When it comes to facilities, IT, staffing, and supply chain, businesses today need a whole new kind of blueprint to thrive in the new era of uncertainty.

Discover the five ways to help prepare for whatever is thrown your way while still meeting your desired business outcomes.

Workforce Experience

The whole purpose of gathering people in offices is so they can connect and interact; replicating this can be challenging when your workforce is distributed across multiple locations. Connectedness is about more than ongoing operations; it’s also about getting and keeping the best people.

IT Optimization

If the old style of blueprint was dogmatic, with a top-down structure offering little room to manoeuver, the new style of blueprint must embrace change and put people at the center. Give your staff the equipment they need to let them use the tools they’re already comfortable with, whether that’s a laptop, a tablet, or workstation.


Nothing is more important than making sure that business can keep going whatever challenges arise. Today, enterprise IT’s mission has emerged not just as a vendor of services to internal corporate clients but as a full strategic partner in keeping the business running no matter where workers or customers may be.

Security Fortification

Whatever your security blueprint is, ensure it acknowledges the very real threats that are already occurring and likely to become more intense. That means having endpoints with built-in protection against the biggest threats and ensuring you have a mechanism to keep those endpoints up to date wherever they may be.

Sustainable Impact

Ensuring that technology is used in a way that aligns with your Environmental, Social, and Governance (ESG) goals is crucial for protecting the planet and creating sustainable working practices. By actively seeking out solutions that reduce your carbon footprint and promote environmentally responsible practices, organizations can make a positive impact on the world. Read the full guide from HP here.

To find out more about HP’s Workforce Solutions click here. To read more about HP’s Sustainable Impact click here. Discover more on HP’s Workforce Experience here. Learn about HP’s Workforce Security Solutions by visiting here. And find out more about HP’s Managed Device Services here.

Remote Work

In recent months, artificial intelligence has been everyone’s favorite buzzword. Both Silicon Valley startups and Fortune 500 companies see industries revolutionize as AI steadily picks up pace. But excitement, progress, and red flags like AI washing, are developing in equal measure. Some businesses, desperate to get on the gravy train, want to cash in on the hype, so they overstate their AI capabilities despite the fact that, in reality, the AI they employ is minimal or nonexistent.

This questionable marketing strategy can help them receive larger seed, A, and B funding rounds compared to non-AI startups. Last year alone, AI startups raised more than $50 billion in venture capital funding, according to GlobalData, and the numbers are expected to grow this year given the frenzy surrounding ChatGPT and others. 

Given the capital poured into these startups, the AI washing phenomenon will only grow in intensity. The US Federal Trade Commission is fully aware of the danger, and warns vendors to be transparent and honest when advertising their AI capabilities. 

“Some products with AI claims might not even work as advertised in the first place,” attorney Michael Atleson, FTC division of advertising practices, wrote in a blog post. “In some cases, this lack of efficacy may exist regardless of what other harm the products might cause. Marketers should know that — for FTC enforcement purposes — false or unsubstantiated claims about a product’s efficacy are our bread and butter.”

In this complex landscape, it can be difficult to distinguish between legitimate AI solutions and marketing gimmicks.

“Companies need to apply a healthy dose of skepticism when faced with vendor claims about their AI products,” says Beena Ammanath, executive director of the Deloitte Global AI Institute. “As with anything, if it sounds too good to be true, it very likely is.”

If CIOs and their companies don’t find the correct answers, they can face consequences that include failed or late projects, financial losses, legal cases, reputational risk, and, ultimately, getting fired, says Donald Welch, CIO at New York University. “I’ve seen executives fired, and I can’t say it was the wrong decision.”

Fortunately, there are several strategies they can use to avoid mistakes.

AI-powered businesses need skilled employees

Vetting businesses that claim to use AI can be a long and time-consuming process. However, simple things, such as performing a LinkedIn search, could uncover valuable insights into an organization’s profile.

“Examine the level of AI experience and education that the vendors’ employees have,” says Ammanath. “Companies that are developing AI solutions should have the talent to do so, meaning they have data scientists and data engineers with deep experience in AI, machine learning, algorithm development, and more.”

In addition to examining employees, CIOs could also look for evidence of collaboration with external AI experts and research institutions. This category includes partnerships with universities, participation in industry conferences and events, and contributions to open-source AI initiatives.

It’s also a good sign if that vendor has experience with similar projects or applications since it shows it can deliver quality results.

“Carefully check the history of the supplier,” says Vira Tkachenko, chief technology and innovation officer at Ukrainian-American startup MacPaw. “If a company is an AI expert, it most likely has a history of research papers in this field or other AI products.”

Look for a well-crafted data strategy

Companies that truly integrate AI into their products also need a well thought out data strategy because AI algorithms need it. They need to work with high-quality data, and the more generous and relevant that data is, the better the results will be. 

“AI systems are fueled by very large amounts of data, so these companies should also have a well-constructed data strategy and be able to explain how much data is being collected and from which sources,” Ammanath says.

Another thing to look at is whether these companies put enough effort into complying with regulatory requirements, and maintain high data privacy and security standards. With the rise of data privacy regulations such as the General Data Protection Regulation (EU GDPR) and the California Consumer Privacy Act (CCPA), organizations have to be transparent about their data practices and provide individuals with control over their personal data. If this doesn’t happen, it should be a red flag.

Request evidence to back the claims

While buzzwords can be seductive, it helps to gently ask for evidence. “Asking the right questions and demanding proof of product claims is critically important to peel away the marketing and sales-speak to determine if a product is truly powered by AI,” Ammanath says.

CIOs who evaluate a specific product or service that appears to be AI-powered can ask how the model was trained, what algorithms were used, and how the AI system will adapt to new data.

“You should ask the vendor what libraries or AI models they use,” says Tkachenko. “They may have just everything built on a simple OpenAI API call.”

Matthias Roeser, partner and global leader of technology at management and technology consulting firm BearingPoint, agrees. He adds that components and framework should be thoroughly understood, and the assessment should include “ethics, biases, feasibility, intellectual property, and sustainability.”

This inquiry could help CIOs learn more about the true capabilities and the limitations of that product, thereby helping them decide whether to purchase it or not. 

Pay attention to startups

Startups position themselves at the forefront of innovation. However, while many of them push the boundaries of what’s possible in the field of AI, some may simply exaggerate their capabilities to gain attention and money.

“As a CTO of a machine learning company myself, I often encounter cases of AI washing, especially in the startup community,” says Vlad Pranskevičius, co-founder and CTO of Ukrainian-American startup Claid.ai by Let’s Enhance. He noticed, though, that recently the situation has become more acute, adding that this phenomenon is especially dangerous during hype cycles like the one currently being experienced, as AI is perceived as a new gold rush.

Pranskevičius believes, though, that AI washing will be kept in check in the near future as regulations around AI become more stringent.

Build a tech professional reputation

It’s not uncommon for a company to acquire dubious AI solutions, and in such situations, the CIO may not necessarily be at fault. It could be “a symptom of poor company leadership,” says Welch. “The business falls for marketing hype and overrules the IT team, which is left to pick up the pieces.”

To prevent moments like these, organizations need to foster a collaborative culture in which the opinion of tech professionals is valued and their arguments are listed thoroughly. 

At the same time, CIOs and tech teams should build their reputation within the company so their opinion is more easily incorporated into decision-making processes. To achieve that, they should demonstrate expertise, professionalism, and soft skills.

“I don’t feel there’s a problem with detecting AI washing for the CIO,” says Max Kovtun, chief innovation officer at Sigma Software Group. “The bigger problem might be the push from business stakeholders or entrepreneurs to use AI in any form because they want to look innovative and cutting edge. So the right question would be how not to become an AI washer under the pressure of entrepreneurship.”

Go beyond the buzzwords

When comparing products and services, it’s essential to evaluate them with an open mind, looking at their attributes thoroughly. 

“If the only advantage a product or service has for you is AI, you should think carefully before subscribing,” Tkachenko says. “It’s better to study its value proposition and features and only start cooperation when you understand the program’s benefits beyond AI.”

Welch agrees: “Am I going to buy a system because they wrote it in C, C++, or Java?” he asks. “I might want to understand that as part of my due diligence on whether they’re going to be able to maintain the code, company viability, and things like that.”

Doing a thorough evaluation may help organizations determine whether the product or service they plan on purchasing aligns with their objectives and has the potential to provide the expected results. 

“The more complex the technology, the harder it is for non-specialists to understand it to the extent it enables you to verify that the application of that technology is correct and makes sense,” Kovtun says. “If you’ve decided to utilize AI tech for your company, you better onboard knowledgeable specialists with experience in the AI domain. Otherwise, your efforts might not result in the benefits you expect to receive.”

Follow AI-related news

Being up to date on AI-related products and the issues surrounding them can help CIOs make informed decisions as well. This way, they can identify potential mistakes they could make and, at the same time, leverage new ideas and technologies.

“I don’t think there’s enough education yet,” says Art Thompson, CIO at the City of Detroit. 

He recommends CIOs do enough research to avoid falling into a trap with new or experimental technology that promises more than it can deliver. If that happens, “the amount of time to rebid and sort out replacing a product can really harm staff from being able to get behind any change,” he says. “Not to mention the difficulty in people investing time to learn new technologies.”

In addition, being informed on the latest AI-related matters can help CIOs anticipate regulatory changes and emerging industry standards, which can help them be compliant and maintain a competitive edge.

And it’s more than just the CIO who needs to stay up to date. “Educate your team or hire experts to add the relevant capabilities to your portfolio,” says BearingPoint’s Roeser.

Additional regulatory action around AI

New regulations on the way could simplify the task of CIOs seeking to determine whether a product or service employs real AI technology or not. The White House recently issued an AI Bill of Rights with guidelines for designing AI systems responsibly. And more regulations might be issued in the coming years.

“The premise behind these actions is to protect consumer rights and humans from potential harm from technology,” Ammanath says. “We need to anticipate the potential negative impacts of technology in order to mitigate risks.”

Ethics shouldn’t be an afterthought

Corporations tend to influence the discourse on new technology, highlighting the potential benefits while often downplaying the potential negative consequences.

“When a technology becomes a buzzword, we tend to lose focus on the potentially harmful impacts it can have in society,” says Philip Di Salvo, a post-doctoral researcher at the University of St. Gallen in Switzerland. “Research shows that corporations are driving the discourse around AI, and that techno-deterministic arguments are still dominant.”

This belief that tech is the main driving force behind social and cultural change can obscure discussions around ethical and political implications in favor of more marketing-oriented arguments. As Di Salvo puts it, this creates “a form of argumentative fog that makes these technologies and their producers even more obscure and non-accountable.”

To address this, he says there’s a crucial challenge to communicate to the public what AI actually isn’t and what it can’t do.

“Most AI applications we see today — including ChatGPT — are basically constructed around the application of statistics and data analysis at scale,” says Di Salvo. “This may sound like a boring definition, but it helps to avoid any misrepresentation of what ‘intelligent’ refers to in the ‘artificial intelligence’ definition. We need to focus on real problems such as biases, social sorting, and other issues, not hypothetical, speculative long-terminist scenarios.”

Artificial Intelligence, CIO, IT Leadership, Vendor Management, Vendors and Providers

Generative artificial intelligence (GenAI) tools such as Azure OpenAI have been drawing attention in recent months, and there is widespread consensus that these technologies can significantly transform the retail industry. The most well-known GenAI application is ChatGPT, an AI agent that can generate a human-like conversational response to a query. Other well-known GenAI applications can generate narrative text to summarize or query large volumes of data, generate images and video in response to descriptive phrases, or even generate complex code based on natural language questions.

GenAI technologies offer significant potential benefits for retail organizations, including speedy price adjustments, customized behavior-based incentives, and personalized recommendations in response to searches and customer preferences. These technologies can create new written, visual, and auditory content based on natural language prompts or existing data. Their advanced analytic capabilities can help determine better locations for new stores or where to target new investments. Generative AI chatbots can provide faster, more relevant customer assistance leading to increased customer satisfaction and in some cases, reduced costs and customer churn. To gain a deeper understanding of how retail organizations can benefit from Generative AI applications, we spoke with James Caton, Practice Leader, Data and Artificial Intelligence, at Microsoft, and Girish Phadke, Technology Head, Microsoft and Cloud Platforms, at Tata Consultancy Services (TCS). James and Girish discussed three ways Generative AI is transforming retail: speeding innovation, creating a better customer experience, and driving growth.

How can Generative AI speed innovation in retail?

James Caton: We’re already seeing a lot of data-driven innovation in the industry. Microsoft Azure OpenAI Service, which provides access to OpenAI’s large language models, allows more probing and deep questioning of data. A frontline worker could have the ability to “chat with their data,” to conversationally query inventory or shipping options for example, see the response in a chart, and ask for trend analysis and deeper insights.

It essentially gives you an assistant or a Copilot to help do your job. Imagine having several assistants that are parsing the data, querying the data, and bringing data reports and visual graphs back to you. And you can send the copilot back and say, “please look here,” and “I want more information there.” As a retail sales manager, OpenAI will allow you to develop more innovative solutions, more tailored strategies, and more personalized experiences.

How does Generative AI’s conversational flow enable a more compelling customer experience?

Girish Phadke: Existing call center tools can be conversational, and they do have access to 360-degree customer views, but there is a limit in terms of how far back they can go and what kind of data they can process to answer the customer’s query.

The new Generative AI models can go deeper into historical information, summarize it, and then present it in a human-like conversation. These models can pull data from multiple interactions and sources, from a huge amount of information, and create a response that is the best fit to answer a particular customer’s question. Essentially, tailoring the answer not only based on a massive knowledge base of data, but also on the individual customer’s preferences.

Can you share an example of how one of your customers has benefited from using OpenAI to process and analyze vast amounts of information?

Caton: CarMax reviews millions of vehicles. The challenge for new buyers was there were too many reviews, and they could not get a good sense for why people liked or disliked a certain vehicle. CarMax used the Azure OpenAI Service to analyze millions of reviews and present a summary. If a customer was looking at a certain make and model, the Azure OpenAI service summarized the reviews and presented the top three reasons people liked it and the top three reasons they disliked it. The technology summarized millions of comments, so that customers didn’t have to, thus improving the customer experience and satisfaction.

Are there steps that retailers can take to get ready for OpenAI and similar tools?

Caton: If a retailer wants to take advantage of these capabilities, the first thing they need to do is move their data to the Microsoft Cloud. Then, partners like TCS can help them develop their preferred use case, such as applying Generative AI to inventory or sales data or helping develop more tailored marketing campaigns. TCS knows the industry as well as most retailers. They understand the technology, how to manage and migrate data, and how to optimize to make best use of the new capabilities.

Phadke: We understand this is a new technology; retailers are likely to be cautious. They can start by augmenting existing capabilities, such as with more comprehensive Azure ChatGPT, and adjust the governance models as they learn more about their data and processes. As confidence grows, they can begin to automate the larger deployment mechanism.

How long does it typically take for an organization to see a return on investment from Generative AI?

Phadke: With the right strategy and right set of use cases, a system can start generating a positive ROI very quickly. TCS offers a six-week discovery assessment to help with ideation and strategy development. Within 12 to 16 weeks of adopting Azure OpenAI Service, an organization can have a more scaled-out implementation.

Do retail organizations have to embrace Generative AI technologies right now if they want to be able to compete?

Phadke: I think if some retailers choose to ignore this technology, they risk falling behind. Earlier adopters might get a competitive advantage. This technology is disruptive in nature and will have a significant impact on many industries, including retail.

Caton: OpenAI is the fastest application to hit 100 million users —faster than Facebook, Instagram, or WhatsApp. The risk for slow adopters is that their competitors are adopting it and might gain a competitive advantage. It is being adopted very widely, very quickly.

Learn how to master your cloud transformation journey with TCS and Microsoft Cloud.


Girish Phadke, Technology Head, Microsoft and Cloud Platforms, TCS
Girish Phadke leads Edge to Cloud Solutions, AI, and Innovation focus areas within the TCS Microsoft Business Unit. He provides advisory to customers on next generation architectures and business solutions. He tracks and incubates new technologies through TCS Microsoft Business Unit Innovation hubs across the globe. Girish is based out of Mumbai, India, and in his free time loves watching science fiction movies.
https://www.linkedin.com/in/ girish-phadke-ab25034/


James Caton, Practice Leader, Data & Artificial Intelligence, Microsoft James Caton serves as an AI Practice Leader at Microsoft, helping global system integrators build sustainable Azure Artificial Intelligence businesses. He has held technical and commercial leadership positions at software companies SAS and IBM, as well as with Larsen & Toubro Construction where he led their India Smart Cities business. James lives in Ave Maria, Florida with his wife and three daughters. https://www.linkedin.com/in/jmcaton/

Artificial Intelligence, Retail Industry

Despite the best of intentions, CIOs and their organizations often struggle to deliver business outcomes from digital transformation strategies. According to research firm Gartner, 89% of corporate boards say digital is embedded in all business growth strategies, but only 35% of organizations are on track to achieve digital transformation goals. And while KPMG reports that 72% of CEOs have aggressive digital investment strategies, McKinsey details a harsh reality that 70% of transformations fail.

Stats such as these raise the question: How can CIOs and digital transformation leaders better recognize failure signs and proactively address issues?

My experience leading many digital transformations is that failures stem from a series of derailments, many of which are inadvertent. Even if digital transformation leaders avoid outright failure, these derailments delay initiatives, create avoidable organizational stress, and often yield underwhelming business outcomes.

Five years ago, I shared that the No. 1 reason digital transformations fail is that executives fail to recognize that digital initiatives are bottom-up transformations that require change across the organization. Employees must understand the why behind digital strategies and have incentives to participate in transformation initiatives. CIOs like to say, “Digital transformation is a journey,” but I believe leaders must strive to lead transformation as a core organizational competency

CIOs can’t be involved in every strategic discussion or dive into every initiative’s details, but there are several high-level signs that indicate a digital transformation may be destined to underperform, especially as CIOs add initiatives. In my experience assessing digital transformations, the following five are the most common. 

1. Prioritize too many initiatives without a shared vision

“One of the most common ways to derail digital transformation efforts is ignoring the importance of a clear strategy and defined goals,” says Arturo Garcia, CEO of DNAMIC.

CIOs must communicate strategy and goals when making investment cases and garnering support from the CEO, executives, and the board. As challenging as it is to get to a yes, it’s what comes next that often derails digital transformations at the very start.

CIOs must facilitate a discussion on priorities. Having too many number-one priorities sets unrealistic expectations with business stakeholders and stresses team leaders. Worse is when prioritized initiatives don’t have a documented shared vision, including a definition of the customer, targeted value propositions, and achievable success criteria.

When I survey transformation leaders and their teams, I privately ask each person three questions: What’s your top priority, why is it important, and how many other initiatives are also taking up your time? The risk of derailments increases as I hear inconsistent answers or too many conflicting priorities.

2. Neglect to set collaboration and communication principles

Digital transformations can start with one initiative, defined goals, and a dedicated team. But CIOs are under pressure to accelerate and find digital transformation force multipliers. That means growing the number of leaders and teams that can plan innovations and deliver transformative impacts.   

“Innovation does not happen in isolation: It occurs when organizations encourage and nurture it, often with processes to enable nontraditional ways of thinking, working, and the space to try out ideas in a safe environment,” says Hasmukh Ranjan, CIO of AMD.

Here’s how I spot derailments: Ask initiative leaders to share access to their roadmaps, agile backlogs, collaboration tools, stakeholder communications, and internal documentation. I seek information completeness, communication consistency, and ease-of-use factors. When CIOs struggle to grow beyond one transformation initiative, the root cause is often gaps in collaboration and communication principles.

3. Customize solutions to meet everyone’s requirements

Many organizations use agile methodologies when planning and executing digital transformation and assign multidisciplinary teams to manage releases, sprints, and backlogs. But are product managers developing market- and customer-driven roadmaps and prioritized backlogs? Unfortunately, many digital transformation initiatives succumb to stakeholders dominating priorities with neverending wishlists and poorly defined requirements.

One recent study shows that only 50% follow a product-centric operating model focusing on customer centricity and delivering delightful customer experiences. “Companies that leverage high-quality data, center their enterprise around responsible risk-taking, and organize around products are the most likely to experience profitable growth from their digital transformation journey,” says Anant Adya, EVP of Infosys Cobalt.

Subject matter experts and internal stakeholders should be contributors to priorities and requirements, not decision-makers or backlog dictators. Digital transformations derail when CIOs miss the opportunity to establish and communicate product management responsibilities for creating and evolving market- and customer-driven roadmaps.

4. Underinvest in developing digital trailblazers

In its 2023 State of Digital Transformation report, TEKSystems found that 48% of tech and business decision-makers report needing to revise the nature of their organization’s talent base, and another 34% acknowledge needing new types of talent. “Organizations can derail their digital transformation journey by failing to map out goals, objectives, and tactics prior to launch and not valuing the right mix of IT and business stakeholders in the planning stages,” says Ricardo Madan, senior vice president at TEKSystems.

CIOs invest in skills development, and HR usually offers leadership development programs, but these approaches often don’t address the knowledge and skills needed to lead digital transformation initiatives.

Digital trailblazers, including product managers, program managers, architects, agile delivery managers, and data scientists, need specialized learning programs and coaching to build their confidence in handling transformation responsibilities. Derailments can happen when transformation leaders seize up when negotiating priorities, fail to facilitate decisions on requirements, or struggle when handling conflicts or blow-up moments. Digital trailblazers face many people challenges when guiding employees through a transformation, and CIOs should identify coaches and development programs to prepare their leaders.

5. Drive KPIs and data-driven decisions without a data strategy

Building digital products, improving customer experiences, developing the future of work, and encouraging a data-driven culture are all common digital transformation themes. Leaders should define new KPIs and OKRs that help people understand the objectives and recognize how their work contributes to the organization’s transformation goals.

But there are common pitfalls, such as selecting the wrong KPIs, monitoring too many metrics, or not addressing poor data quality. “Having bad data, or an inability to realize the value and take action from data, is a surefire way for a digital transformation project to go south quickly, says Dwaine Plauche, senior manager of product marketing at AspenTech. “Without useful, contextual data that can be scaled and used throughout the organization, digital transformation efforts may simply become one-off  projects that get stalled at the pilot phase, leading C-suite leaders to believe the technology was a failure or the investment was a waste.”

This derailment stems from having no defined data strategy or having one not aligned with digital transformation objectives.

Consider how it looks to nontechnical executives when every digital transformation initiative has customized dashboards, different KPIs, and metrics with underlying data quality issues. Instead of initiatives telling a cohesive story, it leaves results open to interpretation and challenges. The data strategy should include guidelines on the types of KPIs, standards for dashboarding metrics, and responsibilities for improving data quality.

The five derailments I focus on here fall within the CIO’s responsibilities to address. They are important for CIOs leading multiple transformation initiatives to deliver against several business strategies. The practices that worked when digital transformations started small with one initiative must evolve into a digital culture and a transformation operating model. It’s in this transition where increasing derailments can lead to digital transformation failures.

Digital Transformation

By: Gayle Levin, Senior Product Marketing Manager for Wireless at Aruba, A Hewlett Packard Enterprise Company.

If you’re like me and you’ve been reading the news lately, the economic outlook is all over the place. It’s difficult enough to prioritize IT spending and align efforts to support business initiatives without trying to predict the future economic outlook. That’s why I wasn’t surprised that a recent survey from IDC[1] showed that IT leaders are taking a measured approach: Keep budgets stable while at the same time building in flexibility should the macroeconomic environment change significantly. To help take control in these uncertain times, this blog outlines six strategies to modernize your Wi-Fi.

6 ways to drive operational efficiencies by modernizing your Wi-Fi network

Shift to cloud-based network management. Cloud-based network management increases agility and allows resource-constrained IT departments to focus on optimizing the network, not deploying, managing, or upgrading the network management system. Cloud-based network management also better aligns spend through a subscription, OpEx-driven model.

Adopt AI to better leverage existing hardware investments. 54% of organizations say that their environment is more complex that it was two years ago.[2] AIOps can help identify areas for optimization using existing hardware by combing through a tsunami of data faster than any human ever could. It is not about replacing network operators with the next incarnation of ChatGPT; it is about surfacing problems and making recommendations based on a huge data lake across all verticals and all geographies to identify best practices.

Future proof with Wi-Fi 6E. Wi-Fi 6E represents the latest generation of the Wi-Fi standard and depending on the geography, doubles or triples the amount of available capacity for Wi-Fi. This is critical because the current 2.4 GHz and 5 GHz bands are crowded. Although they include wider channels which are ideal for latency-sensitive applications like Microsoft Teams (essential in hybrid work and remote work environments), in practice there is not enough bandwidth to use the wider channels. With the extra bandwidth afforded by the 6 GHz band (up to 1200 MHz), business-critical applications can work flawlessly, higher volumes of client and IoT devices can be supported, and new use cases such as augmented reality and virtual reality can be deployed. I’ve heard from some organizations that they plan to wait for Wi-Fi 7; however, the biggest change is the historic introduction of the 6 GHz band – other enhancements pale in comparison.

Automate security enforcement. Manual processes are not only time-consuming but error prone. A unified approach to security – across wired and wireless environments, as well as large campuses, midsized branches, even remote workers – streamlines security efforts. This is critical as security and network efforts become more inextricably linked and the shortage of skilled security professions becomes more entrenched.

Eliminate manual AP surveys. Did you know that it takes approximately 10 minutes per AP to survey its location and place it on a map? This manual process also introduces errors which can limit the usability of Wi-Fi location-based services. By automating the survey process, networking teams can quickly and accurately map the APs to be used in a wide range of applications from traditional wayfinding and asset tracking to highly accurate proximity triggers – at scale. These APs are also well suited to support Wi-Fi 6E Standard Power once it gains regulatory approval as it requires a location using universal coordinates (latitude and longitude) to ensure incumbents are protected.

Start using APs as an IoT gateway. 96% of corporate networks have or will have Internet of Things devices and sensors connecting to them[3]. By leveraging the AP’s ability to support a wide range of devices that use Bluetooth (BLE), 802.15.4/Zigbee, or USB ports, IT can securely onboard IoT devices and streamline management. New protocols such as Easy Connect (also known as DPP) solve the problem: How do I securely onboard my device when I need the device to connect to the network to verify its credentials?

Learn more today:

Explore the infographic

Watch the video on Wi-Fi 6E

Learn how to do more with your network


[1]  Future Enterprise Resiliency & Spending Survey – Wave 11, IDC, December, 2022; n=840. Compared to your organization’s likely final IT spending levels for 2022, how will current disruptions affect your organization’s most likely IT spending levels for 2023?

[2] Enterprise Management Association, Network Management Megatrends 2022

[3] ibid

This blog was published on blogs.arubanetworks.com on 03/27/23.


Have you ever gazed upon a Monet painting and lost yourself for a time? I have. I love great works of art. The University of London’s research says beautiful art catalyzes an instant release of dopamine into the brain. I feel that jolt of reward and motivation when I see a masterpiece.

As an artist and an engineer, I find myself curious about the techniques, stories of the paintings, reasons behind the color choices, the style of brushstrokes, and the preservation of each piece. I started studying art to give myself a break from the high-tech world. Now, ironically, the art world is being disrupted by emerging technology–specifically generative AI tools such as OpenAI’s ChatGPT, Google’s Bard, and Meta’s LLaMa. These tools enable me to discover the information I am seeking—and do so in near real-time and in novel, profound ways.

Art, business, and generative AI

Art has existed since the dawn of humankind, giving us a window into history and stories waiting to be revealed. Each masterpiece is more than just the marks on canvas; it’s the culmination of a culture, an artist, and influence. Art is an ideal example of how generative AI can create a massive amount of value by extending the way we look at artwork. 

Generative AI is a way to create augmented information that expands our understanding of an image and the ephemera around it. For instance, if you start with a Monet painting and use an extended generative AI model trained on all Monet artwork and complementary artists, you can create an input dataset that includes images by the artist. The dataset is complemented by a text-based dataset sourced from the entire internet. You can ask generative AI to tell you about the Monet image, and it will generate an essay about Oscar-Claude Monet and that specific painting. Then, you can use the essay to create more information with direct and indirect correlations, such as to another oil painting, related modern impressionist art, or a different Monet image. Generative AI does this based on a knowledge graph, a network of related concepts. 

Simply put, generative AI is a powerful way to exponentially expand a small amount of information into a very large understanding, which in turn leads to smarter, more informed decisions.

Powerful potential but less than perfect

Generative AI is rapidly moving into the business world, across industries and companies of all types and sizes. Companies such as Salesforce, Amazon, The Coca-Cola Company, and Snapchat are making bold moves to integrate generative AI into a host of capabilities. Generative AI has the potential to revolutionize many aspects of our lives, but ethical considerations must be addressed when developing and deploying generative AI models.

Ethical considerations must be a fundamental part of the development and deployment of generative AI models to ensure they are used in ways that are fair, safe, and beneficial for society as a whole. For that, generative AI needs explainability.

4 ways to enable explainability in generative AI

Creating explainability in a generative AI model can help build trust in the models and the confidence to develop enterprise-level use cases. Explainability requires careful consideration and planning throughout the entire development process. (You can even ask ChatGPT about this.)

Here are some key guidelines:

Model simplification. As AI models become more complex, understanding how they arrive at decisions or predictions becomes more difficult. Deep learning models, for example, can have thousands or even millions of parameters. Model architecture can be simplified by reducing the number of layers in the neural network, which enables understanding and improves interpretation of the model’s decisions.

Interpretability tools. Several tools are available that can help developers interpret the decisions made by a generative AI model. For example, model trainers can use attention maps to visualize which parts of an input image are most important for the output. Model developers can use other tools, such as decision trees or feature importance plots, to identify the key factors that influence a model’s decision.

Referenceable data. Training data must be referenceable and audited for quality control. This helps flag unintentional bias, prevent unfair decisions, and enable ongoing improvements, including provenance for traceability.

Human oversight. In some cases, you may need a human in the loop to provide oversight and ensure that the generative AI model is making decisions in a responsible and ethical manner. For example, a human could be tasked with reviewing the output of a model to ensure it’s not generating harmful or biased content. Using diverse groups of people to access data sets and help identify non-obvious biases has proven to be an effective method of detecting unintentional heterogeneity, which leads to model bias. In addition, trained people from different backgrounds can review algorithms using company or government policies and standards to look for potential ethical challenges with the use of data or in the outputs of the models.

Moving ahead

Generative AI has the potential to transform many aspects of our lives, including the world of art. We already use models to create new works of art, preserve existing works, and expand our understanding of art history. For example, researchers have used AI to restore a painting by Rembrandt that was damaged by fire. The AI model analyzed other works by Rembrandt to generate a new image that closely matched the original painting.

As generative AI evolves and extends value into more enterprise use cases, IT leaders, technologists, and developers must adopt a holistic approach that considers the technical, ethical, and social aspects of AI explainability and involves all relevant stakeholders in the development and deployment of AI models. By doing so, we can ensure that we use generative AI in ways that benefit society as a whole.

How we collectively work toward responsible use of generative AI is the story we want future generations to discover when they see the masterpieces we build with this powerful capability.

Nicole Reineke is senior vice president of innovation at Iron Mountain

Iron Mountain

Nicole Reineke is senior vice president of innovation at Iron Mountain. Prior to this role, she was a senior distinguished engineer in the office of the CTO at Dell Technologies. Over the last 20 years, she has founded and led high-tech companies in product executive leadership roles establishing expertise in areas such as sustainability-aware infrastructure, data trust, blockchain, hybrid cloud, artificial intelligence/machine learning, artificial intelligence ethics, augmented and virtual reality, data center management, and intelligent data management. She has 14 patents, awaits grants on 75 additional filings, and is the co-author of “Compassion-Driven Innovation: 12 Steps for Breakthrough Success.” She is a passionate hobbyist—a pianist, dancer, and artist—who enjoys hiking with her dog and family.

Fraud Protection and Detection Software

“Who owns and oversees employee experience and the future of work at your organization” is a question I’ve been asking CIOs and IT leaders a lot of late. The ensuing conversation usually reveals a telling disconnect that CIOs should remedy for the health of their companies.

Most IT leaders pause before responding to this question. Some go on to describe hybrid work plans,  which is one aspect of the future of work, but it’s not the complete scope. To align on terminology, I share Gartner’s definition, “The future of work describes changes in how work will get done over the next decade, influenced by technological, generational, and social shifts,” and then ask them to reconsider this greater scope.

After another pause, some will say there isn’t ownership around this agenda. Others say human resources leads the future of work considerations for the enterprise, and department leaders own it for their teams. This may be so, but it isn’t a recipe for ensuring long-term organizational success.

The CIO as a key driver for the future of work

Many CIOs will say IT is involved in laying the foundation for the future of work at their organizations, but usually in a supporting role. Helping departments with automations is one area where CIOs consider IT to be a driver. Or when a department procures new technology, an implementation requires IT’s assistance, or when integration is needed. 

But taking this kind of butler approach to the organization’s future of work mission and waiting for business drivers can be shortsighted. CIOs should take more of a leadership role, especially when future of work initiatives can be a digital transformation force multiplier.

CIOs have the opportunity to improve their organization’s competitiveness, promote innovation capabilities, and catalyze culture change by driving blue-sky thinking around how technological shifts will transform employee responsibilities and experiences. Here are three technology areas CIOs should focus on.

1. Transform knowledge management with generative AI

ChatGPT and other forms of generative AI have generated a storm of consumer interest that is carrying over into the enterprise. Many marketing departments are embracing content generation, image creation, and video editing to scale their workflows, while Microsoft added ChatGPT capabilities to its office suite, and Google is adding generative AI tools across Workspace.

“Generative AI is reimagining the future of work, from the content we write to the creative we use and how we converse with each other,” says Yishay Carmiel, CEO of Meaning. “While early challenges with accuracy and credibility remain a barrier to entry, generative tech is still proving valuable for enterprises producing content and uncovering valuable information quickly and at scale.”

One area I expect generative AI to impact the future of work significantly is knowledge management and enterprise search experiences. I expect we’ll see the consumerization of search and knowledge management over the next decade, driven by generative and conversational AI capabilities. 

Today, most enterprises create, store, and search content across a breadth of tools, including CRMs, CMSes, ecommerce platforms, office suites, and collaboration tools. Employees search for content using primitive keyword search boxes instead of natural language processing and conversational AI capabilities. These capabilities are ripe for transformation, and AI search is a force multiplier when it centralizes information access, addresses tribal knowledge risks, and personalizes employee experiences. 

2. Drive self-service capabilities with no-code tech

The first no-code tools for building web applications became available over two decades ago. Today, most organizations use a mix of low-code and no-code tools to build applications, and many support citizen development performed by non-IT employees.

No-code isn’t just for developing apps, as many organizations use no-code self-service business intelligence tools such as Power BI and Tableau to enable a data-driven organization and reduce the reliance on operational spreadsheets. There are also no-code data prep, automation, and integration tools used by marketing, operations, and finance teams with staff and skills to implement technology solutions with little or no IT assistance.

CIOs should embrace no-code and citizen development as a key future of work strategy. The reality is that IT is always understaffed, and many people entering the workplace have the sufficient technical acumen to work with no-code technologies.

Empowering employees with no-code technologies can drive a culture transformation when CIOs drive the initiative and IT provides support services. Instead of IT saying “no” or having staff waiting for IT’s help, departments have technologies to drive their agendas.

What does it mean to drive self-service capabilities? CIOs should define a citizen development governance model and govern citizen data science so that no-code apps and dashboards developed today don’t become tomorrow’s technical debt. Disciplines such as identifying requirements, versioning applications, testing functionality, establishing security access roles, documenting releases, reusing capabilities, and defining standards are all important whether an app is developed with code, low-code, or no-code. 

3. Accelerate decision-making with hyperautomation and real-time analytics

If self-service business intelligence and data catalogs helped democratize data, then hyperautomation and real-time analytics will enable CIOs to accelerate smarter decision-making.

Large enterprises have transformed from batch data processing, where executives review weekly and monthly reports to more real-time analytics. In addition, CIOs have scaled beyond using robotic process automation (RPA) on tasks and workflows and now focus on hyperautomation, the integration of automation, low-code, and machine learning capabilities to enable smarter decision-making.

These technologies and capabilities are mainstream, and more small and medium-sized businesses (SMBs) can no longer afford to be laggards in driving intelligent automation.

Tom Sagi, co-founder and CEO of Hourly.io, says, “As the Federal Reserve continues to increase interest rates, small and medium businesses will continue to look for ways to save money this year. The future of work for SMBs will be driven by their ability to adopt new technologies like automation and real-time analytics and will be a key driver of innovation for SMBs focused on saving time and money.”

Here, opportunities include empowering the finance organization with real-time analytics capabilities or using hyperautomation to improve field operation’s resource scheduling.

But the key opportunity for CIOs is to use these technologies as building blocks by asking, “How can we reimagine workflow X by integrating automation, real-time analytics, machine learning, and low-code capabilities?”

CIOs should become drivers of the future of work, starting with blue-sky thinking, implementing radically reinvented workflows, and focusing on employee experiences.

Artificial Intelligence, Digital Transformation, Emerging Technology, Innovation, No Code and Low Code

The current state of the contact center agent is clear, but for those unaware or overlooking this opportunity for improvement: agent attrition rates currently hover around 40%, the cost of replacing just one agent is between $10k-$20k, and 97% of agents are sometimes or almost always burned out. Unengaged employees (undoubtedly including contact center agents) collectively cost $7.8 trillion in lost productivity, or about 11% of the global GDP. 

Whether your organization has one or two people handling customer service or a dedicated contact center with hundreds of employees, the quality of the experience of these people serving your customers cannot be overstated. A poor agent experience translates into money lost, resources strained, and inconsistent service. The role of the contact center agents spans well beyond merely picking up a phone. These individuals are undisputed stakeholders of the customer experience (CX) and vital contributors to business success.

Can 2023 be the year we start thinking differently? Avaya predicts organizations will double down on the agent experience in three ways this year:

1. Companies will rethink traditional workforce optimization concepts—driven by a focus on strict adherence to rules and procedures—and embrace a workforce engagement approach. 

Instead of being contact answering robots, agents will be encouraged to become creative thinkers, problem solvers, and true brand ambassadors—critical assets for customer experience success. As such, we’ll see workforce engagement (aimed at strengthening the mental and emotional connection agents feel toward the work they do, their teams, and their employer) take its rightful place next to established workforce management solutions in the contact center, which will get their ofaceliftift this year. 

This shift is critical for the future of contact center operations. According to the International Customer Management Institute (ICMI), engaged and satisfied contact center employees are 8.5x more likely to stay than leave within a year, 16x more likely to refer friends to their company, and 3.3x more likely to feel extremely empowered to resolve customer issues.

2. We can expect to see the continuation of a mindset shift at the managerial level in terms of agent criticality.

The average hourly salary of a contact center agent in the U.S. is $16.62, according to Indeed.com, with this rate being as high as $21.75 in states like California and New York. In California, positions including IT Technicians, Maintenance Technicians, and Certified Nursing Assistants have similar hourly rates. 

The job of a contact center agent requires a highly skilled individual who can creatively problem solve, effectively communicate, and positively influence; an impactful role for those up for the challenge. These individuals must be invested in and supported long-term just as workers in other industries, including targeted, customized training and education and the potential for raises and bonuses. Avaya expects more contact center managers to embrace this perspective and act accordingly.

3. Artificial Intelligence (AI) will be massive for contact center communications over the next 12 months. 

Avaya expects more organizations to apply AI to their contact center operations and customer communications in 2023. Doing so will reduce customer wait times (estimated to have tripled during the pandemic), improve the delivery and accuracy of content knowledge so agents can more meaningfully impact CX and influence next best action, and reduce agent busywork (ex: using AI-powered transcription to auto populate transcribed text files into the notes section of CRM records). 

One of the most hard-hitting statistics comes from Gartner: 10% of all contact center interactions will be automated using AI-powered solutions by 2026, compared to only 2% currently, saving organizations approximately $80 billion per year in labor costs. 

Prepare for the agent experience with these resources from Avaya: 

Time To Think Differently l AvayaHow EX and CX Should Work Together l AvayaHow does CX correlate with EX? l Avaya

Artificial Intelligence

CEOs continue to see the need for more collaboration between IT and the business units, so much so that in a recent survey CEOs listed that as the No. 1 objective for the IT function.

The State of the CIO Study 2023 from Foundry, an IDG company and publisher of CIO.com, found strengthening IT and business collaboration to be CEO’s top priority for IT this year, ahead of upgrading IT and data security to reduce corporate risk (No. 2 on the list) and improving the customer experience (No. 3).

Experts say such findings aren’t surprising, as collaboration and alignment between IT and the rest of the business are key for picking the right priorities, driving the right investments, and bringing transformative programs to fruition.

Foundry / CIO.com

Yet they also say CIOs often struggle to get and keep business buy-in, particularly for bigger initiatives that require change or have hurdles to overcome — as most innovative endeavors inevitably do. Those can still be hard to sell, even today, when digital transformation dominates. And enthusiasm for those projects can be difficult to sustain, particularly when teams hit those obstacles.

There are strategies that can help here. Executive advisors and veteran CIOs offer 12 ways to win and maintain business buy-in — even when the going gets tough.

1. Be ‘technology ready’

To win and keep business buy-in, IT has to do more than bring its A-game. It has to deliver for the business “faster, better, and cheaper than anyone else,” says Mohamed Kande, vice chair at professional services firm PWC, where he is US Consulting Solutions co-leader and Global Advisory leader.

That takes attention to all the moving parts within the IT function. CIOs need the right people, the right teams, and the right technology so they’re able to focus on enterprise strategy, knowing they have an engine ready to support it, Kande says.

“Otherwise, they’re still trying to take care of the basics and they aren’t in a place where they’re able to think about the future,” he says.

To that point, he says CIOs may not need to move everything to the cloud but they should “move what they must” so they’re not weighed down fixing fundamentals and instead can focus on the corporate strategy questions.

“Being technology ready means that the technology the company is using is helping them run the company in an optimal way with flexibility and security,” Kande says. “It allows the CIO to have a different conversation; it allows the shift to business outcomes.”

2. Speak to specific business outcomes

Modern CIOs know to speak in business terms and leave the tech jargon behind. But those who are truly intertwined with their business unit colleagues are speaking not only about strategy but key components of it: growth, revenue, profit margin, and so on.

As Kande explains, “The business is asking for technology to deliver business outcomes: Are we selling more products and services? Do we have [for example] more visibility into manufacturing or supplies?”

Christine Dunbar, founder and CEO of ROC Implementation & Management Group, a Gaithersburg, Md.-based business strategy, cybersecurity, and IT consulting company says CIOs who do that best know the metrics the business wants to see and hear in such cases.

CIOs who speak in the metrics that quantify those business outcomes — whether that’s figures on a new digital product’s ability to boosts sales or calculations around a new platform’s customer retention capabilities — are the ones whose plans are continuously endorsed by their business unit colleagues, Dunbar says.

3. Be fluent in the language of the business units

Speaking of the language used, Brian Hoyt, former CIO of Unity Technologies and AppDynamics and now COO at Parkway Venture Capital, says business unit and IT teams tend to develop more enduring partnerships when they’re able to talk about work on the same level.

“It’s absolutely critical to have IT resources highly fluent in the goals of the business units they cover,” Hoyt explains. “I always viewed it as a success when my IT team members were invited to participate in [quarterly business reviews] or strategic planning exercises with the business unit they service. We measured ourselves by how much engagement we are getting.”

4. Be in ‘relationship mode’

In a summer 2022 Harvard Business Review article titled “The C-Suite Skills That Matter Most,” the authors presented findings from their research on this topic, writing that while management of financial and operational resources remains a critical skill, companies “instead prioritize one qualification above all others: strong social skills.”

RJ Juliano, senior vice president and chief information & marketing officer at Parkway, has seen the importance of those skills in winning allies among his executive colleagues. As such, he works to build connections with his peers before he needs to seek their support. That way, when he does need that buy-in, he says there’s a well of trust that he can draw on “because that mutual understanding has already been established.”

“Think about your vendor relationships. Do the same [relationship building] with your peers. Find reasons to interact with them in nonstructured time, whether it’s lunch or it’s those times that aren’t strategic planning meetings. Don’t always be in a transactional mode; be in a relationship mode. That goes for the board, too,” he says.

5. Work out issues in advance

Another way to get and keep business buy-in, according to Juliano: Identify and work out issues ahead of time — especially with any skeptics.

“Make yourself go have those conversations one-on-one well in advance of a decision,” he says. Get feedback early. Check in and course correct before finalizing plans.

Juliano says it’s about recognizing with any initiative that the problems and pain points the business units face and IT’s problems “are the same problem.”

Juliano works by this approach, pointing to how he handled discussions about the current ongoing project to replace the company’s enterprise resource planning (ERP) system. In addition to conversations about business needs, he has talked with business unit leaders about the organizational maturity of all the teams and the teams’ readiness for upcoming changes before embarking on this project.

Through that advance work, Juliano and one of his peers were able to work together on some organizational issues that if left unaddressed could have limited the company’s ability to get full value out of the ERP replacement and, as a result, could have led to diminishing support for the initiative.

6. Make sure the CEO agrees

Buy-in starts at the top, so it’s essential to cultivate positivity there, too. “It’s really important to ensure that the CEO views the IT team as an equal stakeholder for successful business outcomes. Otherwise, the relationship with business unit heads will not last,” Hoyt says.

He adds: “A CIO’s responsibility is to ensure the CEO can easily articulate what projects are happening and why they are important to the overall strategy.”

7. Embrace the concept of shared pain

Another approach Juliano uses to ensure IT and business are in lockstep as they advance organizational objectives is to identify and highlight shared goals. For him, that means in part articulating IT’s piece of initiatives as well as demonstrating IT’s commitment to co-owning success — and, if things don’t go right, co-owning failure, too.

“Your IT deliverables should be 100% part of the business’ strategic goals,” he says. “But if you’re making plans and you’re not seeing that there’s a clear IT objective, then you’re reducing your chance of successes and I’d question why you’re not part of that execution. So get your name on those goals so you are seen as a co-deliverer. Make sure your name is primary or secondary owner.”

Juliano says that sends a good message to executive peers as well as the teams doing the work: “It’s saying, ‘I’m taking responsibility to say my resources and planning are behind this.’ It’s the shared pain, shared gain. If we succeed, we succeed together or if it’s not going well, we’re in it together.”

He continues: “IT can get disconnected from company goals but with this you can walk back into your team and say, ‘This is where we’re on the hook.’ And that improves your team’s chances of success, because it helps focus the team and they know they’re part of the strategy.”

8. Employ business relationship managers

Dunbar believes the business relationship manager is a crucial role for the IT team as business relationship managers help ensure IT knows and focuses on the initiatives that the business values and, thus, will support.

“Business relationship managers understand the business, the strategy, and what’s going on. They’re going to understand the nuances — that’s a key word — they understand the nuances of what the business wants and can communicate that to the IT team,” she says.

Paying attention to those nuances matter, Dunbar says, as they often mean the difference in big wins versus marginal improvements and, consequently, whether a business unit stays committed to the work with IT or wanes in its enthusiasm.

9. Be upfront about risks

IT typically houses project managers, or at least workers skilled in project management principles, which makes the department well qualified on identifying risks and planning mitigation strategies.

Juliano leans into that, using that information to help shepherd initiatives from start to finish and through any periods of concerns or doubts that come up.

He says it’s easy, and understandable, for executives to focus on the benefits and ROI of any given project. And while conversations about ROI are crucial for getting buy-in from stakeholders, Juliano says focusing on those good points upfront may not be enough to sustain everyone when they hit bumps moving forward.

“So have honest conversations about the risks and what you’re going to do about them as well as what the rewards are,” he says, an approach he says works particularly well for CIOs who have built a record of successful change management which demonstrates to their workers’ ability to mitigate risks.

Juliano says he has found that upfront honesty helps address doubts and questions that stakeholders have, prepares them for any obstacles that arise, and reassures them that there’s a plan to pull everyone through.

Juliano points to one particular initiative, a construction project which had a lot of technology components. He brought up potential stumbling blocks and how to address them. He found that others had been thinking about the same potential obstacles. And by vocalizing concerns about the roadblocks ahead and plans to deal with them, he got everyone together to move forward.

10. Use retrospectives to strengthen partnerships

Dunbar says encouraging introspection and feedback from business teams can yield insights on how they view IT, the work it delivers, and where improvements could create a tighter coupling of interests.

As she explains, “Using retrospectives to gain insight on third-party vendors and IT team performance is very helpful. The beauty of the retrospective is that they can be done midway through the year or at any point during the performance of a contract. We recommend that an independent party lead the sessions and that members of the IT team not participate in the sessions to enable candid feedback from the business teams. This type of activity builds trust between the business and technology teams.”

11. Create cross-functional centers of excellence

Another way to get everyone in the same boat — and keep them rowing together — is by creating cross-functional centers of excellence, with the functional business areas involved in projects contributing key players to work with IT, says Mark Taylor, CEO of the Society for Information Management (SIM).

“You’re capturing the innovative energy happening in pockets of the organization and moving it toward more coordinated efforts within these centers of excellence,” he says. This creates a camaraderie where CoE members are able to show up as contributors, which helps eliminate siloes of work and turf wars while building a sense of team.

It reinforces the idea, he explains, that teamwork “is how we collectively get this done.”

12. Empower the business

Robert McNamara, a partner in business and IT strategy at consultancy Guidehouse, says IT can get and keep business buy-in by empowering business units to make and manage some tech-related decisions and tech-driven initiatives.

“There are some things that makes sense to be managed by a tech unit within a business [function] versus the centralized IT organization, and that line of business can reach out for support to the centralized IT organization if and when needed,” he explains.

McNamara says having business units in charge of some technology programs can reduce costs and risk while also improving alignment and compliance. All that, he says, can help get and keep the business onboard with IT strategy.

CIOs can work with their business unit peers to create guidelines for what tech would be better managed within the business units, establish the governance needed, and articulate the benefits this move brings, “whether it’s to shorten the feedback link between the users and what they need from the technology, accelerate innovation, or improve compliance,” he says.

McNamara adds: “If you focus on that, versus who controls it, it helps demonstrate that IT is looking out for the business needs, so they see it’s not about turf.”

Business IT Alignment, IT Leadership

The potential for generative AI systems such as OpenAI’s ChatGPT and Google’s Bard to transform how businesses work is being realized. Hype still surrounds some predictions, but change is here, and one of the first product categories to be impacted is CRM systems. 

Software-based services are the low-hanging fruit when it comes to this emerging revolution. AI can be plugged into existing software more easily than using it to build services from scratch. CRM systems are a particularly attractive target for this since when implemented effectively, they can have a rapid impact on a company’s bottom line. Nudging sales up a couple of points by better targeting profitable customers is a realistic strategy for a CRM deployment.

Segmenting customers more effectively

A well-integrated CRM system should be able to produce reports on customer segments, spending patterns, and spend history. From this, marketing campaigns can be tailored to reach specific customer groups or direct sales approaches made by a sales team. However, depending on the CRM and its configuration, these reports can be difficult to run and be delayed reaching the right people. Microsoft’s integration of ChatGPT into its Power Platform is enabling businesses to quickly build their own workflows that incorporate a user-friendly chat interface. Rather than running often complex report requests, users from across the marketing and sales team can use natural language to identify prospects instantly.

Transforming the sales funnel

Building on the capability above, sales funnels and customer journeys can be modified on the fly to adapt to changing variables such as economic conditions, competitor activity, and evolving tastes. What worked last month, for instance, may not work this month and AI offers the potential for a more dynamic process of changing prospects into customers. 

Repurposing corporate information assets

Companies are drowning in data, with IDC predicting there will be more than a five-fold increase in the data generated by organizations between 2018 and 2025. Bearing in mind that only about 2% of the data generated in 2020 was retained and used in 2021, many businesses are not effectively leveraging their information assets. Generative AI offers the potential to quickly and cost-effectively repurpose corporate data assets as marketing materials, whether in text or image formats.   

Creating personalized marketing content

To further help leverage information assets, marketing content and messages can be tailored on a personalized basis to suit the needs and desires of individual customers and prospects. It’s not too far-fetched to imagine generative AI systems showing customers wearing or using a company’s products in a variety of scenarios. Clearly there are privacy issues, and any implementations would need to be managed very carefully but, with appropriate customer opt-ins, it’s easily within the realms of possibility.

Incorporating the power of platforms

The CRM vendors set to dominate this emerging landscape will be those who build platforms that draw in third-party developers to extend their product offerings, and this year, we’re already seeing this with announcements from Salesforce and Microsoft. Many of the winners of the last 20 years of technological innovation have been companies that built platforms. Both software and hardware products are improved by complementary services built by outside specialists, and creating a thriving ecosystem of apps and add-ons mutually benefits the platform host, its users, and developers. Smaller CRM vendors will struggle to achieve this, however, as they may not have the critical mass of a large user-base needed to attract developers. 

Just as the internet and the Web have changed how we communicate, find information, and shop, so to will generative AI change the dynamics of competition for most businesses. The technological infrastructure is in place for this new generation of AI systems to sweep away many established ways of working. CRM will be just one part of this revolution, but its impact will be felt across all sectors of the economy.

Artificial Intelligence, Channel Sales, CRM Systems