Accelerating Enterprise-wide Decision Making and Execution
As enterprise leaders look to pursue their technology and business objectives, they are increasingly pursuing BizOps. BizOps is a methodology for ensuring technology investments are optimally aligned with the delivery of business outcomes. Now, Broadcom offers an integral solution for organizations pursuing BizOps. Digital BizOps from Broadcom powered by Automation.ai combines business, development, and operations data to generate actionable insights that help customers continuously improve the business outcomes of digital initiatives. This paper provides readers with an introduction to the emerging BizOps imperative facing our customers, and it details how our Digital BizOps solution helps customers address this imperative.
By Guest Contributor, Stephen Elliot, Program Vice President, Management Software and DevOps, IDC Research
As IT landscapes become more dynamic and companies start to adopt multi-cloud environments, leadership teams are trying to manage rising complexity and tremendous amounts of organizational change. They are also under increasing pressure to drive process and tool integration to decrease operating expenses, improve product innovation, and overall deliver better business outcomes.
How are leadership teams dealing with these new challenges? Enter BizOps, a new framework for decision making that companies are adopting to accelerate digital transformation results. Many customers have already adopted DevOps and Agile, and BizOps is simply an extension that includes continuous measurement, continuous feedback, and strategic business planning to enable digital transformation at the speed of business change.
When looking at their development cycle, leadership teams are trying to drive automation and integration across their processes, and they’re also looking for continuous feedback mechanisms. This will allow an SRE, executive, or the DevOps team to have line of sight visibility into feedback being collected from customers. Leadership is also looking into driving measurement into processes and workflows to see the impact on application strategy and operational strategy, and to get a full understanding of the ultimate effect on business outcomes. Because of these changes, teams that weren’t collaborating before are increasingly working together to consolidate data pools so that they can apply automation and analytics to this data and drive a faster decision making process.
The next question is, how does this all fit into a strategic vendor partnership? Traditional business vendor engagement models are becoming more obsolete as they generally challenge the digital transformation objectives that companies are trying to achieve.
In addition, more and more teams that didn’t traditionally work together are beginning to collaborate as they use similar tools, access similar data pools, and drive towards the same business outcomes. CEOs are also trying to figure out how to take domains like continuous testing, agile, automation, security, and IT service management out of their silos and integrating their processes. They are looking at the data pools that come with each of these domains and trying to figure out what data to bring together to drive their specific business outcomes. This means that vendors need to drive a tighter collaborative model through integrated tools and through their engagement models.
That’s where Broadcom’s new vendor model steps in. Called the Broadcom Portfolio License Agreement, this new vendor model is designed to deliver an integrated set of business capabilities to drive specific, well-defined business outcomes.
Perusing the Gartner IT Symposium/Xpo™ Barcelona agenda of keynotes and presentations, as well as the list of attending vendors, I’m reminded of Jean-Baptiste Alphonse Karr’s insightful adage, “The more things change, the more they stay the same.” Used sarcastically most of the time, this axiom posits an inevitability that at its core is also an intriguing challenge. Its truth is a testament of our current digital transformation conundrum.
To be clear, I’m thoroughly excited about attending the Gartner event, and I’m a strong proponent of digital transformation, DevOps, CI/CD, agile and a string of other concepts that promise lasting IT salvation. Still, I suffer from proverbial realism syndrome: If we are to break the cycle of staying the same, something needs to change—we need a paradigm shift or some achievable breakthrough in our ability to execute.
Whether you are the purveyor at a local coffee hangout or the CTO of a large enterprise, your landscape has become digital. Your infrastructure is a virtual extrapolation of tangible metal, cloud variations, all sorts of tools, toolsets, bits, bytes, APIs and apps. Business and personal consumers alike are being herded to a digital landscape that confronts them with wonderful opportunities, glaring risks and mountains of information.
Our worlds have become a maze of technology discontinuity. We in IT know all too well the extent to which the digital landscape has become unnervingly fragmented and (dis)connected.
The constant utopian promise of untapped business value derived from digital is true, and it’s both inspiring and pressure-invoking.
Digital disruption is alive. Organizations that lack access to the technologies, people and processes needed to rapidly and efficiently bring new digital products to market will be left behind to wither away.
Operationalising digital transformation poses countless daunting challenges. A fool with a tool is still a fool. How we work and what we do with our familiar IT tools will only take us so far. The fate of IT and business stakeholders will be determined by the ability of organizations to capture, share, interpret and act on data points in an automated manner in near real-time.
How can we, the collective IT community, leap forward in our quest to support digital transformation and bring our business-stakeholder brethren along with us?
Here’s where I see our digital world heading:
Businesses are investing heavily in all things digital (money, people, technologies, attention) to the point where IT can guide business leaders to reposition entire companies, adopt new business models, enter new markets and interact with customers and partners in entirely new ways at a pace that matches demand.
One fundamental challenge remains: For the most part, IT and business stakeholders work separately. Try as we might to connect, there remains a Chasm of Disparity, where the language, metrics, goals and work processes on one side of the chasm are vastly different from those on the other side.
As organizations strive to bring new products and services to market, inputs and outputs from both sides of the chasm are catapulted back and forth, creating translation errors, timing issues, context confusion, priority mix-ups and more. While I value the infinity symbol depictions of the digital transformation journey, I’ve always wondered: Are we ready to embrace the full impact of this change? And, where is business represented? Should it not be incorporated here?
Enter Stage Left, Digital BizOps
One widely accepted definition of Digital BizOps is an automated decision-support mechanism that better connects business functions and enables the smooth operation of an organization.
The Digital BizOps promise presents opportunities to use technology to close—or at least dramatically narrow—the Chasm of Disparity. It can bring business management and IT closer together by enabling them to exchange in-context, actionable data-as-information in near real-time. Digital BizOps can make the entire ecosystem adaptable and manageable while it matures the alignment of people, processes and technology.
We’re seeing positive, active change with next-generation AI automation capabilities that drive AIOps supported by machine learning that gives organizations in-context, actionable business insights. In-context technology-supported requirements management toolsets give us insights into capabilities derived from real-time customer experience data.
These types of Digital BizOps innovations exert pressure on organizations to evolve work processes, gather new and better information to be used by an expanding cadre of stakeholders, and adapt more quickly.
We are standing at a pivot point in our journey that will usher in a new era when, to coin a new axiom for the Era of Digital BizOps, “The more things change, the more things change.”
The trick lies in our ability to translate technology output into understandable and actionable data points whilst enabling organizations with decision-making power they can act on. Digital BizOps can help us answer our questions and become enablers of people, processes, and technology within organizations.
It’s time to act by laying the foundation to succeed with innovative approaches for Digital BizOps. If you’ll be at the Gartner IT Symposium and want to continue this conversation, please find me in the crowd or email me at [email protected]. To learn more about Enterprise Studio at HCL, click here.
Parting thoughts to ponder: Enormous risks and potential grand rewards lie ahead. To avoid failure and capitalize on success, we need to answer a few questions:
Do we understand the benefits and risks of reducing the chasm between business management and IT when there is a seemingly bottomless chasm between business demand and IT capability?
Have we had an honest discussion of what digital transformation looks like for our organization?
Should we attempt to completely remove the chasm or should we focus on strategies to bridge the divide?
What outcomes do we wish to provide to customers as a result of this journey?
As we bring IT and business closer together, how will people in both camps respond? How can we help them adopt and adapt?
Given our teams’ skills, perspectives, processes and technical infrastructure, what can we do to prepare for this quest?
When conditions change and we learn we were wrong, will we be able to correct our course?
To navigate the risks, we need to work with appropriate partners that can guide us through this maze whilst understanding our needs to evolve. We need to invest today for tomorrow’s success. It starts with a solid technology strategy supported by adoption capability that bridges the Chasm of Disparity.
Across the board, IT development organizations have been adopting approaches that allow for flexible and emergent requirements, faster development, automation of processes, frequent updates, and operational excellence. Agile development processes are one key aspect of this shift. A key value of agile is continual involvement of project business sponsors, flexibility with requirements, and the emergence of a “minimum viable product” (MVP). An MVP is a product with just enough features to satisfy early customers and provide feedback for future product development, taking advantage of the continuous feedback mechanisms to incrementally enhance and improve.
More mature organizations have also adopted a DevOps approach or “development to operations” that coordinates the entire delivery chain spanning development to deployment. A key aspect of DevOps is the automation of the chain to reduce cycle time. It is this approach (among other factors) that allows organizations such as Amazon to deploy 10,000 changes per day.
Despite the advantages of Agile and DevOps approaches, many organizations also face challenges. One challenge is the difficulty in integrating architecture into the Agile approach. A second challenge is that while DevOps greatly increases the speed and efficiency of development, this often comes at the expense of strategic alignment. At a project level, organizations are deploying software faster, but at an enterprise level, they don’t know if they are deploying the right things, or just heading faster toward more debt and redundancy and less consistency and interoperability.
While the speed is important, it is the right things at the right speed — the “speed of business change” — that is critical to success in the new economy. Business operations (BizOps) is aimed at enabling the speed of business change. And while DevOps and Agile are critical components of any business or digital transformation (DX) initiative, they are not incompatible with architecture; in fact, they are better with architecture.
Earlier this year, we published BizOps: The CIO’s Guide to Multiplied Business Transformation (IDC #US44873518, February 2019), which discussed one perspective of BizOps as “a decision support mechanism for connecting business functions together to enable the smooth operation of the company.” This document will examine a different interpretation of BizOps, sometimes called “BizDevOps,” which is about extending the continuous feedback loops of Agile and DevOps to include business strategy and architecture.
Many companies have adapted to a “data-driven” approach for operational decision-making. Data can improve decisions, but it requires the right processor to get the most from it. Many people assume that processor is human. The term “data-driven” even implies that data is curated by — and summarized for — people to process.
But to fully leverage the value contained in data, companies need to bring – intelligence (AI) into their workflows and, sometimes, get us humans out of the way. We need to evolve from data- driven to AI-driven workflows.
Today’s business, IT, and development teams face unprecedented demands. In response, these teams have to gain unprecedented levels of speed, intelligence, and sophistication. Today, Broadcom offers the innovative solutions that help teams meet these new mandates. Digital BizOps from Broadcom integrates business, development, and operations data to generate actionable insights—enabling teams to continuously improve decision making and the execution of digital initiatives, so they yield enhanced business outcomes. Digital BizOps is powered by Automation.ai, the industry’s first AI-driven software intelligence platform. This paper offers a detailed look at the architectural approaches Broadcom has taken in developing Automation.ai, and the unique advantages the platform provides.
In today’s digital economy, organizations that can change faster will win. To gain the agility required, enterprise leaders are moving away from their legacy approaches and employing new BizOps methodologies. In this post, I examine how teams need an approach to successfully capitalize on BizOps, and I offer a detailed look at the capabilities delivered by the Broadcom Digital BizOps solution.
Digital Transformation and the Agility Imperative
While the phrase “digital transformation” may imply a process with a beginning, middle, and end, the reality is that digital transformation is a journey, one that will never be complete. In order to succeed in this journey, teams must be ready, willing, and able to adapt—and be able to do so quickly.
However, the reality is that traditional technologies and processes are stifling organizations’ agility. As IDC analysts report, “Digital disruption moves too rapidly and unpredictably for traditional organizational structures and models—they simply can’t adapt and respond quickly enough.” (Source: IDC, “BizOps: The CIO’s Guide to Multiplied Business Transformation,” February 2019).
Increasingly, decision makers are recognizing the need to employ new approaches in order meet their imperatives. BizOps represents one example of the novel approaches that are now being adopted. BizOps is a strategic approach to gaining alignment across the organization—including across business, IT, and software development—to continuously optimize digital business initiatives. Much as DevOps helps align software development and operations, BizOps promises to deliver the same agility and alignment across all aspects of the digital business.
The Challenge: Disparate Tools and Data Silos
If organizations are to succeed in BizOps, legacy technologies and models aren’t going to cut it. For years, various teams across business, operations, and software development have worked with their own sets of tools and technologies. These disparate tools generate a lot of valuable data—data that could inform business strategy, development priorities, and operational improvements. However, in most organizations, this data largely exists in silos. While information may be available to specific teams or individuals, there has been no way to correlate and analyze this data holistically, in the context of the digital business overall. Because of these limitations, teams have struggled in scaling their digital transformation initiatives.
Digital BizOps: Powered by automation.ai
Today, Broadcom offers enterprises a way to break through traditional organizational silos to maximize trust, alignment, and transparency across the entire digital business. Broadcom’s Digital BizOps solution delivers IT business management, DevOps, and AIOps capabilities in a single solution powered by an AI-driven software intelligence platform that enables a unified, enterprise-wide approach to data (See our blog post on automation.ai for more information on the engine and the key requirements it addresses.)
Broadcom’s Digital BizOps solution leverages AI and machine learning to analyze, correlate, and connect business and IT data across domains, establishing an unprecedented degree of transparency and visibility across the organization. With this solution, enterprises can boost agility, optimize operational efficiency, and foster increased trust and collaboration across the organization.
Digital BizOps Solutions
Digital BizOps delivers complete solutions that enable teams to improve operations, service levels, software, and business outcomes. The Digital BizOps solution suite features these offerings:
AIOps. This solution delivers AI and machine learning, automation, and comprehensive ecosystem observability. With this solution, teams can optimize service levels, operations, and business performance.
SecOps. Our AI-driven SecOps solution delivers resilient, scalable authentication and authorization, so you can manage hybrid cloud complexity while controlling cybersecurity risk. Our SecOps solution delivers AI and machine learning to fuel intelligent threat detection, response, and mitigation—enabling teams to stay in front of their rapidly changing environments, vulnerabilities, and threats. The solution can ingest threat analytics and other intelligence from across the enterprise, and correlate this information with applications and users.
DevOps. DevOps, Agile, and continuous delivery promise to speed development velocity. However, for too many organizations, the reality is that testing practices remain stuck in the past. With our AI-driven DevOps solution, you can establish testing at every phase of the software development lifecycle, so you can reduce defects, speed delivery, and improve quality.
ValueOps. Our AI-driven Agile business management solution helps you define your objectives, outcomes, and development plans and gives early visibility into whether you are achieving your desired KPIs and outcomes. In real time, our solution aggregates and connects intelligence on investments, people, teams, work, and processes from across the organization, so you can improve the flow and predictability of work.
Today, agility is an imperative for digital business success. Agility must be driven by unwavering purpose, one that is aligned with the needs of customers and the goals of the business. Agility with purpose is what enables businesses to proactively navigate shifting market dynamics, rather than waiting to respond. With the Digital BizOps solution, your organization can harness the holistic information that’s needed to innovate rapidly, optimize business operations, and continuously align work to strategy. In short, you can make your enterprise a digital business that scales. To learn more, be sure to visit www.broadcom.com/bizops.
As enterprise leaders look to pursue their IT and business objectives, they are being stymied by their organizations’ legacy tools and data sets. While these legacy approaches worked in years past, they’ve become untenable. These tools create data silos that present increasingly significant barriers to progress, inhibiting collaboration, insights, and innovation. Now, Broadcom offers a solution that eliminates these obstacles. automation.ai is an AI-driven software intelligence platform that enables a unified, enterprise-wide approach to data. In this post, I offer an introduction to automation.ai, detailing the key requirements the platform addresses.
Introduction: Isolated Data Sets Stifling Transformation
In the digital enterprise, data is the fuel that powers success. The ability to harness data and use it to establish innovative offerings and optimized operations is what will separate winning enterprises from the rest.
However, in today’s enterprises, the challenge isn’t a lack of data, it’s the inability to make it actionable and accessible to those who need it. Over the years, individual teams have employed their own tools, leaving the data generated tied up in isolated repositories.
While these team-specific tools serve a purpose within the group, they present significant obstacles for enterprises looking to pursue digital transformation. These disjointed data sets impede intradepartmental communications and collaboration. Further, they fundamentally limit the value that can be gleaned from the data being captured.
automation.ai: Delivering a Unified Approach for Enterprise-wide Intelligence
To succeed, today’s enterprises need a unified approach to data. automation.ai is the software intelligence platform that provides a set of open-source-based, machine-learning algorithms and a common data lake. With these capabilities, the platform enables Broadcom and its partners to deliver intelligent applications and services. As an example, this platform powers Broadcom Digital BizOps solutions leveraging AI and machine learning to analyze, correlate, and connect business and IT data across domains, creating an unprecedented degree of transparency and visibility across the organization.
The elements of a Digital BizOps platform implementation can be represented by a comparison to Apple’s smartphone ecosystem:
Operating system. automation.ai is a software intelligence platform, which can be thought of as the equivalent of the iOS operating system. Through automation.ai, teams can establish an platform that users from across the organization can employ to access the data they need. automation.ai enables organizations to establish a unified data lake that aggregates and correlates data from across the organization. The platform can apply intelligent capabilities, including automation, AI, machine learning, and more to fully leverage the data available.
Device. The Digital BizOps solution is analogous to the iPhone. Powered by the automation.ai platform, the Digital BizOps is a number of robust applications delivered to users in a centralized fashion. Like all devices that use the iOS platform, all the applications delivered through the Digital BizOps platform will provide users with a consistent interface, workflows, and more. (See our blog post on the Digital BizOps solution for more information on the platform and its capabilities.)
Applications. Apple customers can choose from an extensive selection of applications, including those from Apple and an array of partners. Similarly, the Digital BizOps platform delivers a comprehensive set of applications, including those from Broadcom and, moving forward, from third-party partners.
The Requirements—and How automation.ai Addresses Them
automation.ai addresses all the core requirements today’s organizations need in a software intelligence platform. In the following sections, we examine each of these requirements.
Alignment with modern hybrid cloud realities
Today, your environments are characterized by a mix of the legacy and the modern, distributed and mainframe, on-premises and third-party hosted, private cloud and public cloud. Tomorrow, it’s a safe bet that your environments will only be more diverse. By offering a secure, hybrid cloud deployment, automation.ai gives you greater flexibility and more data deployment options to support your entire digital delivery chain, from mobile to mainframe, and all points in between.
AI and machine learning
To operate with the speed and intelligence required, AI and machine learning are essential. With its advanced AI and machine learning algorithms, automation.ai enables teams to leverage comprehensive data sets to better track, understand, and predict changes, both in IT operations and the business. Our algorithms have been deployed in a range of industries, and proven to scale to support the largest enterprises.
To establish operational efficiency while keeping pace with rapidly changing IT and business environments, teams need to establish end-to-end automation across complex workflows and multiple domains. automation.ai delivers these intelligent automation capabilities. automation.ai can automatically trigger proactive execution of remediation scripts, and the automated updating of tickets to reflect the steps taken—all before users ever notice there’s an issue. By coupling intelligent automation with AI and machine learning, automation.ai enables teams to begin to establish self-healing environments.
Delivering optimized user experiences is an imperative, and so too is the need to ensure maximum security of sensitive assets and interactions. That’s why automation.ai delivers advanced security capabilities and why the platform itself is hardened, offering robust defenses and access controls. The platform delivers AI and machine learning to fuel intelligent threat detection, response, and mitigation—enabling teams to stay in front of their rapidly changing environments, vulnerabilities, and threats.
The platform enables teams to establish security that is aligned with DevSecOps environments, promoting security across the entire software development lifecycle. With automation.ai, teams can deliver great digital experiences, while minimizing the potential for data breaches and other security incidents. The platform can ingest threat analytics and other data from across the enterprise, and correlate this intelligence with applications and users.
Open, extensible architecture and comprehensive ecosystem
Today, teams need a software intelligence platform that offers the broadest implementation flexibility, providing deep integration with a suite of solutions and extensive options for interoperability with third-party technologies. automation.ai comprehensively addresses these requirements. The platform offers the flexibility of open source technologies, and provides extensibility to both enable and accommodate innovative new applications, technologies, and business models.
Our platform is built to scale, featuring an open architecture composed of Elasticsearch, Kafka, and Apache Spark Machine Learning. automation.ai correlates and analyzes the industry’s most comprehensive data sets, leveraging data on topology, network flow, user journeys, transactions, time-series, and more.
Moving forward, the platform will enable organizations to maximize the value of their investments by extending the platform in a number of ways. For example, teams will be able leverage their own home-grown machine learning algorithms within the platform. automation.ai also offers a unique extension mechanism that enables teams to include custom software components, including machine learning libraries, which can efficiently be packaged as Docker containers and deployed to a Kubernetes OpenShift cluster.
Find Out How You Can Eliminate the Obstacles Posed by Data Silos
Isolated data sets represent a fundamental obstacle for today’s enterprises. These silos restrict insights, inhibit collaboration, create inefficiency, and fundamentally stifle innovation. With automation.ai, enterprises can harness a software intelligence platform that eliminates data silos and all the obstacles they present. With automation.ai, your enterprise can gain the intelligence and agility you need to adapt and innovate as fast as your markets require. To learn more, be sure to visit automation.ai.
Data science and machine learning have tremendous potential business impact. They’re also rapidly becoming commodified table stakes.
So how do you outperform competitors who are embracing the same principles of machine learning and algorithm-driven decision making as you?
The answer isn’t just more or better data science. To get the most value from algorithms and data, you have to situate great data science in the tightest, most nimble outcome-driven OODA loops you can build.
The OODA model is certainly applicable to artificial intelligence (AI)-enabled business. In this case, “observe” can be understood as the intake of data. Our algorithms then “orient” by making sense of our broad, chaotic data observations. This algorithmic product then allows our systems to automatically “decide” and “act” (although, for a variety of reasons, we often retain human engagement in these two phases).
Of course, unlike the military originals, our AI OODA loops are driven by business outcomes: customer conversion and retention, sales margins, supply-chain efficiency, return on capital, etc. We continuously evaluate our AI implementations to see if they’re delivering on their value promises. We keep recalibrating our algorithms and data inputs to optimize our business KPIs. And we try our best to respond to the ever-changing demands of the market.
The Rise Of The Non-Deterministic Application
While similar in some ways to the agile, DevOps and continuous delivery disciplines our organizations have recently come to embrace, the AI OODA loop is also substantively different. In both cases, we’re attempting to improve the speed, accuracy and efficiency with which we get feedback from the real world and use that feedback to improve our organizations’ digital behaviors.
But with conventional applications, we know what the code does. So when we have a specific new functional requirement to fulfill, we simplify modify that code as appropriate. There are certainly challenges associated with writing that code properly and making sure we don’t accidentally break anything else, but the behavior of that code is ultimately deterministic.
AI doesn’t work like that. Its inner workings are non-deterministic — constantly and autonomously reconfiguring themselves in response to new inputs. So instead of changing and testing procedural code, we have to keep monitoring outcomes and then forensically relate those outcomes back to algorithms, data inputs and other application parameters.
AI Ops: New Rules For New Loops
We’re all discovering how to keep getting the most out of our non-deterministic AI applications over time. Emerging best practices include:
• Define and measure your loop processes. One great lesson from DevOps is that a well-defined process with well-defined metrics is much easier to manage, troubleshoot and improve than unstructured ad hoc team behaviors. So start mapping out your AI processes and selecting some initial metrics for what will eventually become your “AI Ops.”
• Close working relationships between data science staff and business domain knowledge leaders. AI is a team sport. Advancements in algorithmic technology are coming so quickly and are of such great potential value that we can’t expect our data science experts to also be domain masters. They therefore need to be paired with people who really understand our business, our markets and our customers.
• Build outcome guardrails and bias tests. Good AI governance isn’t just about incrementally improving business key performance indicators over time. It’s also about avoiding potential autonomic disasters and the unintentional systemization of bad ethics. Make sure your process continuously addresses these types of issues as well.
The continuous optimization of non-deterministic AI applications is new to all of us. But it’s something we all will need to do extremely well — because there’s a very high-stakes battle going on across virtually all of our markets. And when you’re in a tough fight, your OODA loop can make all the difference.
There’s a lot of hype around artificial intelligence (AI) — and for good reason. With the right algorithms, businesses can quickly pinpoint and act on patterns, clusters, trends and anomalies in customer behavior, supply chains, internal operations and market dynamics. But as the core methodologies of data science become more widely adopted, AI algorithms become less of a competitive differentiator.
In fact, the availability of machine learning, natural language processing and other algorithmic methods on-demand via the cloud and off-the-shelf/plug-and-play models — many of which are open source or free — is likely to accelerate the commodification of AI in the near future, essentially making it table stakes.
So, the question facing business strategists will soon shift from, “How can we quickly adopt AI to beat the competition?” to “Given the fact that our competitors are also using AI, how do we use AI most effectively as part of a broader strategy to build a business with a sustainable moat around it?”
AI And The Enterprise-Specific Digital Value Chain
To answer this increasingly important question, we first have to recognize that while AI is a great catalyst for value, it does not itself deliver value. Instead, it is a powerful potential enhancement to an organization’s unique end-to-end digital value chain.
The potential value of AI is thus only realized to the extent that it fulfills its role in that particular value chain. And that role can be defined in three dimensions:
• Technical architecture. From a technical perspective, AI and other algorithmic resources are “systems of intelligence” that sit between systems of record and systems of engagement. Systems of record include internal databases and transaction processing, as well as a growing number of external systems, such as third-party databases, social media feeds and telemetry from customers’ or partners’ internet of things (IoT) devices. Getting the right data is critical, as data can be biased and may create violation of privacy laws if not captured properly.
Capturing and analyzing data from these diverse back-end sources, AI systems of intelligence deliver actionable outputs to systems of engagement. These are typically customer-facing mobile apps, IoT devices and cloud-based services. However, we might also call these “systems of interaction,” since they also sometimes face supply chains or internal constituencies, rather than customers.
• Business architecture. AI initiatives also have a specific footprint within businesses. We tend to think about AI narrowly in terms of data science, but there are multiple stakeholders in these initiatives, including primary stakeholders (whether that’s sales, marketing, product management, etc.), executives with profit and loss responsibility, corporate legal and compliance leaders and managers of the IT systems AI touches.
Every business structures collaboration by these stakeholders differently. And these collaborative structures tend to evolve as AI moves from early pilots to initial deployments in production to broader implementation across the enterprise.
• Operational management. All AI is not created equal. Different classes of algorithms are appropriate for different types of cluster, trend and anomaly detection. The speed with which results are returned can be affected by many factors, including compute, data input/output and data types. User acceptance and uptake can be dramatically affected by presentation UIs (user interfaces).
These operational parameters are non-trivial and must be attentively managed to ensure optimal business outcomes. This attentiveness is especially important because, as I noted in an earlier article, systems of intelligence running self-learning algorithms are non-deterministic and, therefore, exhibit far less predictable behaviors than traditional deterministic applications.
Sustainable Advantages In AI Execution
The three dimensions of AI execution above give us one clue as to how we can achieve sustainable advantages in a market where algorithms have become commodities. Consistently superior execution leads to consistently better results, just as in every other business discipline.
Every business sells. But some have developed an empirically superior sales culture and process that empowers them to consistently win accounts, especially in target markets. Every business tries to develop products that customers will love. But some have embraced research and design processes that enable them to more consistently produce winners.
The same is true of AI. If you build a technical architecture engineered to ensure data quality, safeguard privacy and eliminate bias in data, your systems of intelligence will drive more value than competitors who don’t. Similarly, if you build a business architecture that keeps your business SMEs (subject matter experts) closely involved with your data scientists, your systems of intelligence will drive more value than competitors who don’t.
And if you build operational management processes that keep the delivery of AI outcomes fast, reliable and easy to consume — even at scale — your systems of intelligence will drive more value than competitors who don’t.
Execution, process, architecture and culture all matter. Get them right, and your company can out-perform its competitors even in a world of commodified AI. But there are still other ways you can leverage AI to create a classic moat around your business — I’ll address those in part two of this series on AI differentiation.
Ashok Reddy, Senior Vice President and General Manager for the Enterprise Software Division at Broadcom shares how today’s hyperconnected businesses demand a new approach to networking – one that is AI-driven and self-healing – even in complex multi-cloud environments. In this video, Ashok shares how Broadcom is executing on a vision of connecting everything, and how self healing networks are the first stop on the journey to power the hyperconnected enterprise.
Every Global 1000 enterprise has the ambition to transform to become a digital business. But while a majority of companies are pursuing digital ambitions, few are successful.
According to the 2018 Gartner Digital Business Survey, only 17% of enterprises have managed to reach the scaling stage of digital transformation. Most digital initiatives cannot scale in an Agile environment–Agile is the ability to create and respond to change, a way of dealing with, and ultimately succeeding in, an uncertain and turbulent business and markets.
To succeed and scale their digital business initiatives, industry analysts and technology leaders agree that CIOs need a sustainable, scalable digital business technology platform. (A platform serves or enables other products or services). A digital business technology platform is necessary to deliver and support the new digital products, customer experiences, and business models in a dynamic business and technology environment.
Beyond delivering products and experiences, the digital platform has to leverage and progressively integrate the mix of legacy IT systems and tools, with modern technologies like AI and Machine Learning, 5G networks leveraging open source software in multi-cloud hybrid environments to deliver world class customer experiences while addressing increasing regulatory compliance requirements and cyber security threats and privacy issues.
If that sounds complex, that’s because it is. Further complicating the matter for CIOs is that a sustainable digital business technology platform is not something that can be simply bought. Furthermore how does one differentiate from competition when everyone adopts the same public clouds and takes an AI First approach built on mathematical algorithms that are in the public domain?
The key to success is the deep knowledge of a specific business and industry domain that is built-into the digital platform. By specifically tailoring the platform to your unique business model creates lasting differentiation. The digital platform itself becomes your primary vehicle for delivering value.
Broadcom, as a global technology leader that designs, develops and supplies semiconductor and infrastructure software solutions is unique position to offer many of the technologies necessary to enable digital business platforms.
We have found it helpful to think of a digital business platform as consisting of several technology building blocks – sort of like layers in a wedding cake. Each layer corresponds to a desired business outcome, delivering upon which can cut across organizational boundaries. Success in digital business means that everything IT does must be to deliver a desired business outcome of their CEO or LOB – the digital business platform provides the visibility and capabilities to ensure this alignment between IT and business outcomes by providing a collection of business and technology capabilities that other products or services consume to deliver their own business capabilities and also enable associated business ecosystems by exposing to partners with in those ecosystems via APIs.
Security and Privacy is an assumed component of digital success, because protecting from cyber threats, customers data and meeting regulatory compliance is an essential component of creating digital trust for any digital business.
Connect and integrate traditional and digital business and technologies. Established organizations cannot simply abandon their existing businesses, nor can they do a wholesale upgrade of their technology systems. With the right digital platform, these organizations can connect both digital and non-digital business and use the combination as a competitive advantage.
Value stream management ensures your business strategy is connected to your execution in agile software development environment teams, so that more investments can be shifted towards digital innovation— business owners and product managers/owners, CIO’s, developers & QA, and operations managers can get a holistic view into business planning and KPI’s & analytics, helping them collaborate effectively to reduce waste and focus on only work that delivers value to the customer and the business
Innovation and Agility at scale is key to succeeding at digital because you need to be able to respond rapidly to shifting market demands or competition. Business agility for an enterprise, is speed without compromising quality, security, privacy and be compliant with regulatory requirements. Your digital platform should enable development teams to rapidly innovate, and just as rapidly change direction.
Transforming Customer Experience is the top layer of the cake for a digital business platform, as organizations need to have an obsessive focus on delivering compelling customer experiences. A digital platform must ensure consistent experience and performance in a omni-channel environment with continuous feedback loops and proactive data driven, AI based automation.
The most important component of a digital platform is an AI-driven automation engine, an intelligent system that enables the flow of data across all these layers, which is basically across an enterprise’s infrastructure- from apps to IOT devices, whether it’s generated by employees or users, to back-end multi cloud systems and services, where it’s translated into strategic insight and business decisions, which are then automated for self remediation with proactive actions.
At Broadcom, we refer to this AI-driven engine as automation.ai, which powers the entirety of your digital business technology platform. Automation.ai is open, built on world’s leading open source technologies used in enterprises such as Elasticsearch, Apache Spark Machine Learning, and algorithms developed and trained using extensive digital business data from the world’s largest enterprises.
Automation.ai is already at work in Broadcom’s AIOps and Mainframe Operational Intelligence solutions We began our journey towards automation.ai through the pioneering launch of our AIOps platform which ingests and stores a wide range of data sets and data types, including topological data, alarm metrics, log files, configuration management databases and more. These different, disparate data sets are normalized and correlated using our patented machine learning algorithms. Through this correlation, your teams can intelligently identify the true cause of issues and automatically remediate them, rather than simply tracking symptoms.
We are now extending this to other aspects of the digital business (business planning to agile management, continuous testing, devops lifecycle to IT operations combined with security and privacy)
Bookmark our automation.ai site and stay tuned for updates to come in the next few weeks.