Artificial Intelligence in Business: Insights from Both Sides of the Pond
We introduce a series of talks where Iflexion’s executives and consulting experts overview the current state, challenges and future of enterprise digital transformation. In this inaugural long-read, Yaroslav Kuflinski, our designated AI observer, shares his vision of AI integration into business, with insights from hub.berlin and O’Reilly NY conferences that happened earlier this year.
- On Business Challenges AI Can Potentially Solve
- Using AI for Better Business Process Automation
- AI Streamlining Company-Customer Engagement
- Cognitive and Predictive Analysis
- On the Challenges AI Brings Up
- Not Enough Skilled Specialists to Hire
- Poor or Lacking Data
- Small Data, Deep Learning
- Compliance Matters
The hype bubble around AI makes many executives think that if they’re not already in the loop, they’re too far behind and missing out on the power of AI transformation. The reality, however, is that businesses should think of AI just like of another helpful tool, simply one of strategic investments.
Not every process should be automated via AI just because it’s the latest trendy thing to do. The approach of “if you have a hammer, everything looks like a nail” is generally a bad idea.
A big part of the effective use of artificial intelligence in business settings is being smart about where to start. Mass corporate adoption of intelligent technologies is still mostly in its infancy, with possibilities and pitfalls growing by the day; it can often seem hard to even begin to work out what to focus on.
So how to figure out where and how to best use AI for your company, right now?
Judging by presentations from this year's O’Reilly NY AI conference, this question rings even truer with today’s growth and democratization of AI and ML. The overall vibe and message to businesses can be summed up as:
It’s wrong to just say ‘We’re going to use AI for our business’. What needs to be said is ‘What is the problem, the goal, the data? How do we break it all down?’ Functionality and real problems should be decision drivers.
A good way to go about this is to look at the common starting points in recent use cases, the ‘whats and whys’ that successful AI adopters across the globe share. What possibilities and challenges do they take into account first? What are the prerequisites? What data and processes are good starting bets for AI integration? And all the while we should keep in mind whether the companies in question are large or medium-sized, from the same or adjacent industry, whether they operate in Europe, North America, and so on.
On Business Challenges AI Can Potentially Solve
Around the world, I’m seeing how companies are looking to gain value from AI in three major categories: business process automation, customer engagement, and data analysis and insights. For a company that just begins moving toward AI development (and isn’t exactly looking to be a trailblazer in the field), it’s generally a good idea to check whether the process or processes singled out fit the mold.
Using AI for Better Business Process Automation
Intelligent business process automation largely deals with administrative, management and financial processes and is all about optimizing tasks and workflows through the combination of RPA and AI.
An example of that would be identifying and transferring data from various enterprise databases into a unified record system. Think moving data from emails and call center systems in a CRM or CDP business information systems—with sorting, structuring and assignment of data to corresponding customer profiles, all automated and happening on the go with the help of artificial intelligence and RPA. Managing credit data and transactions also comes to mind, as well as automated continuous checking for payment inconsistencies and format compliance in real time.
AI Streamlining Company-Customer Engagement
Most companies provide services or products that customers tend to have questions about before, during, and after the purchase. What if users keep sending callback and support requests around the clock? Or perhaps there are rush hours or simply a too large customer base leaving too many messages, making it impossible to get back to all of them promptly. A recent CX report shows that demands for support grow, satisfaction lowers, and companies—SMBs in particular—get swamped with incoming inquiries they’re often unable to effectively keep up with.
Most likely you’ve at least thought of getting a chatbot of one sort or another. Back in 2016, almost everyone I talked to expected their company to be equipped with a smart assistant chat or a voice bot in a few years. Today's competitive market only keeps pushing these numbers up, making this type of business AI easily the most popular, readily available and accessible one. It’s also very comprehensive in terms of tangible goals and clear immediate benefits of implementation, like improved revenue, so no wonder there is an uptrend.
Thanks to all that and the growing number of commercial and custom solutions, today NLP and ML-based systems for customer engagement are among the most readily adopted types of AI in business. Aside from chatbots and text-based intelligent assistants, this category also include voice systems for call centers, recommender services and platforms (through the use of visual and textual information), and so on.
Cognitive and Predictive Analysis
This is about analyzing data and identifying patterns for automated interpretations and predictive analytics, like fraud detection, actuarial analysis and assessment automation, as well as predicting what service, content or items a profiled customer might want to purchase.
These are good candidates for an AI makeover, so you may as well go for it if your enterprise can square its tasks and processes into any of these categories while being fully data-driven.
Paraphrasing Ben Lorica, the majority of successful artificial intelligence applications are not about reinventing the wheel and completely starting from scratch, but essentially building on the existing data and BI frameworks, clearing and structuring the former and layering machine learning on top of the latter.
On the Challenges AI Brings Up
It’s not all roses once the ‘where to start’ part of the question is cleared. AI comes with its challenges, too, so anyone making decisions to adopt it should also keep them in mind.
Not Enough Skilled Specialists to Hire
Last year, O’Reilly named AI skill gap the largest barrier to widespread AI adoption. With corporate demand skyrocketing in the last few years, there’s just not enough experts. Another issue is that the educational programs often can’t keep up with the rapid pace of innovation and the surge of new discoveries within the field.
Sought-after AI software developers have up-to-date university-backed training, upskill constantly, and have considerable on-the-job experience. Companies that have just started adopting AI strategies into their operations need seasoned professionals like that to take leadership roles, shape AI and data strategies, and oversee the processes.
Aside from much-needed changes in official educational programs, there are, however, other ways to find answers to that problem. Retraining current employees in-house while employing skilled supervisors can be a better solution in the long run, particularly for bigger enterprises. Delegating AI implementation to external providers and consultants is also an option, especially well-suited for SMBs and organizations at the very first stage of the process.
Poor or Lacking Data
Many people know about the “garbage in, garbage out” concept. Poor input data can lead to all sorts of issues, from underperformance to negative outcomes, like the much-dreaded machine learning bias.
When AI and machine learning are used for business the problem of poor data quality can come from quite a few directions, such as obsolete legacy systems, fractured datasets, incomplete or fragmented ownership of data, short history of data, unstructured and incomplete data in general, etc.
Take for example the traditional siloed model of collaboration between departments, and subsequently, the problem of segmented, sometimes sealed-off parts of datasets. In such a system, machine learning is done on a specifically provided training data. Everything works great until the PoC stage is over and done with, and then it turns out the algorithm can't be implemented 'for real' because the actual data it’s supposed to work on is fragmented and locked in separate silos.
This is just one example, but different data quality issues can add up to an unsolvable obstacle, resulting in curbing initiatives and well-into-development projects alike.
The solution is a cohesive enterprise-wide data strategy, reworking the vision of data acquisition, storage, access, security, and operability. It should be a product of collaboration between key departments, leaders and stakeholders.
A strong data strategy that will become the basis of the entire AI business project needs to break silos, engage key players, and present a unified vision under a strong leadership. Like with any innovative process integration, cross-functional teams are what success depends on.
Small Data, Deep Learning
Quality data is crucial when it comes to machine learning and analytics, yes, but the quantity of the data is another pain point that is often overlooked in discussions about commercial adoption of AI.
Take one of the more impressive and well-known examples of machine learning and AI: image (facial) recognition and subsequent face generation.
With the latest GAN 2.0 by NVIDIA, we can now produce hi-res realistic images of people who have never existed. The algorithm was trained on ImageNet dataset—which has 14 million hand-annotated images in it, and all of them were used for machine learning and image generating.
The problem is that while computers now are incredible at recognizing patterns and learning things, they still need all that big structured data to learn to do something at the business-grade level. Gathering millions of clean, annotated data points for machine learning and effective AI implementation might simply prove too expensive or lengthy a process for many companies, especially SMEs.
To better illustrate it, I’ll borrow an example from the hub.berlin presentation by Ruchir Puri (Intel) on the future of AI for enterprises. Contrary to what’s often suggested in the media, there are fundamental differences between the way machines and humans learn to recognize images and objects in front of them. A toddler only needs a couple of pictures or photos of a cat to be able to recognize whatever cat, living or depicted, she’ll encounter in her life from that moment. Meanwhile, an ML-based cat recognition process will take labeled images of about a thousand of various cats in different angles, lighting, shapes and forms to reasonably identify photos of real-life cats (it can fail still, and will not recognize toys, cat-shaped ceramic figurines or drawn cats).
Moving on to enterprise-level examples, imagine an ML-based software that needs to audit and perform fact checks, compliance checks and cross-reference checks of legal documents. To be able to successfully do this, hundreds of thousands of specific documents (if not more) will be needed; structured, labeled, annotated in a particular way by qualified lawyers—so the algorithm could learn on that data. The scope, time and resources that’ll be necessary to acquire a dataset like that are far beyond the means of most organizations.
Not every data is big data, and especially with the current tendency toward hyper segmentation and using micro data pools for analysis—for example, when working with personalization—the need for new ways of assessment and processing becomes obvious.
At the moment, the ability to provide that data or training algorithms to operate on small data to learn more from less remains one of the biggest obstacles to enabling effective and widespread enterprise AI adoption. Major players in the field are working on it, with Intel reporting recent successes (though currently still moderate).
Another acute problem of gathering and operating as much and as varied data as possible is legal limitations. The recent rise of the GDPR in the EU and data security and privacy legislation in other regions brought a number of restrictions to navigate when dealing with data-driven automation processes. AI project governance also means minding other standards and compliance guidelines (like HIPAA).
AI and ML have been there for half a century, yet they are still nascent as corporate enterprise systems. Organizations and legislative bodies alike need time to learn, time to establish and follow the guidelines, time for trial and error, and for working out the kinks in the process of standardization.
Meanwhile, balancing innovation with both internal and external regulations, figuring out blind spots where there isn’t much overlap, and keeping up with the landscape changes are all crucial points in using artificial intelligence in a company.
Recorded and transcribed by Nika Vartanova
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