heart of genAI use cases represent small fraction of effort involved in successfully building and deploying solutions at scale, in a way that creates value. Avoid the trap of treating genAI like a 'hammer looking for a nail' – looking for ways to use genAI even when it may not be the right tool for the job.

Think more like a Value Maker. Organisations can benefit from thinking through their overall Data and AI strategy, including genAI strategy in more depth. What is the economic, customer and employee value of genAI ? Lift sights from specific AI use cases | POCs and think about whole business domains and functions. Take a single part of the organisation and reimagine how it might work in 3 to 4 years if it is fully AI-driven. This perspective anchors on the business and seamlessly combines AI, genAI, digital and more. At use case or domain level, integrating genAI based on today's day-to-day operations is where most go wrong. It is important to involve the business from the beginning.

Value Makers: Value-Driven Data and AI Strategy for Future-Built Enterprises and Organisations

The BCG (2024) Built for the Future (BFF) study further highlights the need for most organisations to progress beyond incremental moves and embrace comprehensive data and AI strategies to unlock transformative value across sectors. While organisations have made impressive progress in digital and AI capabilities, there remain opportunities to further enhance their maturity levels in critical areas and continue building on their strengths to lead globally in digital transformation and AI adoption.

To bridge the global maturity gap and accelerate impact, organisations must embrace a bold, digital and AI strategy, across the following key recommendations:

1. Re-align organisational strategy with a value-driven data and AI vision to overcome organisational cultural and structural barriers like operational agility and talent development.
2. Set a bold strategic ambition for AI adoption focused on clear value pathways and guardrails for responsible AI adoption.
3. Boost viable people and org capabilities and underlying technology platforms to support ambition and invest in parallel to scale up.
4. Maintain a pipeline of continuing innovation pilots to rapidly and effectively adapt to changing landscape of emerging technologies.
5. Prioritise high-profile cross-cutting lighthouse initiatives with high ROI to fund the journey and build momentum for transformational organisation-wide change.

By addressing these priorities, enterprises and organisations can unlock transformative potential, enabling them to capitalise on emerging opportunities, catch-up to global peers, and earn their position as future-ready pioneers in an increasingly data and AI-driven digital world.

To read more about future-built enterprises and organisations refer to the next article (PART 4) Future-Built Enterprises and Organisations — 'Value Makers' — Beyond Digital, Data and AI — link to be provided once published, stay tuned.

6. Mind the AI Gap - Close the AI Maturity Gap

McKinsey (2025) just released their AI report. The AI gap is bigger than they thought. Almost all organisations invest in AI, but just 1% believe they are at maturity. This report explores companies’ technology and business readiness for AI adoption. It concludes that employees are ready for AI. The biggest barrier to success is leadership. Yet 92% plan to increase AI investments. The challenge of AI in the workplace is not a technology challenge. It is a business challenge that calls upon leaders to align teams, address AI headwinds, and rewire their companies for change and value.

Here's what's actually happening:

• 47% expect AI to change 30% of their work this year.
• 71% trust their employers to deploy AI ethically.
• 13% use AI for most of their daily tasks.

But there's a bigger story. The organisations that figure this out first will significantly benefit.

Here's what the data shows:

1. Revenue Growth→ 87% expect AI revenue boost in 3 years→ 51% predict >5% growth from AI→ Only 19% seeing that impact today
2. Adoption Gap→ 48% say training is critical→ Half get minimal AI support→ Millennials 1.4x more likely to use AI
3. The Generation Gap→ 62% of millennials (35-44) report high AI expertise→ Only 50% of Gen Z show the same confidence→ Just 22% of boomers are AI-ready

Further findings from the McKinsey (2025) AI Report provided in Figure 4 below.

Data Quality outstrips Quantity

High-quality data is outpacing large datasets to improve data and AI performance, ensuring accuracy, reliability, better outcomes and V A L U E. It's true, 'quality over quantity'. Most of us would have already heard this saying before that applies to many things in life, let alone in the business world. Quality over quantity is a phrase referring to the preferred focus on the condition of something as opposed to how much you have of something. This also holds true and applies to getting value from data and AI.

This is becoming increasingly true as GenAI models are adapted for use in the enterprise and organisation. While frontier models have been trained on massive quantities of data scraped from the internet and other public sources, their utility for specific business purposes is limited.

The ability of these LLMs to extract meaning from data needs to be combined with proprietary data unique to an organisation for real benefits to be realized. Making sure data is ready for this is a key step once business objectives have been set. Gartner (2019) estimates that preparing data for AI improves business outcomes by 20%, which means data must be appropriate for the use cases intended, whether structured or unstructured. A key reason why 30% of internal AI projects are abandoned, according to Gartner (2024), is poor data quality inputs. This involves removing corrupt data and duplicates, and filling gaps where inputs are incomplete.?

And while data quality is key, there also needs to be sufficient quantity. Depending on the objectives and how the LLM is tuned, this means thousands of records at a minimum and possibly significantly more.

Fuel Innovation with Data and AI

Make more informed business decisions, improve operations, and differentiate with predictive and generative AI.

For the past few years, too many executives have focused on AI headlines rather than the data they have been generating. As a result, the data has become siloed, untracked, unorganised and unable to contribute to their AI vision. Without good data, any AI project will fail. This is a solvable challenge, but it requires enterprises and organisations to refocus on the health of their data estate and learn from companies whose quick AI wins have delivered real impact.

Linking data and AI initiatives to business value is a critical challenge for many organisations, but it’s also a great opportunity to realise the full potential of data and AI.

Assistive AI vs Agentic AI: What's the difference?

In the rapidly evolving landscape of AI, two distinct paradigms have emerged: Assistive AI and Agentic AI. While both leverage the power of generative AI, they serve fundamentally different roles in how they interact with users and perform tasks.

Assistive AI is designed to help users complete tasks. It acts as a co-pilot, providing necessary information and suggestions to help users make informed decisions. For instance, in a customer contact center, an assistive AI might provide real-time information and recommended responses to help a human agent answer customer queries more efficiently. The human agent remains in control, using AI's assistance to enhance its performance.

On the other hand, Agentic AI is built to perform tasks on behalf of the user. It takes on a more autonomous role, executing tasks with minimal human intervention. An example of this would be a customer service agent AI that handles Q&A tasks entirely, freeing human agents to focus on more complex issues. In this scenario, the AI not only assists but also acts as the primary executor of the task.

As AI continues to evolve, the move towards more autonomous, agentic AI will undoubtedly shape the future of work, driving greater adoption and unlocking new possibilities for enterprises and organisations worldwide. It is important for organisations to understand the distinction between assistive AI and agentic AI, which highlights the diverse applications of generative AI in today's business landscape. Refer to Figure 5 below for further information on the differences between Assistive AI vs Agentic AI.

How Generative AI is Being Used for Assistive Tasks

Generative AI has already made significant strides in assistive applications. Internal Q&A chatbots are a prime example, where AI systems are deployed within organisations to provide instant answers to employee queries, thereby improving productivity and efficiency. These chatbots leverage natural language processing (NLP)? to understand and respond to questions, acting as a valuable resource for employees seeking information.

Other notable examples are ChatGPT and Microsoft Copilot, an AI-powered tool integrated into Microsoft Office applications. Copilot assists users by generating text, creating summaries and providing data insights, all within the familiar interface of Office applications. This assistive AI helps users complete tasks more quickly and accurately, enhancing their overall productivity. Refer to Figure 6 below illustrating how Generative AI is being used for assistive tasks.

How Generative AI is Evolving into Agentic AI

The evolution of generative AI (genAI) into more agentic roles is marked by the development of frameworks like AutoGen and CrewAI. These frameworks enable the creation of AI teams, each with specialized roles, to tackle complex tasks and automate workflows. For instance, a team of AI agents could be assembled to manage a marketing campaign, with each agent handling different aspects such as content creation, data analysis and customer engagement.

AutoGen and CrewAI allow users to define the roles and responsibilities of each AI agent, ensuring that the output is tailored to specific needs. Human input is still crucial in setting parameters and approving final outputs, but the bulk of the work is carried out by the AI agents. This shift towards agentic AI represents a significant step forward in automating complex tasks and enhancing operational efficiency. Refer to Figure 7 below which provides an example of the work of Agentic AI.

Understanding Agentic AI and its Implications

Gartner (2025) identifies agentic AI as one of the most transformative forces set to reshape enterprise technology landscapes over the next 24 months. Agentic AI is set to transform business decision-making, necessitating risk management in data quality, governance and employee integration. The emergence of agentic AI represents a significant departure from conventional AI implementations that have dominated enterprise software over the past decade. While previous generations of AI systems primarily focused on pattern recognition and predictive analytics, agentic AI introduces a new paradigm where AI systems can independently initiate actions, make decisions and execute complex workflows with minimal human intervention.

This shift marks a crucial evolution in how businesses leverage AI, moving from tools that simply support human decision-making to systems that can actively participate in organisational processes. According to Gartner (2025), agentic AI is characterised by its ability to act on behalf of an organisation, making decisions based on data analysis and predefined goals.

"Agentic AI will eliminate the need to interact with websites and applications. Why bother when your AI agent can do it for you?”

- Gartner (2025)

Unlike traditional AI systems that require explicit instructions from users, agentic AI operates independently, enabling it to quickly analyse complex datasets, identify patterns and take action. This capability is expected to significantly enhance decision-making processes across industries.

Gartner (2025) highlights some of the opportunities agentic AI brings to organisations:

• Agentic AI enhances decision-making by autonomously selecting actions for desired outcomes, improving performance over time.
• It quickly analyses complex data, reducing manual modelling and enabling scalable solutions.
• This technology upskills teams to manage projects through natural language, though it requires robust governance and orchestration tools.
• Effective implementation necessitates clear guidelines on autonomy, security and data privacy.

Gartner (2025) predicts that by 2028, 33% of enterprise software applications will incorporate agentic AI, a substantial increase from less than 1% in 2024.

The timing of this report is particularly significant as organisations seek to understand how to harness the potential of more autonomous AI systems to stay competitive whilst managing associated risks and governance challenges.

The Gartner (2025) report's findings are especially relevant for CDAIOs, CTOs, technology leaders and organisations given the rapid advancement of LLMs and autonomous systems over the past 18 months, which have created new possibilities for AI agencies in enterprise environments. Agentic AI is poised to revolutionise enterprise software applications and AI organisations must carefully navigate and examine both the transformative potential and inherent challenges as they adopt these advanced technologies.

AI Agents vs Agentic AI

Here’s where things get interesting. Even though AI Agents and Agentic AI are both powered by artificial intelligence, they operate in very different ways. It is important for CEOs, Boards, executives and their organisations to understand that there is difference between AI Agents and Agentic AI and even more importantly, understand why this distinction matters.

AI Agents

AI Agents are typically built to do specific tasks. They’re designed to help you with something — like answering questions, organiaing your calendar, or even managing your email inbox. AI Agents are great at automating simple, repetitive tasks but don’t have the autonomy or decision-making abilities that Agentic AI does. Think of them as virtual helpers that do exactly what you tell them to do, without thinking for themselves.

AI Agents core elements, functions and capabilities:

• Input handling
• Process execution
• Output generation
• Seqential workflow
• Task-specific operation

Agentic AI

At its core, Agentic AI is a type of AI that’s all about autonomy. This means that it can make decisions, take actions and even learn on its own to achieve specific goals. It’s kind of like having a virtual assistant that can think, reason and adapt to changing circumstances without needing constant direction.

This makes Agentic AI highly autonomous and able to handle complex tasks that require reasoning, problem-solving and adapting to new situations.

Agentic AI operates in four key stages:

1. Perception: It gathers data from the world around it.
2. Reasoning: It processes this data to understand what’s going on.
3. Action: It decides what to do based on its understanding.
4. Learning: It improves and adapts over time, learning from feedback and experience.

Agentic AI core elements, functions and capabilities:

• Network Connectivity
• Memory and Analysis Integration
• Coordinated Decision-Making
• Optimisation Processes
• Swarm Execution

Take problem-solving as an example:

AI Agent

• Input → Process → Output
• Executes defined tasks within set parameters.

Agentic AI

• Network analysis → Memory Storage → Coordinated Decisions → Parallel Optimisation → Swarm Execution
• Creates emergent solutions through collective intelligence.

The key distinction:

• Agents operate in isolation.
• Agentic AI operates in networks.

The real impact and implications:

Agents excel at: single-path processing, direct execution and output generation.

Agentic AI masters: network analysis, coordinated decisions and swarm-based solutions.

The choice between them is not about capability - it's about individual vs. collective intelligence approaches.

Both AI Agents and Agentic AI are changing the world in different ways. While AI Agents are great for automating repetitive tasks and handling specific actions, Agentic AI is pushing the boundaries of what AI can do by making decisions, learning from experiences, and solving complex problems. Both are valuable tools that are profoundly changing the world and shaping the future of business, industry, technology, society, humanity and our lifestyles — the way we live, work, rest and play.

Refer to Figures 8 and 9 below which illustrates and details the differences between AI Agents and Agentic AI.

AI Agent & Agentic AI: The Benefits

• Revolutionizing Industries: Both Agentic AI and AI Agents are transforming industries. Whether it’s making self-driving cars a reality or automating customer service, AI is making things more efficient and cost-effective.
• Better Decision-Making: Agentic AI has the potential to process huge amounts of data, recognize patterns, and make decisions that are often more accurate than humans can.
• Personalization: In industries like finance and retail, AI can provide highly personalized services — (in finance) adjusting financial advice or investment strategies — (in retail) consumer behaviours and preferences — based on real-time data and predictions.

AI Agent & Agentic AI: The Risks and Challenges

As AI continues to develop, the line between AI Agents and Agentic AI might blur even further. The potential for these technologies to complement each other is huge — imagine an AI Agent that can learn and adapt like Agentic AI, offering even more power to automate tasks and make decisions. There are however associated risks, challenges and tradeoffs that CEOs, Boards and organisations must carefully consider:

• Job Displacement: As AI takes over more tasks, there are likely to be increasingly more job losses across all industries, for example, in sectors like customer service, driving and even healthcare. But there’s also the potential for AI to create new jobs and opportunities.
• Ethics and Accountability: As AI systems become more autonomous, questions about accountability arise. If an Agentic AI makes a mistake, who’s responsible? And how transparent should these systems be?
• Data Privacy: With more AI systems handling sensitive data, privacy concerns are growing. How will companies protect user data, and what safeguards are in place?

Where Do We See These in the Real World?

Both Agentic AI and AI Agents have started popping up in various industries, and their applications are growing fast.

AI Agents in Action

1. Customer Support: One of the most common uses of AI Agents is in customer service. Chatbots can answer questions, resolve issues, and guide customers through processes — all without needing human intervention. Zendesk’s AI-powered chatbot helps businesses respond to customer queries quickly and efficiently, acting as an AI Agent that handles common issues and frees up human agents for more complex tasks.
2. Personal Assistants: You probably already interact with an AI Agent every day if you use voice assistants like Siri or Google Assistant. They can help you set reminders, check the weather, or play your favorite music — tasks that are useful but don’t require much decision-making. These AI Agents rely on predefined commands and are great at handling simple, repetitive tasks.
3. Email Management: AI Agents are also great for managing your inbox. They can sort emails, flag important ones, and even provide smart replies to save you time. Google’s Gmail Smart Compose feature is an excellent example of an AI Agent at work, helping users respond to emails faster by suggesting phrases based on context.
4. Productivity Tools: Tools like GitHub Copilot are AI Agents that help software developers by suggesting code and helping with debugging. They’re like having a second set of eyes that’s always there to help. By offering code suggestions in real-time, this AI Agent enhances developer productivity, allowing them to focus on more creative aspects of their work.

See also Medium (2025) top list of AI Agents set to dominate in 2025.

Agentic AI in Action

1. Self-Driving Cars: One of the most exciting uses of Agentic AI is in autonomous vehicles. These AI systems perceive their surroundings, make driving decisions, and learn from every trip. Over time, they get better at navigating and handling new challenges on the road. For example, Tesla’s Full Self-Driving system is an example of Agentic AI that continuously learns from the driving environment and adjusts its behaviour to improve safety and efficiency.
2. Supply Chain Management: Agentic AI is also helping companies optimise their supply chains. By autonomously managing inventory, predicting demand, and adjusting delivery routes in real-time, AI can ensure smoother, more efficient operations. Amazon’s Warehouse Robots, powered by AI, are an example — these robots navigate complex environments, adapt to different conditions, and autonomously move goods around warehouses.
3. Cybersecurity: In the world of cybersecurity, Agentic AI can detect threats and vulnerabilities by analysing network activity and automatically responding to potential breaches. Darktrace, an AI cybersecurity company, uses Agentic AI to autonomously detect, respond to, and learn from potential cyber threats in real-time.
4. Healthcare: AI is playing a big role in healthcare, too. Agentic AI can assist with diagnostics, treatment recommendations and patient care management. It analyses medical data, identifies patterns and helps doctors make more informed decisions. For instance, IBM’s Watson Health uses AI to analyse massive amounts of healthcare data, learning from new information to offer insights that help doctors and healthcare professionals.

Machines + Humans working together

Do not be fooled, whilst data and technology are foundational for AI enablement, extracting value from AI and AI success is more about the 'human(s) in the equation' . Think about it, every enterprise and organisation on the planet has access to data, data and AI technologies. If you look closer and 'under the hood' of AI Leaders, when you really look through a microscope at what makes their organisation an AI Leader, if you have done your homework, you will start realising that the significant difference, impact and value drivers are the 'humans in the equation'. You will soon see again and again, that the value-driven data and AI-savvy founders, leaders, leadership, ambitions, mindset and grand strategy, along with talented individuals and teams, organisation culture, ways of thinking and ways of working are key drivers and foundational to their success — it is the HUMANS in the equation.

Extracting value from data and AI — it's about MACHINES + HUMANS — or HUMANS + MACHINES working together — MACHINES + HUMANS symbiosis — to harness value from data and AI — MACHINES (generation) + HUMANS (validation)?.

Key to extracting value from data and AI and tap into its fuller potential is the ability to strike that perfect balance between HUMAN knowledge and MACHINE capabilities.

Lasting Thoughts...

• Enterprises and organisations can't scale AI without improving their data literacy, data and technology foundations and ways of working, the way work gets done.
• The truth is, without good data, AI can't help much.
• The need for most organisations to progress beyond incremental moves and embrace comprehensive data and AI strategies to U N L O C K transformative value.
• High-quality data is outpacing large datasets to improve data and AI performance, ensuring accuracy, reliability, better outcomes and V A L U E.
• Only 20% of analytics insights will deliver business outcomes, according to Gartner (2019) research.
• Agentic AI is one of the most transformative forces set to reshape enterprise technology landscapes over the next 24 months and beyond.
• While predictive AI informs decisions and generative AI creates content, agentic AI can independently carry out complex, multi-step tasks that previously required significant human involvement.
• Both AI Agents and Agentic AI are valuable AI tools that are profoundly changing the world and shaping the future of business, industry, technology, society, humanity and our lifestyles — the way we live, work, rest and play. As AI continues to develop, the line between AI Agents and Agentic AI might blur even further as we head towards artificial super intelligence (or ASI) over the coming decade(s).
• Think through your overall organisational Data and AI strategy in more depth. What is the economic, customer and employee value of AI? Lift sights from specific AI use cases | POCs and think about whole business domains. Take a single part of the organisation and reimagine how it might work in 3 to 4 years if it is fully data and AI-driven (predictive and generative AI). Do not just fixate on GenAI, avoid trap of treating genAI like a 'hammer looking for a nail'.
• Artificial intelligence has its own momentum, create a plan to keep up that momentum within the organisation, so your data and AI efforts never lag. This is a long-term strategy that ensures maximum benefit and value from AI efforts — 'flywheel' approach to keep the momentum going.
• Extracting value from data and AI — it's about MACHINES + HUMANS — or HUMANS + MACHINES working together — to harness value from data and AI — MACHINES (generation) + HUMANS (validation)?.
• The single most critical driver of [impacts, risks and] value from data and AI is not algorithms, or technology — it is the human(s) in the equation.
• Key to extracting value from data and AI and tap into its fuller potential is the ability to strike that perfect balance between HUMAN knowledge and MACHINE capabilities.

Action for you

Appreciate a “like, love or comment” if you are reading this and/or learn something from this article.

What are your thoughts on extracting more value on data and AI?

Interested or need Guidance?

If you are interested, need any help or guidance with your organisation's Data and AI Leadership, Strategy, Roadmap and Value, if you are seeking a Data and AI Leader, Strategist or Consultant, or a Value Maker, or if you need help with trustworthy data and AI, data and AI Governance, data and AI architecture or should you require a 'value-driven', 'data & AI savvy' Enterprise Strategy and Architecture Leader, Data and AI Architect, Enterprise Architect, or Business Architect, given my profile, credentials and wealth of experience, I can help you and your organisation extract value from Data and AI and creating your future-built enterprise and organisation — reach out to me.

Final Words

"The single most critical driver of [impacts, risks and] value from AI is not algorithms, or technology — it is the human in the equation".

- Shervin Khodabandeh, Senior Partner and Managing Director, BCG (2020)

"More data beats better algorithms, but better data beats more data".

- Peter Norvig - AI guru and former Director of Research, Google (2024)

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Wharton Online (2023) What is the Network Effect? https://online.wharton.upenn.edu/blog/what-is-the-network-effect/#:~:text=The%20network%20effect%20is%20a,back%20to%20the%20internet%20itself

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? 2025 Litsa Roberts