Artificial Intelligence (AI) is fast becoming a required cornerstone for any successful enterprise. The results are in, and if your corporation isn’t implementing AI, you’re not just standing still; you’re falling behind. Still, implementing AI is a big undertaking. Our annual AIIA Network Survey received feedback from global corporate enterprise practitioners. And what our industry certainly highlights is that most organizations are stuck on data. In fact, a whopping 72% responded that they are still in the data cleaning and mining phase or are still working on understanding what historical/ supplemental data is of value in preparing for their predictive analytics journey. The fact that practitioners are stuck grappling with data is telling. Data has truly been a stumbling block for the past three years according to our annual survey. Coupled with this finding is another repeat survey highlight- corporate culture is also a stumbling block, with 46% of respondents citing culture or change management as their biggest hurdle to implementing AI.

Data has truly been a stumbling block for the past three years according to our annual survey. Coupled with this finding is another repeat survey highlight- corporate culture is also a stumbling block, with 46% of respondents citing culture or change management as their biggest hurdle to implementing AI.

If the full value of AI isn’t realized and communicated during all stages of the planning and implementation process, change management is likely to stall or fail all together. And if you can’t affect change management within your human workforce, the culture can’t evolve. Which puts you back at the beginning.

AI implementation attempts often dead-end after the pilot stage. We spoke with Abhijit Kakhandiki, Senior VP of Products and Engineering at Automation Anywhere, who explains this phenomenon well: “At an organizational level, people have different philosophies and different personalities. They can say, ‘I’ll try AI out with some key use cases’ and mean it. Still, that approach may stagnate at a certain point as they realize they only have the pilot team who understands AI and are receptive to change. The effort then fizzles and stalls.”

How do you integrate artificial intelligence into your enterprise so that it is sustainable, and drives value-add?

Interestingly, by 2025, 80% of those we surveyed expect to be past the planning and proof of concept stage and onto actively leveraging AI to strengthen their business model. This means that if you haven’t done so yet, it’s time to set your enterprise AI vision.



Ask anyone and they’ve got their own definition of AI. Here are a few:

  1. Per Wikipedia: In computer science, artificial intelligence (AI), sometimes called machine intelligence, is intelligence demonstrated by machines, in contrast to the natural intelligence displayed by humans.
  2. Leading AI textbooks define the field as the study of “intelligent agents”: Any device that perceives its environment and takes actions that maximize its chance of successfully achieving its goals.
  3. Colloquially, the term “artificial intelligence” is often used to describe machines (or computers) that mimic “cognitive” functions that humans associate with the human mind, such as “learning” and “problem solving.”
  4. Per Marvin Minsky, AI luminary and Co-Founder of MIT’s AI Lab: “AI is the science of making machines do things that would require intelligence if done by [humans].”



As noted in the introduction, corporate enterprise practitioners are stuck on a combination of data and culture. The definitions above showcase that artificial intelligence takes its lead from humans. Survey respondents are saying the data isn’t speaking to them and they have a culture that’s not ready for artificial intelligence.

That’s the tail wagging the dog. The reason that global corporate enterprise is having trouble with artificial intelligence is because it hasn’t set a vision from which (artificial) intelligence can be conceived.


Harness your data for the insights you want to gain.

“Make sense of your data and integrate with your systems so you can discover how best to interpret, visualize, report and analyse the insights you want to gain.”

-Deepak Subbarao, Digital Transformation Lead, Zurich Insurance Co. at AI LIVE EMEA on the AI & Intelligent Automation Network.

Transcend, collaborate and consolidate to inform your vision.

“We don’t have people with the vision to create the data models and to be able to now train the AIs up. We don’t have a vision for how to transcend corporate silos to consolidate information from across different divisions that are learned from different sources.”

-Vivek Wadhwa, Distinguished Fellow, Carnegie Mellon & Harvard Law School on the AI & Intelligent Automation podcast.

Simultaneous short-term action leads to long-term scalability.

“Be quick in trying to pick low hanging fruit. But simultaneously ensure that you have an approach for long-term which will allow much faster scalability.”

-Anshuman Das, Digital Transformation Leader, Large-Scale Global Entertainment Company


“Some of our customers are creating a center of excellence with AI experts and business leaders to accelerate the use of AI in the enterprise. While business users bring their most important and urgent problems, AI experts bring the algorithms & techniques to solve these problems. This closed-loop ensures that your AI application is aligned to business needs, provides a high ROI and gets needed buy-in from stakeholders. From there, you can scale.”

         -Abhijit Kakhandiki, SVP Products & Engineering, Automation Anywhere



A successful AI CoE takes a unique approach to their change management strategies. Here are a few tips on creating a successful CoE:

  • Use your CoE to deliver results, not just test pilots. This includes clearly defining initiatives, measuring baselines, and creating specific metrics and goals.
  • Use your CoE to provide leadership and administer ongoing training and reskilling across all enterprise verticals. This includes workshops, mentoring, and regular check-ins from the CoE team.
  • A solid CoE should include a technical solution architect who can vet and approve third-party applications and customize and deploy user-friendly dashboards.
  • By building a transparent AI solution to the enterprise’s most pressing issues, a corporate AI CoE establishes its worth and authority which pushes the needle in the direction of an enterprise-wide acceptance of artificial intelligence.




In terms of data, it is possible to have too much of a good thing. With the increase in computing power and the introduction of the cloud, data is flooding in faster than some enterprises know what to do with it. Doing the wrong thing with the data is just as counterproductive as doing nothing at all. The goal is to harness the artificial intelligence of your internal and external data. That’s the long-term view. The short-term view is that it is simply imperative that your best data is working efficiently and effectively for you. The most straightforward way for your data to be working efficiently and effectively for you is, yes-through Robotic Process Automation (RPA).

RPA: Delivery vehicle for AI

While RPA is not actually AI technology, it’s the major driver and enabler for AI in the process automation. Global corporate enterprise is now getting more balanced results from RPA. The organization is better prepared, integration partners have vastly more knowledge, use cases are more defined and so it has indeed been a useful first step. AI deployment efforts by themselves are still scattered and few.

RPA brings data, training and context to AI.

By automating processes using RPA, enterprises enter the next stage of automation of decision-making and judgement-oriented steps that require AI. This makes AI deployment business-focused and ROI-oriented. Hence RPA is a great driver for AI.


RPA requires structured data, but 80% of your data is unstructured. AI helps to understand unstructured data and convert it into a structured form. Automation Anywhere VP of Product Marketing Manish Rai offers an example of IQ Bot as AI technology for processing documents – the most common source of critical unstructured data. Manish shares, “We believe that people should have a choice not to read business documents. If necessary, they would receive extracted data from documents in the right format to make decisions and move on.”

READ: Unstructured Data Processing Trends


Discovering what process to automate and when to automate is the key that unlocks enterprise AI success. Manish shares: “In our vision of democratized intelligent automation, we offer an RPA + AI automation solution, which allows business users to start automating their most tedious tasks quickly and easily. After the initial success, users begin to look around for more processes to automate. At this point, process discovery capabilities become very useful, and we offer these through our platform and partner solutions.” As an example, if you manufacture toothbrushes, this is precisely the moment in time where your enterprise must decide if you should continue manufacturing toothbrushes or if your enterprise is actually more aligned to provide dental care insights.


Automation Anywhere has a global property listing client who took this approach with great success. In order to list a property, web forms must be filled out with the appropriate data. It requires a lot of repetitive, manual work. An Automation Anywhere solution, was able to take this data and populate the listings automatically.

Once that process became second nature and the enterprise was familiarized with the power of RPA, additional solutions naturally evolved out of that. The enterprise realized it could make the process even faster by feeding property photos into the AI solution that would use its computer vision to determine the property’s amenities.

They analysed 5,000 photos with an accuracy of 80%. Another 5,000 photos brought the accuracy up to 90%. By automating this tedious task, humans were able to quickly finish the process and get the listings up faster than ever before. Abhijit describes what happened next:

“What happened was these guys found that because the process cycle time was reduced by 50% and they were providing the results back much faster, they got 2x engagement from their customers, which means more customers who previewed that service signed up with them. It was directly as a result of them handling that productivity and scale and providing that faster turnaround time.”

Sometimes the only barrier to entry is fear of the unknown. By starting small and scaling from there, Automation Anywhere’ s client was able to realize the full advantages of AI.



Implementing AI across company verticals isn’t a one-and-done process. Abhijit lays this out for us like this:

“Change management is often presented as a solution when an enterprise doesn’t have a workforce with the relevant skillsets to transition into the jobs of the future.”

In other words, a corporation may have the technology, the tools, and the infrastructure to power AI. But without the human element trained to develop, supervise, and analyse the output of AI, the enterprise isn’t realizing its full value. Thus, driving AI-evolution through each person, position and process is mandatory.


Global corporate enterprise executives would love to spread a wide net to ensure total enterprise knowledge of all niche AI enterprise solutions. As time is of the essence, early movers are leaning on integration partners for insight. When working with your integration partner ensure there is an internal team to take their knowledge to the rest of the enterprise. As Abhijit puts it, “We’re training the trainers. Our executives will train business unit people and that will then spread. It’s not about reducing your FTE. It’s about realigning the focus to increase efficiency.”


“We can only see a short distance ahead, but we can see plenty there that needs to be done.”

                                                                         -Alan Turing, a founding father of AI



As noted in the introduction, by 2025, 80% of those we surveyed expect to be past the planning and proof of concept stage and onto actively leveraging AI to strengthen their business model.

We’ve identified that the best way get to that desired reality is to set a vision now. That vision allows you optimally leverage your data. Along the way, Along the way, pick low-hanging fruit with straightforward and defined use of predictive analytics.

Here are a few examples of different industries who benefit from predictive analytics:

  • Banking – Financial institutions use predictive analytics to detect and prevent fraud, to score credit, and to approve loans. The ability for a client to instantly sign up for a credit card or loan online is possible because of predictive analytics.
  • Cybersecurity – Predictive analytics can help discover data breaches far earlier than the human eye. It can also forecast the when and where of potential cybercrimes, leading to concentrated fortification and detection efforts. While there is still a long way to go in this arena, with the explosion of cybercrime, predictive analytics is shaping up to be a powerful cybersecurity tool.
  • Retail – In brick-and-mortar retail, predictive analytics assists with inventory management, pricing, and revenue forecasting. New and old customers benefit from the customized offerings predictive analytics allows, and retailers decrease customer churn and increase customer loyalty as a result.
  • eCommerce – Thanks largely to predictive analytics, eCommerce has exploded in recent years. It is predictive analytics that is behind Netflix’s recommendation engine, Amazon’s suggestions, and Facebook’s networking capabilities.
  • Healthcare – Predictive analytics in healthcare has the power to take current science and healthcare processes and expand them on a scale human alone cannot. Symptom calculators, genetic screening, and early intervention and disease prevention all benefit from predictive analytics.
  • Agriculture – Sensors in farm equipment can predict failure with astounding success rates, saving farmers the money spent on costly repairs and prevents them from losing money from downtime and ruined crops.

WATCH: Democratizing AI with External Data


To stay competitive during this technological boom, change needs to happen faster. True agility is difficult for the large enterprise that is not digitally native. According to a 2016 report by Innosight (“Corporate Longevity: Turbulence Ahead for Large Organizations “) corporations in the S&P 500 Index in 1965 stayed in the index for an average of 33 years. By 1990, average tenure in the S&P 500 had narrowed to 20 years and is now forecast to shrink to 14 years by 2026. Agile mindsets have been installed. But being agile isn’t the same thing as having vision. As discussed above, the global corporate enterprise must:

  • Harness data for the insights you want to gain
  • Transcend, collaborate and consolidate to inform your vision
  • Simultaneous short-term action leads to long-term scalability

But above all else, the global corporate enterprise must have a digital transformation vision. That vision can come into focus by answering these three questions:

  • What are you providing to your customers now?
  • How can that change with AI?
  • What does that mean for the future of what your enterprise provides and to whom it provides it? The answer to those questions provide your path for how to get from your 2020 AI reality to your 2025 AI expectations.

Read the Report Here


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