Tag: automation

Advancing from Artificial Intelligence to Humane Intelligence | Blog

I recently came across a news article that said doctors will NOT be held responsible for a wrong decision or recommendation made based on the recommendations of an artificial intelligence (AI) system. That’s shocking and disturbing at so many levels! Think of the multitude of AI-based decision making possible in banking and financial services, the public sector, and many other industries and the worrying implications wrong decisions could have on the lives of people and society.

One of the never-ending debates for AI adoption continues to be the ethicality and explainability concerns with the systems’ black box decision making. There are multiple dimensions to this issue:

  1. Definitional ambiguity – Trustworthy, fair and ethical, and repeatable – these are the different characteristics of AI systems in the context of explainability. Most enterprises cite explainability as a concern, but most don’t really know what it means or the degree to which it is required.
  2. Misplaced ownership – While they can be trained, re-trained, tested, and course corrected, no developer can guarantee bias-free or accurate decision making. So, in case of a conflict, who should be held responsible? The enterprise, the technology providers, the solution developers, or another group?
  3. Rising expectations – AI systems are being increasingly trusted with highly complex, multi-stakeholder decision-making scenarios which are contextual, subjective, open to interpretation, and require emotional intelligence.

 

Enterprises, particularly the highly regulated ones, have hit a roadblock in their AI adoption journey and scalability plans considering the consequence of wrong decisions with AI. In fact, one in every three AI use cases fail to reach a substantial scalable level due to explainability concerns.

While the issue may not be a concern for all AI-based use cases, it is usually a roadblock for scenarios with high complexity and high criticality, which lead to irrevocable decisions.

Advancing from Artificial Intelligence to Humane Intelligence

In fact, Hanna Wallach, a senior principal researcher at Microsoft Research in New York City, stated, “We cannot treat these systems as infallible and impartial black boxes. We need to understand what is going on inside of them and how they are being used.”

Progress so far

Last year, Singapore released its Model AI Governance Framework, which provides readily implementable guidance to private sector organizations seeking to deploy AI responsibly. More recently, Google released an end-to-end framework for an internal audit of AI systems. There are many other similar efforts by opponents and proponents of AI alike; however, a feasible solution is still out of sight.

Technology majors and service providers have also made meaningful investments to address the issue, including Accenture (AI fairness Toolkit), HCL (Enterprise XAI Framework), PwC (Responsible AI), and Wipro (ETHICA). Many XAI-centric niche firms that focus only on addressing the explainability conundrum, particularly for the highly regulated industries like healthcare and public sector, also exist. Ayasdi, Darwin AI, KenSci, and Kyndi deserve a mention.

The solution focus varies from enabling enterprises to compare the fairness and performance of multiple models to enabling users to set their ethicality bars. It’s interesting to note that all of these offer bolt-on solutions that enable an explanation of the decision in a human interpretable format, but they’re not embedded explainability-based AI products.

The missing link  

Considering this is an artificial form of intelligence, let’s take a step back and analyze how humans make such complex decisions:

  • Bias-free does not exist in the real world: The first thing to appreciate is that humans are not free from biases, and biases by their nature are subjective and open to interpretation.
  • Progressive decision-making approach: A key difference between humans and the machines making such decisions is the fact that even with all processes in place, humans seek help, pursue guidance in case of confusion, and discuss edge cases that are more prone to wrong decision making. Complex decision making is seldom left to one individual alone; rather, it’s a hierarchy of decision makers in play, adding knowledge on top of previous insights to build a decision tree.
  • Emotional Quotient (EQ): Humans have emotions, and even though most decisions require pragmatism, it’s the EQ in human decisions that explains the outcomes in many situations.

Advancing from Artificial Intelligence to Humane Intelligence

These are behaviors that today’s AI systems are not trained to adopt. A disproportionate focus on speed and cost has led to neglecting the human element that ensures accuracy and acceptance. And instead of addressing accuracy as a characteristic, we add another layer of complexity in the AI systems with explainability.

And even if the AI system is able to explain how and why it made a wrong decision, what good does that do anyway? Who is willing to put money in an AI system that makes wrong decisions but explains them really well? What we need is an AI system that makes the right decisions, so it does not need to explain them.

AI systems of the future need to be designed with these humane elements embedded in their nature and functionality. This may include, pointing out edge cases, “discussing” and “debating” complex cases with other experts (humans or other AI systems), embedding the element of EQ in decision making, and at times even handing a decision back to humans when it encounters a new scenario where the probability of wrong decision making is higher.

But until we get there, a practical way for organizations to address these explainability challenges is to adopt a hybrid human-in-the-loop approach. Such an approach relies on subject matter experts (SMEs), such as ethicists, data scientists, regulators, domain experts, etc. to

  • Improve learning models’ outcomes over time
  • Check for biases and discrepancies
  • Ensure compliance

In this approach, instead of relying on a large training data set to build the model, the machine learning system is built iteratively with regular inputs from experts.

Advancing from Artificial Intelligence to Humane Intelligence

In the long run, enterprises need to build a comprehensive governance structure for AI adoption and data leverage. Such a structure will have to institute explainability norms that factor in criticality of machine decisions, required expertise, and checks throughout the lifecycle of any AI implementation. Humane intelligence and not artificial intelligence systems are required in the world of the future.

We would be happy to hear your thoughts on approaches to AI and XAI. Please reach out to [email protected] for a discussion.

Is It Open Season for RPA Acquisitions? | Blog

Robotic Process Automation (RPA) is a key component of the automation ecosystem and has been a rapidly growing software product category, making it an interesting space for potential acquisitions for a while now. While acquisitions in the RPA market have been happening over the last several years, three major RPA acquisitions have taken place in quick succession over the past few months: Microsoft’s acquisition of Softomotive in May, IBM’s acquisition of WDG Automation in July, and Hyland’s acquisition of Another Monday in August.

These acquisitions highlight a broader trend in which smaller RPA vendors are being acquired by different categories of larger technology market players:

  • Big enterprise tech product vendors like Microsoft and SAP
  • Service providers such as IBM
  • Larger automation vendors like Appian, Blue Prism, and Hyland.

Recent RPA acquisitions timeline:

RPA Robotic Process Automation

Why is this happening?

The RPA product market has grown rapidly over the past few years, rising to about US$ 1.2 billion in software license revenues in 2019. The market seems to be consolidating, with some of the larger players continuing to gain market share. As in any such maturing market, mergers and acquisitions are a natural outcome. However, we see multiple factors in the current environment leading to this frenetic uptick in RPA acquisitions:

Acquirers’ perspective – In addition to RPA being a fast-growing market, new category acquirers – meaning big tech product vendors, service providers, and larger automation vendors – see potential in merging RPA capabilities with their own core products to provide more unified automation solutions. These new entrants will be able to build pre-packaged solutions combining RPA with other existing capabilities at lower cost. COVID-19 has created an urgency for broader automation in enterprises, and the ability to offer packaged solutions that provide a quick ROI can be a game-changer in this scenario. Additionally, the adverse impact of the pandemic on the RPA vendors’ revenues, which may have dropped their valuations down to more realistic levels, is making them more attractive for the acquiring parties.

Sellers’ perspective – There is now a general realization in the market that RPA alone is not going to cut it. RPA is the connective tissue, but you still need the larger services, big tech/Systems-of-Record and/or intelligent automation ecosystem to complete the picture. RPA vendors that don’t have the ability to invest in building this ecosystem will be looking to be acquired by larger players that offer some of these complementary capabilities. In addition, investor money may no longer be flowing as freely in the current environment, meaning that some RPA vendors will be looking for an exit.

What can we expect going forward?

The RPA and broader intelligent automation space will continue to evolve quickly, accelerated by the predictable rise in demand for automation and the changes brought on by the new entrants in the space. We expect to see the following trends in the short term:

  • More acquisitions – With the ongoing market consolidation, we expect more acquisitions of smaller automation players – including RPA, Intelligent Document Processing (IDP), process orchestration, Intelligent Virtual Agents (IVA), and process mining players – by the above-mentioned bigger categories as they seek to build more complete transformational solutions.
  • Services imperative – Scaling up automation initiatives is an ongoing challenge for enterprises, with questions lingering around bot license utilization and the ability to fill an automation pipeline. Services that can help overcome these challenges will become more critical and possibly even differentiating in the RPA space, whether the product vendors themselves or their partners provide them.
  • Evolution of the competitive landscape – We expect the market landscape to undergo considerable transformation:
    • In the attended RPA space, while there will be co-opetition among RPA vendors and the bigger tech players, the balance may end up being slightly tilted in favor of the big tech players. Consider, for instance, the potential impact if Microsoft were to provide attended RPA capabilities embedded with its Office products suite. Pure-play RPA vendors, however, will continue to encourage citizen development, as this can unearth low-hanging fruit that can serve as an entry point into the wider enterprise organization.
    • In the unattended RPA space, pure-play RPA vendors will likely have an advantage as they do not compete directly with big tech players and so can invest in solutions across different systems of record. Pure-play RPA vendors might focus their efforts here and form an ecosystem to link in missing components of intelligent automation to provide integrated offerings.

There are several open questions on how some of these dynamics will play out over time. You can expect a battle for the soul (and control) of automation, with implications for all stakeholders in the automation ecosystem. Questions remain:

  • How will enterprises approach automation evolution, by building internal expertise or utilizing external services?
  • How will the different approaches automation vendors are currently following play out – system of record-led versus platform versus best of breed versus packaged solutions?
  • Where will the balance between citizen-led versus centralized automation lie?

Only time will tell how this all plays out.

But in the meantime, we’d love to hear your thoughts. Please share them with us at [email protected], [email protected], and  [email protected].

How can we engage?

Please let us know how we can help you on your journey.

Contact Us

"*" indicates required fields

Please review our Privacy Notice and check the box below to consent to the use of Personal Data that you provide.