Intelligent Automation Radio

People of a certain age will fondly remember an American TV game show called "Name That Tune", where contestants won prizes for correctly identifying a song with as few notes as possible played by an orchestra.  Name That Tune offers a fitting analogy to explain Indico's value proposition - automating complex processes involving unstructured content, using as little as 200 sample documents for its AI model to learn from.  With just a fraction of what their competitors require, Indico can name that tune (automate unstructured content workflows) in very few notes (documents), using a proprietary "transfer learning" algorithm.  Since Forrester Research estimates that up to 85% of all enterprise content is unstructured, this represents a huge untapped opportunity. 

Founder/CTO Slater Victoroff is the driving force behind Indico's ambitious goal of helping organizations turn process into profit.  He joins us to discuss the path that led him to embrace transfer learning and what makes this technique ideal for automating workflows involving unstructured content of intertwined images and text.  Along the way we learn why "time on task" is a much more intuitive metric than ROI, why machine teaching is better than machine learning, and why companies are lying about how much data they actually have.

Build or Buy?  It’s a question both IT & business operations have grappled with ever since John W. Tukey first coined the term "software" in 1957.  As automation & artificial intelligence quickly permeate nearly every aspect of enterprise ops over 6 decades later, the question still remains.  Should an organization build its own automation & AI application, or let someone else do the heavy lifting? Building custom solutions allows companies to retain control, but at what expense?  Then there's ongoing maintenance costs and other considerations.  On the other hand, buying software can accelerate time to value, but that often entails making compromises on functionality.

To delve further into this question, we turn to Shan Haq, Vice President of Corporate Strategy and Development at Transcepta, an intelligent Procure-To-Pay vendor.  Transcepta’s platform leverages automation augmented by AI to deliver 100% straight-through invoice processing across supply chains. Shan shares his insights with us on why buying the services of an automation platform can make more sense, & the beneficial ripple effects enterprises can experience by automating just one area of their organization’s operations.

Who among us hasn't called a contact center at some point for customer service, tech support, or some other matter?  Usually those interactions are mundane, unremarkable, and ultimately forgettable.  You get what you need and go on with your day, blissfully unaware of what transpired behind the scenes to provide you that friction-free encounter.  It turns out however that automation & AI often play a key role in delivering those satisfying outcomes, and NICE Ltd. (formerly Neptune Intelligence Computer Engineering) is a key player enabling those experiences.

Oded Karev runs NICE's RPA division, and has a lot to say about how the attended & unattended real-time automation they’ve innovated has digitally transformed their contact center customers.   He joins us to share his insights about some interesting use cases, whether or not all the various forms of automation will eventually merge into one singular platform, and briefly talks about NICE’s next major innovation - robots building robots.

Prior to their acquisition by Oracle, Sun Microsystems operated under a memorable tagline - "The network is the computer".  Their slogan illustrated the maxim that tapping into the combined computational power of an entire network would yield far more impressive results than what a lone desktop could deliver on its own.  That principle is center stage again today, thanks to the promise of intent-based networking (IBN), which leverages AI & machine learning to automate many network processes now managed manually.  The pandemic accelerated demand for IBN-driven network automation when organizations were forced to accommodate the work from home mandate with an agile infrastructure.  Consequently, network automation has emerged as one of the most important areas of focus for enterprise digital transformation, and Cisco is one of its leading providers.

As Cisco’s Sr. VP & CTO of DevNet Ecosystem Success, Susie Wee is the executive entrusted with growing the community of developers & IT professionals whose skills will deliver the promise of network automation worldwide.  We talk with Susie to learn her motivation in founding DevNet, the skills necessary to succeed in network automation, and what IT leaders can do to create an innovation mindset that enables their teams to solve complex network automation challenges.

Failing fast is one of Agile development’s conceptual pillars. Embracing the principle of failing fast leads to lower cost of failure, accelerated learning, and innovation-driven organizational culture.  In the AI & machine learning world however, where testing & fast failing of models is heavily dependent on access to computing power, these desirable benefits are often out of reach.  If your employer is computationally affluent like Facebook or Google, your machine learning models enjoy the best environment to fail fast & get rapidly fine-tuned towards a minimum viable product.  If your employer doesn’t have those resources, then not so much.

William Falcon wants to remedy that inequality with a Training-at-Scale system that compresses research & testing from months into days.  Expediting that process not only gets machine learning models into production faster, but can also accelerate an organization’s journey towards digital transformation.  We talk with William about how his startup Grid.ai will provide this “superpower” and how doing so can provide enterprises with a competitive advantage.

The trenches of technology's marketplace battlefield are strewn with the remains of high-tech leaders, undercut by adversaries marching in with the next great innovation.  It's a manifestation of Creative Destruction, economist Joseph Schumpeter's famous theory about the continuous cycle of annihilation & creation that enables the new to replace the old.  Of course "old" is a relative term in IT, but some are beginning to view automation in its current form as a legacy encumbrance.  If it is, then what's next?

To find out we turn to Gaurav Dhillon, Chairman and CEO of SnapLogic, a leading provider of Integration Platform-as-a-Service.  Integrations are the synapses of the online world, enabling creation of neural circuits between applications, which ultimately coalesce into a computational nervous system we call the internet.  Gaurav believes that combining iPaaS with AI will redefine how enterprises interact with their employees and their customers.  The result will be organizations whose whole is greater than the sum of their parts.

There's a quote circulating on the internet that "trust takes years to build, seconds to break, and forever to repair".  Its author was almost certainly referring to human relationships, but it turns out this sentiment applies equally to AI-driven decision making.  Complex machine-learning models feeding AI algorithms can take a long time to develop.  However, if they result in people's credit applications being erroneously rejected or medical conditions being misdiagnosed, confidence in that AI will evaporate, and possibly never be restored.  That's where Explainable AI comes in.

Explainable AI seeks to transform a "black box" model into a fishbowl, so that its outputs are understandable, explainable, and ultimately trustworthy.  One leading proponent of Explainable AI is Krishna Gade, a software engineering veteran of Facebook, Pinterest, Twitter, and Microsoft who Co-founded Fiddler Labs, an Explainable AI company.  Krishna joins us to discuss how Explainable AI can address unconscious or unintentional bias, expedite debugging of AI models, and accelerate acceptance of and trust in AI-driven decision making.

As women bridge the diversity gap in the technology industry, female executives like Cognizant’s Mariesa Coughanour, have advanced their careers to prominent positions in our profession, and it’s worth taking note.

As head of Cognizant's Automation Advisory, Mariesa runs a practice for one of the world's leading professional services companies that transforms hyper automation from a buzzword into a reality. By tying together analytics, AI, and automation, Mariesa's team has generated tens of millions of dollars in savings for Cognizant clients.  She joins us on the podcast to share her insights about the key issues surrounding hyper automation and discuss implementations she’s overseen in the pharmaceutical, insurance, and airline industries.

"If you can't describe what you are doing as a process, you don't know what you're doing." That quote was coined by William Edwards Deming, the famous 20th-century management consultant, who viewed process excellence as a sine qua non to performance excellence.  When it comes to digital transformation, process excellence is very much foundational to automation excellence.  Yet surprisingly, many organizations can't fully describe what they're doing as a process, suggesting that Deming was more prescient than many realize.

One man on a mission to bring process excellence to the mid-size market is Robert Hutter, Founder and CEO of FireStart. His firm focuses on human-centric process modeling, documentation, and enterprise workflow automation, resulting in holistic digital transformation.  We chat with Robert to learn how enterprise workflow automation differs from robotic process automation, the natural limitations facing citizen developers, and why you should value process as the most relevant asset in your company.

If a parallel could be drawn between the history of Artificial Intelligence and a professional star athlete who rehabilitated his career, it might go something like this.  As a rookie, AI flashed occasional signs of brilliance that enthralled a million minds with the promise of greater possibilities.  Then it floundered, getting sent down to the minor leagues to overhaul & revamp itself.  Eventually it worked its way back up to the big leagues, and began fulfilling the expectations of greatness many had predicted.  Now that AI is delivering consistent superstar results, organizations seeking their own operational victories want to sign it to a long-term contract.  Has AI finally redeemed itself enough to gain everyone’s trust though?

That’s a topic of particular interest to Bob Friday, Vice President, CTO, and Co-Founder of Mist Systems, a Juniper Company.  As a pioneer in smart wireless networking, Bob has seen a lot in his storied Silicon Valley career.  He stops by to share with us why 2014 was a watershed year for AI, why adoption of AI is accelerating for enterprises with complex networks, and the risks for companies who don't develop an AI for IT strategy in the coming year.