The Innovation Game – How Data is Driving Digital Transformation

Technology waits for no one. And those who strike first will have an advantage. The steady decline in business profitability across multiple industries threatens to erode future investment, innovation and shareholder value. Fortunately, the emergence of artificial intelligence (AI) can help kick-start profitability. Accenture research shows that AI has the potential to boost rates of profitability by an average of 38 percent by 2035 and lead to an economic boost of US$14 trillion across 16 industries in 12 economies by 2035.

Driven by these economic forces, the age of digital transformation is in full swing. Today we can’t be “digital to the core” if we don’t leverage all new data sources – unstructured, dark data and thirty party sources. Similarly, we have to take advantage of the convergence of AI and analytics to uncover previously hidden insights. But, with the increasing use of AI, we also have to be responsible and take into account the social implications.

Finding answers to the biggest questions starts with data, and ensuring you are capitalizing on the vast data sources available within your own business. Thanks to the power of AI/machine learning and advanced algorithms, we have moved from the era of big data to the era of ALL data, and that is helping clients create a more holistic view of their customer and more operational efficiencies.

Embracing the convergence of AI and analytics is crucial to success in our digital transformation. Together,

  • AI-powered analytics unlock tremendous value from data that was previously hidden or unreachable,
  • changing the way we interact with people and technology,
  • improving the way we make decisions, and giving way to new agility and opportunities.

While businesses are still in the infancy of tapping into the vast potential of these combined technologies, now is the time to accelerate. But to thrive, we need to be pragmatic in finding the right skills and partners to guide our strategy.

Finally, whenever we envision the possibilities of AI, we should consider the responsibility that comes with it. Trust in the digital era or “responsible AI” cannot be overlooked. Explainable AI and AI transparency are critical, particularly in such areas as

  • financial services,
  • healthcare,
  • and life sciences.

The new imperative of our digital transformation is to balance intelligent technology and human ingenuity to innovate every facet of business and become a smarter enterprise.

The exponential growth of data underlying the strategic imperative of enterprise digital transformation has created new business opportunities along with tremendous challenges. Today, we see organizations of all shapes and sizes embarking on digital transformation. As uncovered in Corinium Digital’s research, the primary drivers of digital transformation are those businesses focused on addressing increasing customer expectations and implementing efficient internal processes.

Data is at the heart of this transformation and provides the fuel to generate meaningful insights. We have reached the tipping point where all businesses recognize they cannot compete in a digital age using analog-era legacy solutions and architectures. The winners in the next phase of business will be those enterprises that obtain a clear handle on the foundations of modern data management, specifically the nexus of

  • data quality,
  • cloud,
  • and artificial intelligence (AI).

While most enterprises have invested in on-premises data warehouses as the backbone of their analytic data management practices, many are shifting their new workloads to the cloud. The proliferation of new data types and sources is accelerating the development of data lakes with aspirations of gaining integrated analytics that can accelerate new business opportunities. We found in the research that over 60% of global enterprises are now investing in a hybrid, multi-cloud strategy with both data from cloud environments such as Microsoft Azure along with existing on-premises infrastructures. Hence, this hybrid, multicloud strategy will need to correlate with their investments in data analytics, and it will become imperative to manage data seamlessly across all platforms. At Paxata, our mission is to give everyone the power to intelligently profile and transform data into consumable information at the speed of thought. To empower everyone, not just technical users, to prepare their data and make it ready for analytics and decision making.

The first step in making this transition is to eliminate the bottlenecks of traditional IT-led data management practices through AI-powered automation.

Second, you need to apply modern data preparation and data quality principles and technology platforms to support both analytical and operational use cases.

Thirdly, you need a technology infrastructure that embraces the hybrid, multi-cloud world. Paxata sits right at the center stage of this new shift, helping enterprises profile and transform complex data types in highvariety, high-volume environments. As such, we’re excited about partnering with Accenture and Microsoft to accelerate businesses with our ability to deliver modern analytical and operational platforms to address today’s digital transformation requirements.

Artificial intelligence is causing two major revolutions simultaneously among developers and enterprises. These revolutions will drive the technology decisions for the next decade. Developers are massively embracing AI. As a platform company, Microsoft is focused on enabling developers to make the shift to the next app development pattern, driven by the intelligent cloud and intelligent edge.

AI is the runtime that will power the apps of the future. At the same time, enterprises are eager to adopt and integrate AI. Cloud and AI are the most requested topics in Microsoft Executive Briefing Centers. AI is changing how companies serve their customers, run their operations, and innovate.

Ultimately, every business process in every industry will be redefined in profound ways. If it used to be true that “software was eating the world,” it is now true to say that “AI is eating software”. A new competitive differentiator is emerging: how well an enterprise exploits AI to reinvent and accelerate its processes, value chain and business models. Enterprises need a strategic partner who can help them transform their organization with AI. Microsoft is emerging as a solid AI leader as it is in a unique position to address both revolutions. Our strength and differentiation lie in the combination of multiple assets:

  • Azure AI services that bring AI to every developer. Over one million developers are accessing our pre-built and customizable AI services. We have the most comprehensive solution for building bots, combined with a powerful platform for Custom AI development with Azure Machine Learning that spans the entire AI development lifecycle, and a market leading portfolio of pre-built cognitive services that can be readily attached to applications.
  • A unique cloud infrastructure including CPU, GPU, and soon FPGA, makes Azure the most reliable, scalable and fastest cloud to run AI workloads.
  • Unparalleled tools. Visual Studio, used by over 6 million developers, is the most preferred tool in the world for application development. Visual Studio and Visual Studio Code are powerful “front doors” through which to attract developers seeking to add AI to their applications.
  • Ability to add AI to the edge. We enable developers, through our tools and services, to develop an AI model and deploy that model anywhere. Through our support for ONNX – the open source representation for AI models in partnership with Facebook, Amazon, IBM and others – as well as for generic containers, we allow developers to run their models on the IoT edge and leverage the entire IoT solution from Azure.

But the competition to win enterprises is not only played in the platform battlefield, enterprises are demanding solutions. Microsoft AI solutions provide turnkey implementations for customers who want to transform their core processes with AI. Our unique combination of IP and consulting services address common scenarios such as business agents, sales intelligence or marketing intelligence. As our solutions are built on top of our compelling AI platform, unlike ourcompetitors, our customers are not locked in to any one consulting provider, they remain in full control of their data and can extend the scenarios or target new scenarios themselves or through our rich partner ecosystem.

AI Analytics

Click here to access Corinium’s White Paper

RPA – A programmatic approach to intelligent automation to scale growth, manage risk, and drive enterprise value

Business leaders and chief information officers around the world are jumping on the robotic process automation (RPA) pilot bandwagon to start their companies on the automation journey. Some RPA pilots are evaluating software designed to stitch together known technology concepts—such as screen scraping and macrobased automation—through user-friendly tools to take process automation to the next level. Other pilots are venturing into the use of machine learning and cognitive automation to unleash new business insights.

These pilots—or proof-of-concept programs—help leaders set a foundation for their understanding of RPA, while at the same time introducing new ideas for how automation can help scale operations or define new business strategies. And now the pilot was successful, and leaders are seeing the possibilities. So what happens next?

When performing RPA pilots many companies get stuck in basic automation and stop there. Other companies have basic and cognitive automation pilots going on simultaneously.

Aligning the goals of basic RPA with cognitive computing and artificial intelligence can seem improbable. But are the objectives really that different? Leaders want to use all levels of automation to

  • drive business growth,
  • manage risk,
  • and increase value.

The trick is having a strategy for getting from pilot to program, and putting in place a comprehensive structure looking beyond the RPA pilots to intelligent automation (IA) as an across-the-board investment. This ensures IA ventures become more than speculation and remain significant to the business.

  • But how can leaders ensure that IA is more than a one-time cost play?
  • How are future automation opportunities identified and evaluated for both risk and benefit?
  • How is “electronic employee” service performance monitored?
  • How do leaders ensure the optimal mix of basic, enhanced, and cognitive automation?
  • How is business continuity maintained if the IA solution fails?
  • How is
    • system security,
    • change management,
    • system processing,
    • and authentication control
  • maintained as automation risk becomes more complex?
  • How will IA be used to transform the business?

Leaders know technology is changing rapidly, and IA is a moving target. Implementing a “bullet-proof” value-based program is critical to managing the automation revolution and ensuring it delivers positive business impacts over time. Robust program management balances risk and reward with structures driving sustainable IA value. An IA program model delivers these ideals.

An Intelligent Automation program can help enhance and expedite the implementation of IA throughout an organization. Here are four critical characteristics for success:

  1. It is strategically positioned – Positioning IA on par with other business strategies as integral to enterprise objectives is the best place to start. Similar to outsourcing (OS), these dependent IA vendor relationships are treated as strategic. Global processowners (GPO) use IA to transform end-to-end services. Global teams engage in IA opportunity evaluation to ensure bad processes are not automated.
  2. It uses a “center of excellence” service model – Establishing a center of excellence (CoE) demonstrates a commitment to IA success. Focus drives effectiveness, and CoEs drive transparency to IA results. CoEs have varied formats (virtual, centralized, regional, etc.) and engage cross-functional teams. CoE governance guides IA strategy and validates results. Clarifying decision rights balances governance and operations accountabilities. Incorporating IA support roles (e.g., HR, IT Security, Internal Audit, risk) in decision-making ensures change integration is well managed.
  3. It has a robust delivery framework – Integrating technologies, toolkits, and tactics into IA program execution safeguards sustainability. Including relevant designers, IT professionals, and operations teams in testing makes sure solutions work. Socializing and managing life cycle compliance (e.g., intake, approvals, testing) ensures team interaction is clear. Program management, repository, and workflow tools makes oversight effective.
  4. It incorporates a proactive risk management structure – Making IT risk and security control oversight a part of IA development ensures solutions are sound. Like any technology integration, change control is critical to implementation success. An IT security risk and control framework provides this support. Risk mitigation strategies linking security reviews to IA validation ensures business goals and technology risks are appropriately considered.

RPA

Click here to access KPMG’s detailed RPA report

2018 AI predictions – 8 insights to shape your business strategy

  1. AI will impact employers before it impacts employment
  2. AI will come down to earth—and get to work
  3. AI will help answer the big question about data
  4. Functional specialists, not techies, will decide the AI talent race
  5. Cyberattacks will be more powerful because of AI—but so
    will cyberdefense
  6. Opening AI’s black box will become a priority
  7. Nations will spar over AI
  8. Pressure for responsible AI won’t be on tech companies alone

Key implications

1) AI will impact employers before it impacts employment

As signs grow this year that the great AI jobs disruption will be a false alarm, people are likely to more readily accept AI in the workplace and society. We may hear less about robots taking our jobs, and more about robots making our jobs (and lives) easier. That in turn may lead to a faster uptake of AI than some organizations are expecting.

2) AI will come down to earth—and get to work

Leaders don’t need to adopt AI for AI’s sake. Instead, when they look for the best solution to a business need, AI will increasingly play a role. Does the organization want to automate billing, general accounting and budgeting, and many compliance functions? How about automating parts of procurement, logistics, and customer care? AI will likely be a part of the solution, whether or not users even perceive it.

3) AI will help answer the big question about data

Those enterprises that have already addressed data governance for one application will have a head start on the next initiative. They’ll be on their way to developing best practices for effectively leveraging their data resources and working across organizational boundaries. There’s no substitute for organizations getting their internal data ready to support AI and other innovations, but there is a supplement: Vendors are increasingly taking public sources of data, organizing it into data lakes, and preparing it for AI to use.

4) Functional specialists, not techies, will decide the AI talent race

Enterprises that intend to take full advantage of AI shouldn’t just bid for the most brilliant computer scientists. If they want to get AI up and running quickly, they should move to provide functional specialists with AI literacy. Larger organizations should prioritize by determining where AI is likely to disrupt operations first and start upskilling there.

5) Cyberattacks will be more powerful because of AI—but so will cyberdefense

In other parts of the enterprise, many organizations may choose to go slow on AI, but in cybersecurity there’s no holding back: Attackers will use AI, so defenders will have to use it too. If an organization’s IT department or cybersecurity provider isn’t already using AI, it has to start thinking immediately about AI’s short- and long-term security applications. Sample use cases include distributed denial of service (DDOS) pattern recognition, prioritization of log alerts for escalation and investigation, and risk-based authentication. Since even AI-wary organizations will have to use AI for cybersecurity, cyberdefense will be many enterprises’ first experience with AI. We see this fostering familiarity with AI and willingness to use it elsewhere. A further spur to AI acceptance will come from its hunger for data: The greater AI’s presence and access to data throughout an organization, the better it can defend against cyberthreats. Some organizations are already building out on-premise and cloud-based “threat lakes,” that will enable AI capabilities.

6) Opening AI’s black box will become a priority

We expect organizations to face growing pressure from end users and regulators to deploy AI that is explainable, transparent, and provable. That may require vendors to share some secrets. It may also require users of deep learning and other advanced AI to deploy new techniques that can explain previously incomprehensible AI. Most AI can be made explainable—but at a cost. As with any other process, if every step must be documented and explained, the process becomes slower and may be more expensive. But opening black boxes will reduce certain risks and help establish stakeholder trust.

7) Nations will spar over AI

If China starts to produce leading AI developments, the West may respond. Whether it’s a “Sputnik moment” or a more gradual realization that they’re losing their lead, policymakers may feel pressure to change regulations and provide funding for AI. More countries should issue AI strategies, with implications for companies. It wouldn’t surprise us to see Europe, which is already moving to protect individuals’ data through its General Data Protection Regulation (GDPR), issue policies to foster AI in the region.

8) Pressure for responsible AI won’t be on tech companies alone

As organizations face pressure to design, build, and deploy AI systems that deserve trust and inspire it, many will establish teams and processes to look for bias in data and models and closely monitor ways malicious actors could “trick” algorithms. Governance boards for AI may also be appropriate for many enterprises.

AI PWC

Click here to access PWC’s detailed predictions report

 

What’s now and next in Analytics, AI, and Automation

Over the past few years, rapid technological advances in digitization and data and analytics have been

  • reshaping the business landscape,
  • supercharging performance
  • and enabling the emergence of new business innovations
  • and new forms of competition
  • and business disruption.

Yet progress has been uneven. While many companies struggle to harness the power of these technologies, companies that are fully leveraging the capabilities are capturing disproportionate benefits, transforming their businesses and outpacing—and occasionally disrupting—the rest.

At the same time the technology itself continues to evolve rapidly, bringing new waves of advances in

  • robotics,
  • analytics,
  • and artificial intelligence (AI),
  • and especially machine learning.

Together they amount to a step change in technical capabilities that could have profound implications for business, for the economy, and more broadly for society as a whole. Machines today increasingly match or outperform human performance in a range of work activities, including ones that require cognitive capabilities, learning, making tacit judgments, sensing emotion, and even driving—activities that used to be considered safe from automation. Adoption of these technologies could bring significant new performance and transformational benefits to companies that go beyond simply substituting labor and lead to previously unimagined breakthrough performance and outcomes. Moreover, they have the potential to boost the productivity of the global economy at a time when it is sorely needed for growth and the share of the working-age population is declining.

Yet their advent raises difficult questions about how companies can best prepare for and harness these technologies, the skills and organizational reinvention that will be required to make the most of them, and how the leaders in the private and public sector as well as workers will adapt to the impact on jobs, capability-building and the nature of work itself.

Disruption

MGI-Briefing-Note-Automation-final

The Essential CIO Guide to Artificial Intelligence

The topic of AI has reached such a fever pitch in the media with the coverage of driverless cars, conversational bots and even movies made by AI that it’s only a matter of time before every CEO starts asking their CIO “What’s our AI strategy.

For many CIOs this will be a “deer in the headlights” moment since the topic of AI is so multi-faceted it’s hard to know where to start. We put together this e-book as a primer for CIOs wanting to get to grips with the topic of AI.
We start by giving some insight and context into why your CEO is asking this question, why now, and why you. Then, we will give you a foundational framework to think about AI so you can give your CEO a thoughtful response. Finally, we will discuss how you as CIO, can engage the business on the topic of AI and important considerations when evaluating AI vendors.
So, why is the CEO asking you this now? CEOs are humans too and they react to their environment. Their environment is often dominated by other CEOs, their board, and the outside world. AI as a topic has risen to the boardroom and the popular press with even Vanity Fair recently publishing an article titled “Suddenly Everyone is Obsessed with AI.” So if your CEO hasn’t broached the AI topic yet, they soon will.ai_breakthrough