At IBM Think in March of 2018, IBM aggregated all of its conferences into a megaconference held in Las Vegas and targeted and providing a large venue for IBM’s strongest thought leadership themes and public facing announcements. Amalgam notes that this conference was very successful at showing IBM’s long-term vision and perspective, but that this first-year conference had some logistical challenges that prevented attendees from capturing the full depth and breadth of IBM’s progress over the past year.
At this event, two key themes emerged: IBM’s role in creating the next generation of technologies, including AI, Blockchain, and quantum computing and also IBM Research, which was front and center to a greater extent than in past years where IBM Research scientists were often relegated to a small portion of the expo and the occasional session. At this event, IBM Research director and Senior Vice President of Hybrid Cloud Arvind Krishna and his colleagues played a much stronger role than in previous years, which was helpful in providing IBM observers and customers with a clearer view of IBM’s intentions regarding the future of IBM.
Near the beginning of this event, there were two key presentations that set the tone for this trade show: IBM’s “5 in 5” focused on five key innovations targeting massive change in the next five years as well as Ginny Rometty’s keynote.
The “5 in 5” presentation brought out the concepts of blockchain to prevent counterfeiting, lattice cryptography, AI-powered microscopes, AI bias and ethics, and quantum computing as areas where IBM has created proofs of concepts, academic frameworks, and guidance to support some of the most vital challenges of authenticity and the next generation of computing. This presentation both provided a strong vision of the key challenges of the future and how IBM is targeting these challenges from a primary research perspective. This presentation was one of the strongest proof points in the event to support the claim that IBM is taking on the biggest problems in the world.
IBM CEO Ginny Rometty took the keynote stage to formally kick off the event and proceed to posit that this moment in time was an “exponential” moment. She provided the perspective that there was an opportunity to add AI, including both guided learning and deep learning, into every process to support the enhancement of human effort and the “era of Man and Machine.” Rometty also pointed out a key differentiator from an AI perspective in that IBM’s business model was not targeted at selling user data, a perspective that may take on added importance in the recent wake of Facebook’s challenges.
Rometty also brought up three key announcements for users across IBM’s user base to consider:
IBM Cloud Development Console for Apple , which provides IBM Cloud services directly to Apple developers and is intended to expand adoption of IBM’s existing data and Ai services
Watson Studio – This product is IBM’s product for supporting data ingestion, analytic model management, and AI operationalization and should be considered as IBM’s workbench product to support data science. Based on its initial announced pricing, including free and $99/month options, this offering should be considered IBM’s competitive offering in the data science workbench space for companies seeking to expand data science from an individual to a collaborative activity.
Watson Assistant: Previously called Watson Conversation, this product will provide trained Watson-based assistants to provide a user interface, content, and recommendations
One of the challenges I found at IBM Think was that the breadth of announcements and relatively new technologies being shown at the show were not necessarily easy to find and were not always announced. The Think Campus was broken up into four areas: Business & AI, Cloud and Data, Modern Infrastructure, and Security & Resilience. However, within each area of the campus, there were a combination of products and capabilities being shown with little prioritization or showcasing of new products vs. existing or legacy products which made it hard to prioritize what was new and different.
As one example, IBM’s Cognos Analytics has gone through a major facelift and adopted much of the user interface that makes Watson Analytics valuable, including reporting, dashboard, and exploration advances that greatly improve data discovery on Cognos. The natural language interface rivals any discovery capability on the business intelligence market, yet this user experience that opens up Cognos to practically every user in the company was a relatively minor announcement with little press.
This trend was common at IBM Think, where product announcements that would normally be the centerpiece of a smaller event were relatively ignored to the point where there might have only been a single session focused on a key announcement and there was no recording or press release to summarize the announcement. If anything, IBM was being too subtle in showcasing existing product announcements in core business areas. Although industry analysts often complain about overmarketing, I actually felt that IBM Think had the opposite problem: it was not easy to find the announcements that I was most interested in from a business intelligence and machine learning perspective. And it felt as if key executives and speakers were overstretched in their ability to articulate the IBM message.
Overall, Amalgam believes that the converged IBM Think show did a good job in providing high level thought leadership, including cutting-edge education on quantum computing and a wide variety of blockchain capabilities. However, Amalgam also believes that IBM has room for improvement in a couple of areas.
First, IBM needs to take better advantage of the internal evangelists that are already in place. There are a variety of strong evangelists below the EVP and VP levels who are good at articulating their products and roles: IBM’s Research team showed this very strongly in their presentations. Providing IBM’s best evangelists with a keynote appearance would provide a big picture of how IBM excels across all of its business areas.
Second, As an industry analyst with 20+ years experience in predictive analytics across sports, chemistry, and telco, I found it difficult to find spokespeople on analytics and machine learning who could summarize all of the IBM announcements in these spaces. Recent announcements like (PAIRS Geoscope), a tool that allows developers to deeply analyze geospatial data and to augment with IoT data, such as drones, to create extremely granular maps, were hidden as one-off booths in the expo. This left me with the experience of having to hunt them down, one-by-one, in my core IBM coverage area and left little time to explore adjacent areas of cloud, DevOps, and blockchain. To expand past the core and take advantage of the broad coverage and subject matter at IBM Think, attendees first need to be able to quickly digest core topics.
Overall, Amalgam believes that IBM Think was a transitional experience for IBM in sharing its message. Although the big picture message of IBM as an enabler for disruption and technical transformation was clear, Amalgam believes there are additional opportunities in the future for IBM to clarify the customer journey by guiding attendees to relevant people, sessions, and products. A fully open-ended event at the scale of an IBM Think will always be difficult to traverse without defined tracks. With the convergence of a wide variety of systems, analytics, industries, and emerging technologies all under one roof, the need for guidance only increases. Amalgam looks forward to seeing how this event evolves for next year’s iteration in San Francisco.