Artificial Intelligence

At the start of 2020, RPA is already worth a staggering total of $1.5 billion. This means that companies are increasingly integrating this new hot-button tool into their processes and infrastructure. However, when utilizing something as new as RPA into an organization, how can organizations get the most out of it?

Implementing RPA is just the start

Deploying sustainable RPA into a company’s processes is harder than just clicking a button and turning it on. Many common pitfalls that companies get caught-in, fall under the categories of organizational, technical and process.

Organizational pitfalls occur when there is a lack of leadership, IT support, RPA strategy and established responsibilities.
Process pitfalls happen when choosing a process with insignificant business impact, a process experiencing frequent changes, and a process where better custom solutions exist.
Technical pitfalls arise when companies don’t fully leverage their chosen solutions, rely on outdated solutions, and don’t take advantage of RPA resources and vendors.

After purchasing RPA, organizations start considering how their IT infrastructure, initial integration into existing systems, on-boarding processes and future procedures & frameworks will have to be adjusted. Unlike a lot of digital tools and platforms out there, RPA not only needs to be carefully implemented and maintained, but also adapted for specific industries.

Don’t get advice, get a partner

Despite RPA’s ability to perform tasks on its own, the overall success and RoI it brings is wholly dependent on a trusted team of experts that have deep knowledge of both RPA and specific industries. Possessing knowledge about RPA and its capabilities is crucial but having an expert that also has extensive industry experience provides organizations with clear direction. The best way to establish a team onboard for the long-haul, is to create an RPA Center of Excellence (CoE). A CoE provides a strong and centralized structure and governance for a variety of aspects of automation such as skills, resources, assets, coding, security standards, and best practices.

One of our clients, a leading provider of electrical connection and protection solutions, wanted to start automating a range of their processes such as Sales Order Processing, Purchase Order Processing, Journal Entries process and Customer Credit Requests. Since the client was totally new to RPA, they did not know how to implement it into their current infrastructure and strategize for the future. We took a three-pronged approach to our solution by providing: advisory services, project management and development, and the RPA bot licensing and maintenance.

Advisory services provided the client with a solid foundation in which their RPA process could be implemented and then developed. We ensured that the launch would be successful by providing guidance on important elements such as Bot ID Creation, Impact Assessments, Design Standards, Post Production Support and more.

Project management and development gave the client much needed support in streamlining a variety of their processes, while ensuring that RPA would optimize their services. We involved the client in the development and planning process for implementing RPA into their organization, which provided them with an Executive-level vision on a successful automation journey.

RPA bot licensing and maintenance focused on giving specific bot licenses for the client that would fit their long-term RPA plan. Maintenance support for the bots proved to be invaluable, as the client was ensured that each one was delivering excellence and maintaining high standards of operation.

Developing a plan with the future in-mind

It can be easy to get caught-up in RPA’s various capabilities, especially since it has such a quick implementation and payback phase. However, the true power of RPA is the fact that it can be built into an overall organizational strategy. Regardless of any changes that the future holds, having a knowledgeable partner by your side will help you side-step common pitfalls.

RPA is more than just an automation, it is an adaptable tool that can be utilized for any long-term strategy.
The right partner will take the time to fully understand your organization’s goals, objectives and needs.

Professional services firms strive to create a lean and efficient business development function. Despite investing in several technology-enabled solutions, Partners still do not have access to timely and relevant intelligence. If leveraging tools and technology is the right approach, then what’s falling short?

Partners constantly worry about accessing timely and relevant intelligence that is critical to making impactful business decisions

Professional services firms have traditionally relied on Partners/fee-earners to be rainmakers, leading client acquisition and new business development efforts. However, as client delivery invariably takes precedence over new business development, very limited bandwidth is available with the Partners for pursuing business development initiatives.

Nevertheless, Partners require timely and relevant intelligence to draw actionable insights and make impactful business decisions. Being able to achieve this is more vital now than ever, with the coming of the age of sector convergence and creative disruption. For this, they rely on business development teams to provide in-depth research on not only their client’s sector but also their own competitors. The best use of Partners’ limited bandwidth should be in providing subject matter expertise and being in front of prospective clients necessary to close the deal, while that of the business development professionals should be in preparing targeted pitches rather than worrying about gathering intelligence.

There’s no question then that leveraging technology-enabled solutions is vital to bringing in efficiencies in the business development value chain. The right technology-enabled solution should free up the already lean business development bandwidth to focus on higher order problems and create incremental value.

AI and ML may automate data gathering, but can they lead to real return on investments (RoI) on their own?

Professional services firms have been flooded with all sorts of tools promising to provide intelligence by implementing Artificial Intelligence (AI) and Machine Learning (ML) technologies. Sometimes, even a tool with a few basic rules programmed in a deterministic fashion, is being pushed as AI / ML. But far from delivering intelligence that can call for action, the sheer quantity of information can overwhelm business development professionals with data that is inconsistent, sometimes irrelevant, and almost always disconnected, thereby slowing progress.

In tandem, AI and ML technologies may minimize human intervention by automating certain rule-based tasks associated with data gathering. But can they deliver intelligence just on their own?

Evalueserve argues AI has not yet reached a stage where it can be expected to deliver a complete end-to-end solution. In other words, AI tools might be essential for automating certain rule-based tasks as part of some use cases and support the minds, but they are not (yet) able to provide truly actionable insights or automate verifications where a judgement call has to happen. The problem is not the AI engine itself, rather it is the complete neglect of the rest of the value chain it is to sit in. Therefore, the better approach is to have a master plan in place that addresses the overall pain point in a comprehensive manner. This should not only include the actual problem, but also include the equally important workflow aspects especially from a human angle. It is only when mind and machine work together in an optimally balanced fashion, will the RoI be generated and the end user benefits be achieved. A combination of mind+machine™, technology and human minds complementing each other, is vital in delivering higher efficiency and greater quality.

Therefore, professional services firms need to find solutions that not only leverage technology but combine it with human intelligence. This will act as the growth engines professional services firms need to scale their business development activities across practices and functions.

Insights generated but not readily accessible at the last mile do no good

Oftentimes insights are lost or isolated in static spreadsheets or PowerPoint presentations that are not accessible to Partners when in front of the clients, who need them the most. An efficient insight generation platform should directly equip Partners, anywhere and on any device, with timely insights needed to make impactful business decisions. In other words, the solution should have the ability to disseminate insights to the last mile on any device, lowering response time and giving power to end-users right where they are presenting their pitch to prospective clients. Additionally, users should have the ability to slice and dice data through visual interactive dashboards.

Conclusion

In summary, Partners struggling to scale their business development efforts should equip their business development teams with technology-enabled solutions that combine the power of mind+machine™ to provide timely and relevant intelligence that can call for action while anticipating threats and opportunities alike.

Benefits that the Right Technology-enabled Insight Generation Solution Should Offer

To learn how Evalueserve’s cloud-based AI platform, Insightsfirst for professional services firms leverages Mind+Machine™ to provide actionable insights for your business development needs, write to PS@Evalueserve.com

The growth and change of market research (MR) is happening at an accelerated pace. This has enabled a multitude of organizations to address data-driven issues by using new technology to support their projects.

Recent advancements in digitization, data analytics and data management related technologies have started to impact the MR function in terms of its importance, insights, relevance, scope and nature of its operations, and eventual impact in corporate decision making. However, during this flurry of adoption and implementation, MR projects are being driven by data operations and data technology rather than by their most vital assets—clients.

The disadvantage of having a narrow technology focus is its inability to factor-in evolving business requirements. Achieving a faster and more efficient MR process through advanced technology is counter productive if the outcome is not aligned with business goals and vision. The inability to align technology with end-users and clients will make any productivity or efficiency gains seen as simple cost saving measures, rather than having a direct impact on the end-client. The heart of any MR function strives to make a positive impact on data, audience/consumers and clients.

Audience: utilize the MR function to attain a deeper level of engagement with audiences. MR teams can now capture meaningful and relevant insights with open-ended questions, rather than using limited closed-ended questions. The advancement in technology (e.g. Natural Language Processing, Machine Learning, Artificial Intelligence) has made it easier for audiences to share their thoughts, which in-turn enables organizations to analyze the responses and efficiently derive insights. As a result, Organizations can make specific changes and plans which can align with their client’s goals.
Data Operations: gain access to faster and cheaper data processing and reporting. Prior to the acceleration in the development of MR technology, these processes required significant amounts of manpower, time and capital. Value-additions such as data integration, advanced analytics and smarter systems are now in-scope for a range of organizations.
End Users: there are greater collaborative possibilities between business functions. MR teams and businesses can collaborate with ease throughout the research process and generate relevant insights. End-users have access to reporting tools, contributing to the user experience by providing near-real time updates and actionable insights.

Harnessing emerging technologies that take an integrated approach towards targeting the entire MR value chain, while bringing relevant MR insights, allows business functions to reclaim ownership and command of their MR projects. Every new tool that is adopted by an organization will work to optimize the MR value chain, which will then contribute to generating actionable insights. An integrated approach also ensures that businesses can adapt and adjust throughout each step of the MR process. Having an agile and responsive process enables businesses to make sure they are achieving goals and objectives.

One of our global payments & technology clients wanted to look at centralizing and streamlining its consumer tracking and survey operations, in order to have a data driven decision-making process across their marketing departments. Previously, the client conducted their survey operations through separate regional marketing developments. This made their survey data processing time longer and inconsistent with reporting standards across regions, while also making it harder to disseminate information between the MR team and the business functions. We created a center of excellence for their MR operations, which supported the client in developing and disseminating insights using advanced analytical techniques (NLP for text-based feedback/comments, Machine Learning-based profiling, etc.). Dynamic interactive visual dashboards provided stakeholders with access to relevant and deep insights. This helped the client with streamlining the data decision-making processes across the marketing department for their annual planning, marketing campaigns, customer support services and product development initiatives.

An integrated approach to MR helps clients achieve universal mapping and alignment of customer and business goals, ensuring that collaboration with stakeholders will achieve consistent results. Businesses will also be able to have a single view of customers through the integration of MR data with various other data sources (such as CRM, web, social, support data, etc.). Having near real-time insights will provide the client with a faster, scalable, responsive, efficient, accurate and effective insights engine.

Firms are adopting technology in order to keep-up with evolving MR expectations, however these initiatives are focusing on making the data operations more efficient rather than making an impact on clients.
Technology affects all parts of the MR function, which should work towards generating client-focused, relevant and actionable insights.

To learn more about how your MR function can be optimized and improved for your development needs, please write to marketing@evalueserve.com

Staying ahead of competitors in an everchanging landscape, is something that many organizations find themselves struggling with. Up until recently, organizations only worried about what was happening in their industry but the co-mingling of digital platforms, data analytics, mobile capabilities, the Internet of Things (IoT), and other emerging technologies are broadening the scope of what organizations need to stay on top of. As a result, organizations have been flooded and overwhelmed by of all sorts of intelligence gathering tools. With each touting that they are the best choice, it can be tricky finding the best tool that helps organizations both navigate through the clutter and achieve their goals.

Traditional intelligence can’t keep up with the needs of organizations anymore. Sporadically receiving insights without a set schedule, introducing insights with no correlation to each other and under-utilizing different data sources is pushing traditional intelligence to the wayside. Organizations need intelligence that is consistent, thorough and fast. Having up-to-the minute intelligence gives organizations the power to not only pursue opportunities, but to also prepare for threats ahead of time. Intelligence should also be focused on converting internal and external information into actionable insights that can make a positive change to the user.

Introducing Evalueserve’s Insightsfirst—a market and competitive intelligence platform that harnesses the power of your internal knowledge and external information to generate strategic insights, delivered to the right stakeholders at the right time. It is the cornerstone of all the ‘Insights Program’ delivered by Evalueserve.

A lot of organizations have leveraged the effective Insights provided by Evalueserve’s platform led insights program to:

Spot opportunities for growth such as finding new sources of revenue, finding and appealing to new clients, and refining their processes and approaches. By taking an integrated approach towards payments intelligence, a global payment service provider was able to identify and take timely action on relevant, recent, and valuable opportunities in the payments market. Read more about this case here.
Increase market presence, achieve consistency through a robust strategy development process, and broaden stakeholder bandwidth. An industrial goods company leveraged Insightsfirst’s robust and comprehensive data collections, rigorous analysis, and dynamic visualizations of relevant information to accelerate and unify key marketing and strategy solutions. Read more about this case here.
Quickly respond to threats and stay ahead of competitors by monitoring innovative technologies, products and ingredient trends. A personal care giant used Insightsfirst to take a proactive stance on industry trends and innovations which enabled them to plan and act ahead of competitors. Insightsfirst’s AI-powered elements helped facilitate a comprehensive data gathering process from 10 geographic locations across the globe. Read more about this case here.

A couple of years ago, RPA was seen as the “next big thing”, prompting professional services firms to dive head first–but results fell far short of the hype. Here, we argue that RPA can still offer benefits in the specific areas of research and analytics, as long as PS firms approach it in the right way and on the right use cases.

What is RPA?

In Robotic Process Automation (RPA), software “robots” mimic human actions to carry out specific tasks. In some ways, RPA tools are powerful and flexible versions of simple automations found in popular applications such as Microsoft Excel. Often, RPA tools emulate the way humans approach a task, and interact with other systems as a human would.

RPA is different from both artificial intelligence (AI) and machine learning (ML). While AI and ML involve some “thinking” and “learning”, respectively, RPA is all about “doing”. It is ideal for rule-intensive tasks that require little human reasoning. With the evolution of RPA technologies, there are elements of the knowledge services and life cycle that can be done faster or more efficiently.

For example, RPAs are good at answering simple factual questions, or pinpointing valuable information among a mass of unstructured data. Some examples can be data collection/assimilation that can be done by bots; the chatbots that are now familiar from many firms’ websites, where users can ‘talk’ to a bot in their own language; etc.

Trends in RPA

A study by Deloitte found that 81% of digital leaders plan to invest in RPA by 2020. Taking a longer view, a study by McKinsey suggested that 60% of all work could be automated by 2055. According to Deloitte, all professional services (PS) firms will have to consider using technology sooner or later. “[PS firms’] clients are asking for more efficient and cost effective services; their staff want to spend more time on value-adding activities; and the financial incentives are improving as the cost of technology declines.”

In response to these trends, interest in RPA suddenly exploded a couple of years ago, as PS firms looked for a way to automate the entire consulting process. The problem is that consulting is “mind-heavy”, demanding problem-structuring, problem-solving, analysis and solutioning to unique problems. At the end of the day, you can’t get too powerful insights by applying rules.

Moreover, even the repetitive tasks that PS staff carry out are fairly niche, and demand cognitive skills. For example, they often require finding insights from multiple data streams, intelligent reuse of material created for a previous assignment, understanding unstructured data or perceiving complex relationships.

Problems arise when PS firms push too hard for automation without considering these downsides. If they pursue automation for its own sake, or with too much focus on short-term benefits and cost-cutting, they can wind up jeopardizing quality for the sake of efficiency. And when you live or die by your know-how, that sort of risk is simply not worth taking.

In our experience, many PS firms’ adventures with RPA have not ended as well as they hoped. McKinsey’s findings underline this. “The economic outcomes often aren’t as rosy as originally projected,” notes their report. “While it may be possible to automate 30 percent of tasks for the majority of occupations, that doesn’t neatly translate into a 30 percent cost reduction… Unless the process and the organization are reconfigured, savings can prove ethereal. Also, bots treat localized pain points… fixing one bottleneck may just move the problem elsewhere.”

Where RPA can help PS firms

When a consultant starts work on an assignment, they need to get a detailed picture of what is “out there” in terms of market offerings, competitor activity, industry trends, expert insights and so on. As a result, they typically spend as much as 70% of their time gathering data. Then, they need to capture their ideas in a PowerPoint presentation, so they can pitch them to the client. Once again, this involves bringing together digital resources from multiple sources, which takes many hours but adds only marginal value.

Moreover, many PS firms are promoting a self-service business model. They want all their staff to be able to do basic research, produce brand-compliant output, create pitch decks, etc. But these staff members are necessarily not trained researchers or analysts, They may not know which database to scan for what type of data, what the right search strings would be or how to make the best use of available infographics. When these problems are multiplied over thousands of staff in multiple locations, it’s clear that there are major opportunities for time and cost savings from automation.

There are parts of select tasks, such as online research or creating standardized PowerPoint slides, where robots can make a valuable contribution. While it might take a junior analyst several hours to pull together all the logos required for a “tombstone” slide, an automated productivity tool can do the same job in seconds. Standard, brand-approved templates can be made available through a single, centralized online repository.

We believe RPA has strong potential to add value and deliver cost savings for PS firms in the research and analysis function. However, they do need to identify the right use cases by breaking the problem down, pinpointing areas where RPA can genuinely add value and considering what market solutions might work for them.

Benefits of Adopting RPA

Instead of replacing human consultants, technology can make their expertise both broader and deeper – and make it cheaper to deliver that expertise too. “Rapid advances in machine learning and RPA are helping to create a new source of supply in the PS market,” says the World Economic Forum.

Ultimately, automation opens up new ways for consultants to add value. As the WEF has observed, “By automating routine tasks, technology is freeing people to focus on solving higher-order problems… Companies that can anticipate change and react faster than competitors will stay ahead of the curve.”

From a purely internal perspective, smart use of RPA tools helps to create an engaging employee experience and boost employee morale. The most talented analysts do not want to spend their working lives trawling the internet for news articles; they expect a machine to do it on their behalf.

This positive, proactive outlook is the ideal context to start thinking about how RPA can make a contribution to your PS firm, beyond simple cost-cutting or even reducing headcount. “We’ve seen a shift away from anxiety towards generating new ideas that will help drive the business forward,” comments David Royle, at auditors Grant Thornton. In other words, it’s not about jealously guarding your own job, but about finding areas where technology can free you up to add more value. That is the heart of the promise made by RPA.

To find out how Evalueserve’s subject matter experts or technology expertise could help you save time and cost, check out this case study or get in touch with us at ps@evalueserve.com.

In recent times, my conversations with general managers have inevitably veered to the topic of Artificial Intelligence (AI) and Machine Learning (ML). What puzzled me was the lack of true understanding of these terms and the imprecision with which they were being used. Some general managers stated their need for better understanding, while others persisted with these terms, trying to impress the audience with half-knowledge or hoping that the other participants of the meeting don’t know the details either.

I realized that the key challenge, when talking about AI and ML, is determining what we really mean and what such technologies can really deliver at this point in time. It is vital that managers who decide on resource allocation for ML and AI projects are able to clearly state the benefits that products containing ‘smart’ technologies will deliver to their clients.

Overstating the market promise or misallocating resources can have a devastating effect on organizations. In the last 12 months, I have come across two serious cases of organizations having to write off USD 60 million and USD 30 million respectively in misguided AI / ML projects. Even worse, one of the organizations (operating in the healthcare domain) and the corresponding AI vendor suffered negative reputational effects.

As I have already mentioned, in most of my conversations I notice these terms being used interchangeably, and sometimes in very fuzzy or plainly erroneous ways. Moreover, these technologies’ ability to deliver is often overstated by enthusiastic program managers or semi-knowledgeable general managers and sales people.

One of the reasons for such over-enthusiasm might be the current hype around these topics, and the very public progress made by companies such as Google with TensorFlow and AlphaGo, Amazon, Microsoft, and IBM Watson.

The result? Everything, even a tool with a few basic rules programmed in a deterministic fashion, is being pushed as AI / ML. On top of that, throw in a few complex terms, such as deep neural networks, and you will emerge the winner in party talk.

All in all, there is a lack understanding of the difference between ‘true’ cognitive AI, in which the machine comes up with the rules or findings by itself (e.g., with unassisted deep learning); smart behavior trained by humans (e.g., with assisted deep learning); and rule-based expert systems in which a human programs deterministic rule sets that are run over large data sets. Often the terms AI and ML, and also Big Data are mentioned in one breath, although they are very different concepts. (Do you still remember the term Big Data? Not many of our clients refer to it anymore.)

Just because a product uses AI / ML in its marketing brochure, it does not mean that the product will solve the use case at hand or generate RoI. In fact, current developments suggest that specialized application of AI and ML, trained for specific and narrow use cases, deliver much better results. Managers should be highly cautious with statements such as ‘We have AI and ML in our product.’

In any business discussion involving AI, ML, or Big Data, it is important that these terms are used correctly, so that clients know exactly what outcome and performance to expect from projects that use these technologies. AI refers to ‘the broader concept of machines being able to carry out tasks in a way that we would consider “smart”.’ ML can be seen as an application of AI, where machines learn and improve, based on certain rule sets or patterns, independent of their ‘smartness’ at the start of a project.

A common misreading is that AI and ML products always need Big Data for training. While it is true that several ML applications require Big Data sets, e.g., in image recognition, several other applications don’t. They can live very well with ‘small data’, e.g., when a machine learns to optimize its user interface for a specific customer. Keep in mind that simple ML with ‘small data’ may provide very high RoI.

Of course, this barely scratches the surface, but the purpose of this blog post is to highlight the need for better understanding of underlying concepts and their ability to deliver results among the general management community.