AI News, How Financial Services Firms Can Leverage AI with High ... artificial intelligence
Competing in the Age of AI
In 2019, just five years after the Ant Financial Services Group was launched, the number of consumers using its services passed the one billion mark.
Spun out of Alibaba, Ant Financial uses artificial intelligence and data from Alipay—its core mobile-payments platform—to run an extraordinary variety of businesses, including consumer lending, money market funds, wealth management, health insurance, credit-rating services, and even an online game that encourages people to reduce their carbon footprint.
Every time we use a service from one of those companies, the same remarkable thing happens: Rather than relying on traditional business processes operated by workers, managers, process engineers, supervisors, or customer service representatives, the value we get is served up by algorithms.
True, managers and engineers design the AI and the software that makes the algorithms work, but after that, the system delivers value on its own, through digital automation or by leveraging an ecosystem of providers outside the firm.
Whether you’re leading a digital start-up or working to revamp a traditional enterprise, it’s essential to understand the revolutionary impact AI has on operations, strategy, and competition.
To bring about dramatic change, AI doesn’t need to be the stuff of science fiction—indistinguishable from human behavior or simulating human reasoning, a capability sometimes referred to as “strong AI.” You need only a computer system to be able to perform tasks traditionally handled by people—what is often referred to as “weak AI.” With weak AI, the AI factory can already take on a range of critical decisions.
As soon as someone starts to type a few letters into the search box, algorithms dynamically predict the full search term on the basis of terms that many users have typed in before and this particular user’s past actions.
AI also generates the organic search results, which are drawn from a previously assembled index of the web and optimized according to the clicks generated on the results of previous searches.
The great Alfred Chandler described how modern industrial firms could reach unprecedented levels of production at much lower unit cost, giving large firms an important edge over smaller rivals.
And for a long time they’ve been enabled by carefully defined business processes that rely on labor and management to deliver products and services to customers—and that are reinforced by traditional IT systems.
AI-driven processes can be scaled up much more rapidly than traditional processes can, allow for much greater scope because they can easily be connected with other digitized businesses, and create incredibly powerful opportunities for learning and improvement—like the ability to produce ever more accurate and sophisticated customer-behavior models and then tailor services accordingly.
We call this kind of confrontation a “collision.” As both learning and network effects amplify volume’s impact on value creation, firms built on a digital core can overwhelm traditional organizations.
Network effects produce little value before they reach critical mass, and most newly applied algorithms suffer from a “cold start” before acquiring adequate data.
For leaders of traditional firms, competing with digital rivals involves more than deploying enterprise software or even building data pipelines, understanding algorithms, and experimenting.
For a very, very long time, companies have optimized their scale, scope, and learning through greater focus and specialization, which led to the siloed structures that the vast majority of enterprises today have.
When each silo in a firm has its own data and code, internal development is fragmented, and it’s nearly impossible to build connections across the silos or with external business networks or ecosystems.
In fact, in a recent study we looked at more than 350 traditional enterprises in both service and manufacturing sectors and found that the majority had started building a greater focus on data and analytics into their organizations.
You don’t have to be a software start-up to digitize critical elements of your business—but you do have to confront silos and fragmented legacy systems, add capabilities, and retool your culture.
(For a closer look at the key principles that should drive such transformations, see the sidebar “Putting AI at the Firm’s Core.”) Fidelity Investments is using AI to enable processes in important areas, including customer service, customer insights, and investment recommendations.
You can see this dynamic in companies such as Google, Facebook, Tencent, and Alibaba, which have become powerful “hub” firms by accumulating data through their many network connections and building the algorithms necessary to heighten competitive advantages across disparate industries.
But if auto executives think of cars beyond their traditional industry context, as a highly connected, AI-enabled service, they can not only defend themselves but also unleash new value—through local commerce opportunities, ads, news and entertainment feeds, location-based services, and so on.
Instead of focusing on industry analysis and on the management of companies’ internal resources, strategy needs to focus on the connections firms create across industries and the flow of data through the networks the firms use.
Strategies are shifting away from traditional differentiation based on cost, quality, and brand equity and specialized, vertical expertise and toward advantages like business network position, the accumulation of unique data, and the deployment of sophisticated analytics.
A digital signal—a viral meme, for instance—can spread rapidly through networks and can be just about impossible to halt, even for the organization that launched it in the first place or an entity that controls the key hubs in a network.
Without friction, a video inciting violence or a phony or manipulative headline can quickly spread to billions of people on a variety of networks, even morphing to optimize click-throughs and downloads.
Digital scale, scope, and learning create a slew of new challenges—not just privacy and cybersecurity problems, but social turbulence resulting from market concentration, dislocations, and increased inequality.
Financial Services: Why Is Data Stuck in the Dark Ages?
Here are some of the key technologies that are making insights transparent and accessible across lines of business: Cloud computing.
And having systems on a single platform eliminates the reconciliation issues that occur when data and systems are on separate platforms. Financial services firms are starting to take the leap into the cloud.
For example, open APIs enable an investing app to connect to a user’s bank account without compromising the security of the account holder’s financial institution. Workday Cloud Platform leverages open APIs to help companies extend their Workday data ecosystem and drive additional value for the business with third-party applications.
Sayan Chakraborty, executive vice president of technology at Workday, describes more ways ML has changed the way we work, including how it speeds up business processes. For example, finance typically spends extended amounts of time reconciling the monthly close.
AI Consulting: In-depth Guide with Top AI Consultants of 2020
Different consulting companies have different estimates but companies like McKinsey, PwC all rightly claim that AI is a multi trillion dollar economic opportunity for the world which will be unlocked in the next decade.
For example, a translation services company needs to make dramatic changes to its business to ensure that it survives in 5-10 years when Google translate reaches human level translation capabilities.
It is helpful to have strategy sessions envisioning 5-10 years into the future, helping executives understand the future of AI so they can identify how their business needs to start changing today.
due diligence require a consulting team to prepare the inputs to a valuation in a short amount of time, typically in 1 month.
These include understanding and evaluating data sources relevant for AI models, formulating fast approaches to benchmarking different AI vendors’
Implementation should also be considered as multiple activities such as planning, vendor selection if needed, project management, development, improvement of business processes impacted by the project, change management and so on.
However, for example, if the client lacks the tech know-how to implement urgent initiatives, starting with consultants can help the client progress faster.
However, please bear in mind that, in the long run relying on consultants completely for implementation will likely be more expensive than completing those activities in-house.
Traditional consulting firms such as MBB (McKinsey, BCG, Bain) have been active in strategy side of things for a long time, but as the greater dependence on data continues, companies’
So this raises the question of whether consulting will be able to survive without artificial intelligence implementation or not. Those two industries will likely to become more interlinked because of the advantages of a possible partnership.
They are focusing on AI applications, educating executives on AI and identifying limits of AI: Though launched as accounting companies in early 1900s, Big 4 (Deloitte, PwC, EY and KPMG) have been running consulting businesses for >50 years.
Big 4 accounting firms are some of the largest employers in the world and they have deep enough pockets to make large investments in this space and they have the business development know-how and resources to make partnerships across the AI ecosystem to deliver end-to-end services.
Their teams have gained substantial expertise in handling massive amounts of data through their specific cloud architecture, and for a data-intensive activity like artificial intelligence, they can provide the right advisory for their clients.
These companies have productized their solutions to some degree however, they still engage with their enterprise clients in labor intensive engagement which involves customizing and integrating their product into clients’
This is not a comprehensive list yet, please feel free to add other firms to comments GoodAI Consulting is an AI-focused consulting firm using artificial intelligence solutions to maximize business success for companies and organizations across a range of industries.
The company analyzes how AI can help a client’s business, do the necessary research legwork on their behalf, and implement practical solutions.
Even though artificial intelligence provides huge scalability advantages, integration into a differently sized organization requires a different type of focus points.
Therefore, some artificial intelligence consulting firms choose to focus on small and medium enterprises and provide custom tailored solutions WildFire is an example of these consulting firms.
It is common for tech companies to add complimentary services on top of product sales or to provide free PoCs to gather new customers.
However, most AI consulting projects, like most consulting projects, are priced based on the time and materials necessary for the project as estimated by the consultancy.
AI in the Accounting Big Four – Comparing Deloitte, PwC, KPMG, and EY
As with most large consulting firms, Deloitte vies for market share and press through thought leadership – and like the other Big Four – it has increasingly focused it’s white papers and research on the topic of artificial intelligence.
The company claims that this technology has helped reduce the time spent on reviewing legal contract documents, invoices, financial statements and board meeting minutes by up to 50 percent or more.
Following are some business use-cases as a result of this partnership: Deloitte Catalyst – another recent AI initiative from the global consulting giant – is a network of startups, that are working together to translate AI technologies into practical business solutions for client firms.
Most AI firms are still figuring out exactly how to drive business value with their technology, and Deloitte hopes to forge partnerships with these cutting-edge firms to help bring the latest AI innovations to their clients.
“Technology like AI can help make these processes more efficient, accurate and comprehensive, helping us get better insights to clients sooner, and empowering our professionals to deliver high-value services to our clients”.
They claim that this reduces the administrative time spent on reviewing audit documents and gives employees more time to participate in the judgment and analytical part of the process.
According to EY Global Assurance Innovation Leader Jeanne Boillet in a June 2017 report: “These pilots show that AI tools would make it possible to review about 70%-80% of a simple lease’s contents electronically, leaving the remainder to be considered by a human.
With more complex leases (in real estate, for instance), that figure would be more like 40%, but as the tools improve, and the machines learn, it is likely that more complex contracts and data can be read, managed and analyzed.” Another area where EY has applied AI technology is the automation of routine tasks, such as auditing (which we covered in greater depth in our full article on AI in insurance), by using it’s own proprietary Robotic Process Automation (RPA) system.
For example, this drone can count the number of vehicles in a production plant under audit, and communicate this data directly into the EY Canvas – the global audit digital platform.
We find the word “never” to be downright ridiculous, but we cannot blame EY leadership from having to tout this opinion, and there is no reason to blame Mrs. Boillet for being so black-and-white about the matter, anyone in her shoes would say the same thing.
Many would argue that businesses of this kind (chock full of knowledge workers working in spreadsheets) are among the biggest “low-hanging fruit” for automation, and it behooves older people-heavy businesses to dispel disturbing thoughts about massive layoffs.
The impact on employee morale and shareholder value would likely be tragic if EY’s leadership (or the leadership of Accenture, Deloitte, or any other large consulting firm) were to state openly that many of their current jobs may be fully automated, and that some of these jobs would not be replaced.
We should expect to see large, people-heavy legacy companies (For more insight into the kinds of jobs that might be first – or last – to automate, read or listen to our previous interviews with Martin Ford, Kevin LaGrandeur, and Marshall Brain – all of which are focused on the impact of automation on the job market.) The second largest professional services firm by revenue, PwC, claims to have begun adopting AI as well.
we have helped large auto manufacturers visualize and navigate over 200,000 go-to-market scenarios to fine-tune their strategy and launch a new multi-billion dollar business in ridesharing and autonomous/electric vehicles.
A recent PwC analysis of the financial services sector identifies a number of automation and augmentation concerns related to AI – and advice from PwC on how firms might adapt to AI in the future.
The image below is only a small snapshot, because of the complexity of the topic (and the inherent challenges in predicting the future), we advise the reader to assess multiple sources and develop their own conclusions.
Key features of KPMG Ignite include AI tools (a few examples of which are listed below), AI integrators to make these tools compatible with existing IT infrastructure, guidance for client firms and testing, prototype development, and innovation on emerging AI applications.
Like Deloitte, they’ve created a number of layperson-friendly AI explainer videos to explain AI’s potential impact on their work: KPMG’s “ethical compass for automation” highlights some of the societal concerns that they consider to be important in the years ahead.
In the world of startups, companies increasingly see their competitors use “artificial intelligence” in pitch decks and on their “about” page – and the pressure mounts to do the same (even if they have no AI skills or talent, and even if the product is in no way predicated on artificial intelligence).
For some reason I find that business readers often know intuitively that a startup’s pitch deck is self-serving, but they’re often unable to apply that same “taking it with a grain of salt” mentality to white papers and reports, which – like pitch decks – serve the goal of filling a company’s bank accounts, not of portraying truth for its own sake.
These companies have massive revenues and thousands of talented people – and based on our assessment, all four of the Big Four claim to have integrated AI into some of their service offerings, and we’d suspect that this is, in fact, the case.
Indeed part of our job here at TechEmergence is to do the “digging” ourselves and aim to get behind the bluster of press releases, but we’d like to remind our readers to think like an analyst as they consume information that might affect their strategy and procurement.
Why Artificial Intelligence is Important for Businesses in 2020
As business moves into a new decade, the 2020’s, enterprises continue to look for the leg up that will push them above the competition.
survey for the O’Reilly ‘AI Adoption in the Enterprise’ report found that just under 75 percent of respondents said their business was either evaluating ‘AI’ or not yet using ‘AI’, leaving only a quarter of industries such as financial services, healthcare, telecommunications, and electronics and technology with fully-fledged and operational artificial intelligence systems.
AI generally refers to the equipping of machines with the ability not just to execute commands or follow a defined path from the programmer, but to absorb data in large quantities, analyze it, and then make determinations on actions based on that data.
Because the machine learning system relies so heavily on a strong and constant influx of data, ensuring you have or can create a massive data pool from which to draw is crucial when starting out.
You must be sure you know what kind of data you’ll be collecting and what kind of data will be most useful in optimizing your computer’s learning trail, which draws back to having a clear use case in mind before beginning.
The extent to which artificial intelligence relies on data is such that even an enterprise with an exceptionally high functioning data science team will need to take another look at how their information is structured, and how their data is tagged before using it to train their AI investment.
If a company wishes to institute artificial intelligence into its business, there are currently three major options through which to do so: building customized AI solutions and creating tailored algorithms to your enterprise, buying solutions through specialized companies, or using public solutions through cloud APIs.
Ensuring buy-in from upper management, even in the early days when the system is in development and not yet profitable, and meaningfully conveying the future positive growth that artificial intelligence is equipped to bring is crucial when embarking on the AI journey.
Show an utterly thought-out plan alongside AI processes with clear and useful objectives to find relatively short-term results, a map for how to build the AI platform out in the future, and support from all stakeholders within the organization.
- On 13. april 2021
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