The impact of AI is unmistakable in every swipe on our smartphones and every click on our laptops, subtly shaping our digital interactions. It recommends articles, directs investments and refines our travel routes. This extends beyond mere personal benefits — AI is poised to significantly drive the global economy forward, with GenAI potentially boosting it by an impressive $13 trillion. This compelling economic benefit is a clear call to businesses: evolve with AI or risk falling behind.

Already, more than half of global companies are heeding this call, ready to amplify their investments to harness AI’s full economic potential.

Obstacles Mark the Path to AI-First Status

However, being an AI-first company takes a lot more than just updating software or getting the latest solutions; it takes a complete transformation of how businesses function, where we put the right focus, how we use technology, and the scale at which we innovate and operate.

Enterprises eager to integrate AI into their operations might first meet several critical speed bumps.

Endless Disruption:

AI is constantly evolving, accelerating transformations across sectors. Take banking, for instance. Its future is veering towards being a deeply personalized, intelligent, and omnichannel experience.

Imagine this: Sam, a working professional, seeks health insurance through her bank. Instead of the usual rate assessment, she receives a 2% discount on her premium, a perk dynamically calculated from her gym attendance and sleep quality. All this is streamlined by GenAI, slashing operational costs while delivering a hyper-personalized omnichannel experience.

Banks that harness AI and rapidly constantly evolve with it will be on track for exceptional growth.

On the other hand, those unable to keep pace stand to lose out significantly. The message is clear: AI is consistently transforming industries. Matching AI’s pace ensures robust growth and augmented shareholder value. Failing to do so, however, risks stagnation. The performance gap AI widens is undeniable.

SuboptimalTech Investments: 

When it comes to tech investments, it’s easy to get caught up in the latest and supposedly greatest, but that’s leading many companies down a costly path. Take this bank in North America as an example: they built a web of over 1,000 systems that are costing them $2 billion in ‘technical debt’ – the price we pay later for quick fixes made in a rush. This technical debt represents not just a financial strain but a significant obstacle to achieving a complete digital transformation.

A study indicates HR professionals navigate between three to six applications to complete a single task, a clear symptom of tool redundancy. Not only is the quantity of tools overwhelming, but the quality also leaves much to be desired, with reports of user-unfriendly interfaces and inconsistent performance.

The underlying issue is a misalignment of technology and business strategies. When we rush into new tech investments without a game plan that’s aligned with our larger business goals, we end up with a mishmash of old and new systems. It’s like trying to fit pieces from different puzzles together—impossible and expensive.

To avoid this, we need to think of digital transformation not as buying the latest tech but as a strategic rethinking that involves the whole company. It’s about choosing tools that integrate smoothly, keep our data in one piece, and make sure we are agile and can adapt as needed. That’s the kind of strategic thinking that will put us ahead and keep us there.

Pilots that don’t scale:

Pilot projects are popping up in boardrooms, each promising to be the next big thing in business transformation. But numbers show that despite the initial enthusiasm, a significant 70% fail to scale across the enterprise. Financial restrictions, operational complexities, and resistance to change clip the wings of widespread digital adoption.

These challenges mean that while certain areas of a business might enjoy the benefits of digital innovation, the impact doesn’t extend across the entire operation to user experiences.

This isn’t just about missing out on a few opportunities for improvement. It’s about the bigger picture: staying competitive. As digital initiatives fail in scaling up, businesses leave gaps for more tech-savvy disruptors to leap ahead. In short, we’re witnessing the paradox of progress: abundant technological advancements, yet a struggle to harness them for transformative change.

The great wall of Silos:

Take Sam’s case, where her dedication to fitness nets her a customized discount on health insurance, showcasing the power of AI to forge partnerships across banking, insurance, and health tech. This is the benchmark for an AI-first company: hyper-personalization at its finest.

However, what’s striking is that while this kind of inter-industry connectedness is the goal, many companies are struggling to achieve this seamless integration within their own walls. Silos are stifling the flow of information between people, processes, data, and tech, costing up to 30% of potential revenues and hindering the kind of integration essential for AI to thrive.

If Sam’s health data doesn’t speak to her financial profile, the opportunity for customized service vanishes. In this disconnect, the aspiration to be AI-first becomes unattainable. Breaking down these silos is an operational adjustment and a strategic imperative to unlock AI’s transformative promise.

These obstacles teach us an important lesson. Companies must ensure that their technology investments are not just new but right — right for their people, processes, and long-term strategic vision. Only then can they unlock the full potential of digital transformation and avoid the pitfalls that have ensnared many in a costly cycle of technical debt, unscalable pilots, and an overall disconnect in the enterprise.

Breaking Ground for a Customer-Focused, Automated, Data-Driven Future

Imagine a business landscape that not only adapts to market fluctuations but also forecasts them with pinpoint accuracy. This is the hallmark of AI-first enterprises.

Consider a leading supply chain organization that seamlessly predicts disruptions, adjusts its routes, and changes its course in real-time with the help of connected data systems, analytics, and AI.

How is this foresight achieved?

Their strategy is anchored in a customer-first or business-first philosophy. For them, transformation transcends digital—it’s about refining the entire user journey, enhancing it with technology, not being led by it. They place a premium on creating value through ecosystem partnerships and prioritizing human connections, tapping into contextual intelligence to guide their transformation efforts.

These organizations are steering towards a “hands off the wheel” future, where human ingenuity is the architect and sophisticated systems are the operatives.

Rather than focusing on cost reduction alone, they harness technology to amplify human capabilities. They aim for a high level of straight-through processing across essential customer journeys and business processes. By taking this approach, they don’t merely retrofit new technology into old systems. They design their processes with digital DNA from the outset.

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This strategic overhaul extends across all business divisions. In our supply chain scenario, it’s the convergence of logistics, inventory management, and customer service into a unified digital framework. Every department enriches the AI system, making decision-making processes not just quicker but also more intelligent and crafting a customer experience that is proactively engaging.

Does this mean an overhaul at the expense of existing investments?

Not at all. These businesses adopt a measured approach to reshaping their operating model. They strategically scale AI throughout their organization, taking a platform approach that connects all business operations, making digital engagement intrinsic to their identity.

[To share your insights with us, please write to sghosh@martechseries.com]

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In the pulsating heart of the digital age, businesses across the globe are relentlessly pursuing innovative solutions to streamline their operations. Amidst this pursuit, Intelligent Document Processing (IDP) has emerged from the shadows, rapidly becoming a high priority for organizations striving to digitize their workflows. According to research, 68% of shared services practitioners view IDP as a high priority for their process optimization efforts, proving that the solution is necessary for many organizations looking to digitize. By automating document processing, IDP offers many benefits, from increased accuracy and speed of data processing to reduced errors and manual intervention, improved compliance, and enhanced customer satisfaction. According to Gartner, by 2025, half of the business-to-business invoices worldwide will be processed and paid without manual intervention. The future is even more promising, with 80% of business-to-business invoices worldwide expected to be transmitted digitally by 2030.

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Unleashing the Power of IDP: The Multiplier Effect in Action 

The strength of IDP lies in its ability to combine various advanced technologies. It integrates natural language processing (NLP), deep learning models, optical character recognition (OCR), AI analytics, and neural networks to automate the processing of structured, semi-structured, and unstructured documents from various sources. This potent combination can revolutionize a business’s data extraction and analysis capabilities, leading to more informed business decisions. Furthermore, the versatility of Intelligent Document Processing allows businesses to apply the same solution to multiple applications. This reduces the need for separate solutions for each document type, improving efficiency and accuracy across different workflows. This multiplier effect is one of the key advantages of IDP, enabling businesses to maximize their return on investment.

Charting the Course: Overcoming Automation Challenges and Building Successful IDP Use Cases 

While AI and machine learning have revolutionized how businesses operate, many are struggling to extract the full potential of the solutions they invest in. To ensure the success of IDP implementation, businesses must address key challenges such as system integration, model development, performance optimization, user adoption, data security, and job security. Overcoming these challenges is crucial to harness the power of IDP fully. Furthermore, the success of an IDP implementation depends on several factors. These include selecting the right business processes, continuous improvement, stakeholder engagement, and effective data management. These elements contribute to successfully creating the initial use case, which can be replicated across other business processes.

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IDP in Action: A Glimpse into the Transformative Potential 

To illustrate the power of IDP, let’s consider the case of a large telecommunications company. The company was able to automate the contract review process, leading to improved workforce productivity by 70%, processing over 700,000 mobile tower lease contracts, and saving more than $20 million. This example demonstrates the transformative potential of IDP when implemented effectively.

Beyond the Horizon: The Future of IDP and the Next Leap in its Evolution 

As we look to the future, generative AI (such as ChatGPT) has the potential to complement and enhance the capabilities of Intelligent Document Processing solutions. By automating document creation, improving document quality and personalization, enabling new document types, and reducing human bias, generative AI will add even more value to IDP investment.

As we move forward, IDP will play a crucial role in driving automation efforts worldwide and creating a more sustainable planet by digitizing documents. The future of IDP lies in its ability to form the foundation for digital transformation, helping businesses reimagine their operations and unlock new performance levels. This is the next step in the evolution of IDP, and it promises to be a game-changer for businesses worldwide.

As we stand on the brink of a new era in document processing, the adoption and effective implementation of Intelligent Document Processing (IDP) will be a defining factor in the success of businesses. Embracing IDP is about streamlining operations improving efficiency and unlocking new avenues for innovation and growth.

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[To share your insights with us, please write to sghosh@martechseries.com]

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As automated systems become commonplace throughout businesses, there is a natural progression to integrate these systems to achieve even higher productivity levels. Often referred to as hyper- automation, this is one of those terms that is less of a technology and more of a methodology or a goal. Laying the groundwork for a “Connected Enterprise”, hyper-automation is the strategic orchestration and integration of various automation technologies, creating an interconnected business model with heightened productivity. Industry experts describe hyper-automation as “a discipline that helps to combine several technologies in an orchestrated manner to deliver end-to-end, intelligent, event-driven automation.” The goal of that approach is aimed at reducing manual efforts and errors, improving efficiency, and increasing the speed of business operations through the integration of various automation technologies, including automation, robotic process automation (RPA), Artificial Intelligence (AI), Machine Learning (ML), and other advanced solutions to automate business processes end-to-end.

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Hyper-automation enables departments across the enterprise to automate workflows – from data extraction and processing to decision-making and analysis, without human intervention. This can reduce the time and cost associated with business operations while improving accuracy and consistency.

The foundational technological elements for hyper-automation are already available to businesses in all sectors and functions.

Leading analyst firm Gartner recently predicted just last year that “By 2024, 90% of integration-platform-as-a-service vendors will enable process automation, while almost all RPA vendors will offer integration via APIs.” However, integrating advanced automated systems poses significant obstacles for entire enterprises. Challenges include complexity, technical expertise, data compatibility, security, scalability, and cost. Despite these challenges, integrating multiple automation technologies can significantly benefit efficiency, accuracy, and cost savings. By addressing these challenges, organizations can create a connected enterprise that unites technology, processes, and people, driving a more intelligent and efficient business environment.

Complexity

First and foremost, organizations as a whole need to be prepared to tackle the complexity of implementing these technologies. The complexity of implementing hyper-automation can be immense, especially when envisioning it as the key to a Connected Enterprise. To give a sense of how complex this process can be, let’s use a hypothetical example of a large financial institution that has decided to implement hyper-automation to enhance its loan approval process. To achieve this goal, they needs to integrate multiple tools and technologies, such as RPA, AI, ML, natural language processing (NLP), and advanced analytics. That’s assuming that the organization already has these solutions in place to begin with.

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What’s more, the technology implementation team must manage the cultural and organizational changes that come with implementing hyper-automation. They need to collaborate with different departments, provide training and support, and address any concerns or resistance from employees.

Taken together, these elements present a significant challenge. Gartner again provides two key steps to take prior to any actual work. First, “Create a hyper-automation capability map by working with your business peers to identify all capabilities required to achieve task automation, process automation and augmentation goals.”

And second, “Select technologies by prioritizing the key hyper-automation capabilities required to deliver the identified use cases and by following a decision framework to determine the right combination of technologies.” Following these two steps will lay the groundwork for any successful implementation. Technical expertise

Teams across the enterprise must ensure seamless integration of these diverse technologies, which can be challenging as each tool may have different requirements, architecture, and protocols. The team must create a unified platform that allows these tools to work together efficiently. In order to achieve this goal, organizations need to make sure they have the right personnel for the job before taking this next step.

Data compatibility, consistency, and quality 

With data being gathered from multiple sources, it’s essential for the entire enterprise to ensure data consistency and quality. In a Connected Enterprise, data compatibility, consistency, and quality are of paramount importance.  They need to establish processes for data cleansing, validation, and integration to avoid inconsistencies and inaccuracies that could compromise the automation process.

For example, data management team can standardize data formats and structures to ensure that data is consistent across different systems and applications. This can be achieved by defining a set of data standards, such as data type, format, and length, and enforcing them across the organization.

Similarly, data management team can also leverage data integration tools to extract, transform, and load data from various sources into a unified data model. This can help to ensure that data is consistent, accurate, and up to date across different systems and applications.

Establishing data governance policies will also be important. Solid data governance policies ensure high-quality data that is managed and used in a consistent and secure manner across the organization. This can include defining data ownership, access, security, and privacy policies, as well as establishing data stewardship roles and responsibilities.

Finally, it’s the responsibility of the data stewardship division to continuously monitor data quality to implement quality checks and to ID and resolve issues before they arise. This can be done by implementing data quality metrics and dashboards, as well as establishing data quality management processes and procedures.

Security and compliance

While secure systems are a priority for just about every organization, hyper-automation brings new wrinkles to this issue given the lack of human involvement, particularly with often sensitive financial, personal, or healthcare data being processed.  As the enterprise becomes more connected and automated, the security and compliance aspects gain higher importance. Robust access control, data encryption, and auditing mechanisms are critical tools to ensuring your automated systems are safe from attack.

Scalability

As the organization grows and transforms into a more Connected Enterprise, it’s vital for the whole enterprise to ensure that the hyper-automation platform can scale to accommodate increasing workloads and adapt to changing business needs. This requires constant monitoring, updates, and optimization of the automation infrastructure. Selecting vendors and technologies at the outset that are capable of growing as your implementation grows is also very important.

Overall, tackling the challenges of hyper-automation will, in the end, deliver a raft of important benefits to the enterprise. Successfully implemented hyper-automation paves the way for a Connected Enterprise, eliminating bottlenecks across all operations, optimizing processes, eliminating the need for time-consuming manual tasks, and fostering a more productive, driven, and motivated workforce. The journey toward a Connected Enterprise may be complex, but the benefits it brings make it worth the effort. Once implemented, it’s the responsibility of the entire organization to regularly evaluate the effectiveness of the hyper-automation solution and fine-tune it to ensure maximum efficiency and return on investment. This may involve monitoring performance metrics, identifying bottlenecks, and implementing improvements. Organizations must plan carefully, involve stakeholders, and continually optimize their hyper-automation strategies.

However, when successfully implemented, enterprises can expect significant benefits; bottlenecks eliminated across all operations, optimized processes, obviating the need for time-consuming manual tasks, and a more productive, driven, and motivated workforce.

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