Two Types of Companies: IT as a Cost — or IT as a Catalyst

Posted on August 22, 2025 by NicB72

The 2020s Reality Check

IT spend keeps rising—not just to “keep the lights on,” but to modernize the stack for AI. Gartner projects global spending will hit $5.7T in 2025, with outsized growth in data centers and software to power AI workloads.

Adoption has gone mainstream: McKinsey reports ~75% of organizations now use AI in at least one function, and ~65–71% are already using generative AI regularly.

But adoption ≠ impact. A 2025 MIT study found 95% of gen-AI pilots show no measurable P&L impact. Not because models fail—but because workflows aren’t redesigned and integration is weak.

That’s the dividing line between the two types of companies.

Type 1: IT as a Cost Center

Companies in this camp view technology as a utility, not a differentiator. The mindset: minimize spend, outsource thinking, and justify projects as expense lines.

Symptoms:

Tool sprawl without a platform strategy.
AI experiments that never change how work is done.
Compliance seen as drag, not design constraint.

The result? Transformation fatigue. Studies show ~70% of large digital programs still fall short on time, budget, or scope.

Type 2: IT as a Catalyst

Catalyst companies use technology to rewire operations. They measure outcomes, centralize data and governance, and redesign workflows.

Evidence is clear: gen-AI impact shows up when senior leaders (often the CEO) own governance and drive workflow change—not just tool adoption.

MIT CISR’s 2024 research on “real-time businesses” found top performers achieved 62% higher revenue growth and 97% higher profit margins than laggards.

What Changed in the Last Five Years

Gen-AI moved from novelty to embedded capability. Adoption nearly doubled from 2023 to 2024.
Productivity gains are real—but only with redesign.
- AI-assisted customer support: 14% lift on average, 34% for novices.
- AI pair-programming: tasks completed ~55% faster.
Cloud and data platforms matured. Moderna and Pfizer proved how cloud-native ML stacks compress discovery and clinical timelines.
Regulation reshaped design. The EU AI Act and NIST AI RMF now define the floor for compliance.

Why Many Still See IT as a Cost

No operating-model change: tools without workflow redesign.
AI sprawl: dozens of disconnected “vertical AIs” that don’t compound value.
Transformation risk: executives over-rotate to proofs-of-concept versus scaled platforms.
Integration gap: most pilots stall without integration and feedback loops.

Proof Points: IT as Catalyst in Action

Real-time operating models: outperform peers on revenue and margins.
Function-level productivity: 14% gains in support; 50%+ faster coding—when paired with process and guardrails.
Biopharma at scale: Moderna’s ML-driven stack enabled rapid vaccine development; Pfizer reports materially faster clinical operations with AI/ML.

Playbook: Shifting from Cost to Catalyst

Make AI a leadership discipline. Assign CEO-level ownership.
Redesign work, not just tools. Map workflows and KPIs first.
Tame AI sprawl. Standardize on interoperable platforms.
Anchor on data quality. Treat data as a product.
Engineer compliance in. Use NIST AI RMF and align to EU AI Act timelines.
Prove value early. Start with domains with proven lift: support, coding, knowledge work.

Measuring ROI in 2025

Track value at three levels:

Team: throughput & quality (tickets/hour, PR merge times).
Process: unit economics (cycle-time compression, rework rate).
Business: financial outcomes (incremental revenue, EBIT lift, churn/NPS movement).

If a proof-of-concept can’t show a leading indicator inside 90 days, stop or pivot.

Conclusion

The “two types of companies” lens is sharper than ever. Budget-cutting won’t catch up to firms that treat IT as a value engine. The winners assign executive ownership, adopt platform thinking, redesign workflows, and bake in compliance from the start.

Adoption is widespread. Impact is uneven. Advantage goes to companies that rewire how work is done.

👉 Which type are you building?

Optimal Time Allocation for IT Teams

Posted on July 22, 2025 by NicB72

Why Time Allocation Matters

One of the biggest questions IT leaders wrestle with is: how should my team spend its time?
Lean too far into operations and innovation stalls. Lean too far into projects and the business risks outages, compliance gaps, or unhappy users. The right balance drives both stability and growth.

The 50/25/25 Framework

A simple but effective baseline I’ve seen work across organizations:

50% Operations — keeping the lights on: uptime, user support, monitoring, patching, compliance, incident response.
25% Business Process Improvement (BPI) — workflow optimization, automation, cost reduction, service improvements.
25% Large Projects — ERP deployments, cloud migrations, cybersecurity enhancements.

This model ensures IT stays reliable while still investing in efficiency and transformation. It’s not rigid, but it’s a strong starting point.id — priorities shift with business needs, but the model provides a healthy baseline.

Alternative Models for Different Contexts

Every organization is different. These three models adapt the baseline to different business realities:

Balanced Growth (50/30/20): Mid-sized firms with stable ops, tilting more toward projects for growth.
Transformation-Heavy (40/45/15): Organizations in the middle of major digital change where large programs dominate.
Lean Optimization (45/25/30): Companies doubling down on automation, continuous improvement, and reducing technical debt.

The point: time allocation should shift with business maturity and priorities.

Challenges in IT Operations

Even with half the time dedicated, operations remain tough:

Legacy + new systems that don’t integrate cleanly.
Limited visibility into assets and services.
Balancing scalability with security.
Budget pressure — “do more with less.”
Rapid change in tools, practices, and skills.

The best IT leaders anticipate these challenges by building automation, resilience, and visibility into their operating model.

Making Business Process Improvement Real

Improvement work only matters if it’s measurable:

KPIs: response times, error rates, cycle times.
ROI: cost vs. benefit.
Feedback loops: employee and customer insights.
Process metrics: throughput, lead time, defect rates.

If you can’t measure it, it’s not true improvement.

Large IT Projects: Where Time Goes

Typical categories include ERP/CRM deployments, cloud or data center migrations, cybersecurity modernization, and disaster recovery. These demand cross-functional planning and strong governance to deliver.

Digital Transformation Is Continuous

Transformation is no longer “one and done.” It’s baked into every initiative:

IT uplift: modern infrastructure, cloud, security.
Digitized operations: AI, RPA, analytics.
Customer experience: seamless digital touchpoints.
Workforce transformation: training, new tools, new ways of working.

In pharma and biotech, these shifts accelerate R&D, streamline trials, and strengthen patient engagement.

Talent and Culture Matter

Time allocation isn’t just what IT does—it’s how IT works. Smart leaders carve out time for:

Upskilling on cloud, AI, and cybersecurity.
Agile practices and short delivery cycles.
Knowledge sharing across IT and the business.

Without investing in people and culture, even the best time split won’t deliver full value.

Automation and AI Are Changing the Ratios

Automation is steadily reducing the operational burden. Monitoring, patching, and incident response are increasingly automated—freeing teams to invest more in process improvement and strategic projects.
The 50/25/25 balance could become 40/30/30 in the future.

Running IT by the Numbers

Strong IT management requires metrics:

Visibility: inventories, monitoring, dashboards.
KPIs: uptime, MTTR, SLA compliance, cost per incident, project timeliness, user satisfaction.

With these in place, IT shifts from reactive firefighting to proactive strategy.

Conclusion

The 50/25/25 framework provides a healthy starting point, but it’s not a rulebook. The right mix shifts with context—growth, transformation, or optimization. Pair any model with visibility, metrics, culture, and automation, and IT leaders ensure their teams deliver stability today and transformation tomorrow.

From Pit Lane to Pipeline: Lessons in Data Strategy from McLaren F1

Posted on June 25, 2025 by NicB72

Back in the early 2000s, I had the opportunity to visit McLaren F1 and witness firsthand how they were beginning to harness vast, disparate data streams to drive performance. Even then, McLaren was ahead of the curve—integrating telemetry, tire wear, weather conditions, and driver behavior into centralized systems to inform race strategy and car development.

What struck me wasn’t just the volume of data, but how they approached it. They weren’t just collecting information—they were thinking in terms of relationships. They were connecting seemingly unrelated signals to uncover patterns and optimize outcomes. In hindsight, it was an early glimpse into what we now call semantic data modeling and real-time analytics.

At the time, I was working at a large pharmaceutical company, facing many of the same data challenges that still persist today—particularly in High Throughput Screening (HTS) and early-stage drug discovery. We were generating massive datasets from compound libraries, assay results, and lab instrumentation. But much of that data was siloed—scattered across systems, formats, and teams. Integrating and interpreting it was a major hurdle. We often relied on manual processes and intuition to connect the dots, but the complexity and fragmentation made it nearly impossible to see the full picture. We knew the insights were there—but without better tools for correlation and context, they remained locked away.

That visit to McLaren sparked a question that stayed with me: what if we could apply the same principles—centralized data integration, real-time feedback loops, and predictive modeling—to accelerate decision-making in pharma?

That experience fundamentally shaped how I began to think about knowledge management, data architecture, and the future of AI/ML in life sciences. It was a clear example of what becomes possible when data is treated not as a byproduct of operations, but as a strategic asset.

Today, we’re finally seeing that vision come to life. Advances in AI and computing power are helping bridge the gap. Natural Language Processing (NLP) and machine learning models can now extract structure and meaning from unstructured content, making it easier to unify data across formats and sources. Foundation models trained on scientific literature and experimental data can identify patterns and relationships that would be nearly impossible to detect manually. And with scalable cloud infrastructure and high-performance computing, we can process and analyze these massive datasets in real time. Together, these technologies are transforming how we approach discovery—turning once-inaccessible data into actionable insight.

AI/ML in Life Sciences: Evolution, Not Revolution

Posted on May 21, 2025 by NicB72

Introduction

AI and machine learning dominate biotech headlines. They’re pitched as silver bullets—from discovery to clinical trials. But if you’ve lived inside Pharma IT or led digital transformations, you know the truth: AI isn’t a revolution. It’s an evolution. And evolution only works when the environment is ready.

After 25+ years leading IT across Research, Development, and Commercial functions, I’ve seen the cycle repeat. The need for innovation hasn’t changed. What’s changed is the infrastructure: more compute, more data, better tools. Yet the core challenges remain.

Core Challenges

Despite the hype, success still comes down to fundamentals:

Clean, connected, governed data.
Security, permissions, and compliance.
Robust information architecture.

Without these, even the smartest model fails. No AI can paper over a broken data foundation—and the cost of ignoring this reality is wasted time, money, and trust.

Semantic Data Models: An Old Idea, a New Edge

What’s exciting today isn’t entirely new. In the early 2000s at Pfizer, I worked with ontologies and semantic data models—using triples to define relationships:

TP53 — regulates — Cell Cycle
BRCA1 — associated_with — Breast Cancer
Aspirin — inhibits — COX1

These simple triples form knowledge graphs, which are now powering drug discovery and clinical decision support.

The new capability: LLMs can now extract triples from unstructured text and align them to formal ontologies. This bridges natural language with structured knowledge, creating intuitive interfaces and advanced reasoning systems. It’s a powerful addition to the biotech data toolkit.ces to advanced reasoning systems. It’s a powerful new capability in the biotech data toolkit.

Figure: This is a high-level architecture. It shows how LLMs interact with triples, ontologies, and knowledge graphs. They support AI reasoning in life sciences.

Big Data, Content, and Governance

AI needs fuel—large, diverse, high-quality datasets. It also needs access to unstructured content: documents, reports, emails. Yet many organizations still operate with fragmented, poorly indexed data.

The tension is real: innovation vs. control. AI can expose sensitive information as easily as it can surface insights. That’s why security, governance, and trust frameworks must scale alongside AI.

Modern AI helps—NLP can extract structure, AutoML can flag quality issues—but even the best tools require a solid foundation. As one analysis put it: “Without well-organized information, AI-driven insights skew, search fails, and knowledge-sharing initiatives collapse.”

Big Pharma vs. Small Biotech

I’ve worked in both worlds. Each has strengths—and blind spots:

Big Pharma

Scale, data, mature infrastructure.
But siloed systems, legacy baggage, and cultural resistance.
Opportunity: responsibly scale AI across workflows.

Small Biotech

Nimble, cloud-native, therapeutic focus.
But limited datasets and compliance maturity.
Opportunity: build smart, unified ecosystems from the ground up.

In smaller organizations, I’ve built secure digital platforms integrating tools like Benchling, Egnyte, and Microsoft 365 to support both regulated and non-regulated domains. Agility is the edge.pport both regulated and non-regulated domains.

Risk, Opportunity, and Leadership

This moment is full of potential—but also risk. If AI is bolted onto broken foundations, it won’t scale. If organizations invest in semantic data, governance, and interoperability, AI can deliver trusted, explainable, and resilient outcomes.

That requires more than data scientists. It demands digital leadership: leaders who can align data, compliance, and innovation.

Conclusion

AI in life sciences isn’t magic. It’s the next step in a long evolution of data and systems. The winners will be those who architect for intelligence—semantic, explainable, and scalable.

This is our moment to lead. Let’s not chase hype. Let’s build the foundations that turn information into lasting advantage.

Private Conversations to Public Sharing — Why I’m Posting Again

Posted on May 19, 2025 by NicB72

After several years of staying quiet on LinkedIn and on this blog, I’ve decided to start posting again. It’s been 10 years since my last post on here.

I had to pause this blog many times in the past. This was due to varying social media policies at the different companies I worked for. And I avoided posting on LinkedIn because its changed, it’s become a mix of:

Sales pitches disguised as thought leadership
Personal stories that feel more performative than professional
Comment threads that escalate quickly

For a while, that made me hesitant to share on LinkedIn. I stuck to private conversations, industry events, and smaller circle discussions. Recently, peers and colleagues encouraged me to share those insights openly. I’ve realized there’s still a real appetite for substance over noise.

So here’s what I’m committing to:

Sharing thoughtful, experience-based insights from the Biotech/Pharma + IT/Digital space
Focusing on real-world challenges and innovations
Staying grounded, respectful, and open to dialogue

If you’re also craving more signal and less noise on your feed, I hope you’ll follow along. Better yet, join the conversation.

SharePoint 2016 – last on premise ?

Posted on May 22, 2015 by NicB72

Having managed an enterprise collaboration space for coming up to 10yrs now and taking the SharePoint journey for a lot of that I want to discuss one big change that I believe is happening over the next 2-3yrs.

Throughout the time I have managed collaboration we were always on premise. We evaluated BPOS which became O365 yearly and for multiple reasons it didn’t work for us. It fell down on functionality, reliability, cost and in the early days security concerns. The magic with large enterprise is the economies of scale plays out well so the cost can be driven down quite well on premise.

Slowly but surely O365 has been closing the functionality gap and although its not closed completely there are now some functional components that make it to O365 first. ie. Oslo/Delve/OfficeGraph.

Reviewing all the material from Ignite and the commentary across the blogosphere we truly believe that SharePoint 2016 will be the last on premise version. When we were planning our SharePoint 2013 upgrade we talked about this at length internally and came to the conclusion as well.

Hybrid offerings means that those companies that are still concerned about certain data can keep that on premise and have the general collaboration and social elements in O365. It looks like one experience to the user.

Hybrid Search now works and can take advantage of the power of Azure and Password Hash, etc. has resolved the authentication and security concerns of the past.

So should you install SharePoint 2016 next year when it is released. In my opinion No. Or at least not at any scale. Instead start your business case now for a pilot in year 2016 of O365 (Exchange, Office, Yammer and SharePoint). With a plan to move full scale in late 2016 or early 2017 to O365 for the majority including social, people profiles and search.

Use the remaining 2015 and 2016 to…

1) evaluate your content with content owners and Information Security to work out what content should be on premise and what can go to the cloud – include legal obviously !

2) Get the other functions, Exchange(Mail), Office Desktop and Social (Yammer) on board. This is a change for all and will create an hollistic collaboration environment and done right and together will accelerate your return on investment,

SharePoint fails

Posted on January 11, 2015 by NicB72

When I attend conferences I hear all the time from other attendees that they have SharePoint at their company but it 1) doesn’t work well, 2) nobody uses it or 3) everyone hates it and hates IT for pushing it on them !

I seem to be in the minority of those companies who have SharePoint and its working very well. So over a drink with a few attendees we discussed why and why not SharePoint succeeds and the findings can be applied to any enterprise software offerings.

1) SharePoint is an infrastructure project, leave it to IT.

Wrong. SharePoint is a end user product, it affects the way you collaborate, share information, find information, find people, socialise, present data, etc.

Yep there is software installation and server build out, if on premise and integration if going Office365 but just putting it out there and sending users a URL is going to fail.

At my company we did several things

Built a governance group of business folk, not IT Business partners (although they were represented as they are also a user), but real business folk. Empowers the business lines and ensures the roadmap is aligned to the business need not hindering it.
Business Sponsor and IT Sponsor. A business sponsor was key to show this was not just another IT solution. Of course it also involved changing the way IT worked and so an IT sponsor was also required. Plus there is large ongoing IT investment therefore a senior IT lead was essential.
Created a forum where anyone interested in Collaboration could attend. Held monthly, a virtual meeting which has 1000’s of attendees. Creates an ownership culture
Power Users and Champions – yep no surprise there. These folk started to appear within around 3months of our initial pilot back in 2007. Over the years some have remained and new ones have joined that list. These folk are used to present with us the collaboration offerings to their business line. They present at our forums and they blog and update our wiki on the collaboration services.

2) SharePoint is not Intuitive

Again I hear all the time that SharePoint is difficult to use and it should be as easy as an iPad. Okay, lets stop a minute. An iPad is a very different kettle of fish.

I do however agree that SharePoint UI is not as easy as it should be and takes a little longer to get used to. However this is because it is trying to get enterprise users to move from the long established content mgmt and lets face it fileshares to a new way of working. We have seen the UI change from SP2007 through SP2010 and SP2013 and it is getting better but there will always be some need for education,

So what did we do. Well…

Ready, Set, Go program – with each new release and for anyone new to SharePoint we created an online program where people could get acclimated with SharePoint. It is a mix of wiki material, one-pagers (in 15 languages), PowerPoints and Video snippets on our internal Youtube solution (built on SharePoint !).
Collaboration Forum – we also used our monthly collaboration forum to get one of our power users to show a feature of sharepoint.
Use Social – we post a tip of the week on our social feeds (internal twitter and our blog) highlighting a feature. Sometimes this comes from ‘How To’ questions submitted to our support group and sometimes its suggested by a user or a team member
Site Visits – Throughout the year members of the collaboration team are at various sites visiting and discussing collaboration with governance members, power users and customers. We use this opportunity to provide training drop in sessions. Where anyone can pop along, ask a question, show us how they use SharePoint or tell us whats wrong with it !
Traditional training – and lastly we do offer traditional classroom training. Some groups still want this and often newly formed teams find it a great way to accelerate their collaboration.

One thing we didn’t do was change the UI heavily. The only thing we did was reduce the number of templates, ban SharePoint Designer from our main collaboration offering and add a custom footer. More on the custom footer in a separate post but briefly it provides Site Information, support information and analytic data to us for planning and forecasting.

Set dumb expectations

Keep the buzz going

Lastly…

Its critical to see SharePoint as part of a bigger end to end Knowledge, Collaboration and Information Strategy for your company. If you end up with SharePoint being used in just some parts of the org and competitors in another its going to devalue your investment and lead to customer confusion and dissatisfaction.

You need to think about how people will find the content they put in, think tagging, taxonomy, folksonomy and the search experience.

You need to think about how to identify the critical content and how to remove/deprioritise the stale/inactive content.

SharePoint is a journey and having been able to keep Collaboration on SharePoint now for over 8yrs at my company has been critical. That strong user network has grown with it and learnt it together. We havent done the traditional thing which is change product every 2-4yrs just because something new is out there !

Office Assistants (more like Cortana and Google Now and less like Clippy)

Posted on January 5, 2015 by NicB72

I need Help!

With Siri, Google Now, Cortana, Easily.Do we see a new series of little helpers to assist us in getting through the day !

Siri and Cortana are part of the listening family of helpers or the Phone A Friend brigade if you remember those gameshows. You ask them a question and they go out and try and find an answer. Sometimes they do it well and sometimes they don’t ! They are getting better as they take context, location and the history of the questions into account.

Google Now and Easily.Do are part of a different family of helpers. They look through locations you tell them, e.g. facebook, email, calendars, linkedin, etc. and they parse together your day. They fill in the gaps so if you have 2 meetings at different locations they will give you travel information and any weather detail that might affect you.

In the travel space there are many apps that also fill in gaps, tripit is a good example. It will take all those emailed itinearies and flight, train, hotel bookings, etc. and stitch them together. Again giving you warning on how long it will take you to get from place A to place B and some other useful data along the way.

Some things are missing though and most of this has been focussed on the consumer space. So here are my thoughts for bringing this into the enterprise.

Clippy (Office Assistant)

Before we do though lets remind ourselves of Clippy. In 1996 Microsoft introduced its office assistant Clippy (later renamed to Clippit). It came with Microsoft Office and other Microsoft products, looked like a paperclip and tried to understand what you were trying to do and tried to help.

It was seen as intrusive and annoying.

Welcome to the future – hmmm

So looks like we have gone round circle and the idea of assistants is back with us. It is not really a big surprise. The amount of information that is thrown our way has grown, we are more mobile and the technology has progressed both in processing capability, location awareness and voice recognition.

So what do we need from this new wave of assistants to help us in the workplace.

1) The assistant needs to know me – It needs to understand my role in the organisation, where I fit in the organisation (reporting place, etc.), it needs to understand the purpose of my division/group, etc. – this is critical in providing relevant information and creating connections. As an example if I am searching for information on aeroplane wings then the results presented to someone from Research or Design would be different to the results presented to someone from Finance as we can deduce they are after different things.

2) It needs to know who I work with – not by me telling it but by it learning from my emails, blog posts, content I work on, the sites I visit, my meetings, etc. – having this data again gives the assistant context. If my work tasks for the day have items such as purchase new machine for tablet pressing and all my searches are around machinery then the assistant can intelligently present me results before I even begin a search and can bring me relevant data such as cost and procurement approach before I even ask.

3) It needs to know what I am working on – again by looking at my emails, meetings, task lists, search queries, etc.

4) It needs to know where I am and where others are – locational data is critical to give contextual information. If I am visiting a location in NYC from out of town and am have a meeting on a particular topic then the assistant should tell me of other people who are in the same location who are useful to me. Those people may be located there OR they may also be visiting like myself.

I see an assistant that becomes an integral part of my life and lives across devices and OS’s. It is always watching my email, calendar and tasklist, monitors my other activities such as location, phone calls I make, blogs, content I tag, and is aware whats happening outside in the world 🙂

My Working Day

When I start the day it already presents my day (think Easily.Do and Google Now). My day broken up into little cards/components detailing everything I have planned. It glues it together with other contextual information such as weather and travelling details.

It also provides me information to complete my tasks. The assistant reviews the detail, pulls relevant information together, maybe background on attendees for meetings, details on what we last worked on together, maybe detail where we match in our profiles, for those tasks it pulls everything including process info and people who have been through the process recently and can help me.

I have a personal assistant in my pocket/hand that is one step ahead of me pulling info for me. If there are traffic issues making travel to my next appointment impossible it is scheduling a video conference or telepresence without me having to tell it. If a new attendee gets added to the meeting I get the information on that person to review before the meeting. If new information comes up at the last minute that is critical to the meeting, again it presents it to me in a timely manner and formated to the device I am using and my current context.

Did I mention I can speak to it and ask it about my day, whats up next, what do I need to know, etc. and it talks back to me. Not just presenting that data textually but talking to me when its relevant – again it knows what communication mechanism works based on the context of who I am and where I am.

Millennials

Posted on November 1, 2014 by NicB72

Millennials are the big buzz word of 2014. Just watch this bloomberg video if you don’t believe me. (youtube link)

First lets define Millennials (wikipedia).

They were born 1980-2000 (ish).
This means that they grew up with the internet (I can remember a time before it and dial up connections !)
They grew up with Wifi, youtube, facebook and myspace and with mobile technology.
During their formative years the number of television channels boomed to over 250,
They find information coming to them from every direction,
They don’t visit libraries or read magazines or newspapers as much as previous generations but instead get their information from wikipedia, their social channels and information aggregators.

So what is this all about and why should we care.

1) Well in 10yrs millennials will account for 50% of the workforce according to wikipedia, Forbes, etc.

2) Millennials have grown up with technologies such as social networks, smartphones and the flexibility to use whatever device or app they want.

3) Millennials are known for living in the moment and having huge dreams. More millennials than any previous generation are starting up their own companies.

4) It is expected that Millennials will move continually and on average work for around 10-15 companies in their working lifetime. Lets assume a 30yr career, 25-55yrs old, thats a move every 2-3yrs. Blind loyalty to a company is gone and their driver is interesting, rewarding work.

So for a company to attract and retain millennials the current workplace and tools we impose on them won’t work. Companies need millennials as they are the next gen workforce and of course they will become the buyers of products when the current generation retires/moves on. Its essential to employee this generation, get them money to pump into the economy.

So what needs to change. Well lots, 81% of millennials feel they should be able to set their own hours, and around 80% believe they should be able to wear jeans or whatever they want. Thats a change from the traditional 9-5 worktime and the suit and tie brigade. They also feel the way they progress and the way they are rewarded should be based on value and delivery and not time spent in the office, etc.

So lots of cultural stuff needs to change. I am going to focus though on the technology side.

Millennials change apps on their smartphones very frequently. They have no love of a particular app for long and its more about how the app gets the job done.

They also don’t care much for corporate rules restricting applications they can use and how they can use those applications with corporate data.

Social is key to them. Getting a job completed is about working with others and not the traditional team where a project manager or general manager will construct the team. Millennials will construct their own teams to get the work done and then disband and move on to the next task.

So what do we have so far.

Flexibility – use whatever app, whenever, wherever,
Abstraction and Integration – they expect the data they need to work on to be available anywhere and to be accessible from any of the apps they use,
They expect strong social tools to be available and not constrained by corporate redtape,

Yammer, Chatter, etc

Posted on July 30, 2014 by NicB72

Today you have to choose as an enterprise if you are in the Yammer camp, the Chatter camp or one of the other similiar locked down tools. What frustrates me is that a Chatter conversation cannot mix with a Yammer conversation and vice-versa. We are expected that all the people we need to socialise for work will be on the same tool. By the way this is the same in the consumer social space, Google+ and Facebook don’t mix and match either.

But imagine that you had a cellphone and you could only talk to people on the same carrier as you. Frustrating hey. You’d have to either convince everyone to join AT&T or whoever OR you would have to have a phone for each carrier !

Of course you can phone anybody on any carrier on any network on any device anywhere in the world.

We need the same open-standards for social especially in the corporate space. B2B, B2C, etc. means we do not have control on the tools each person and each company will have and we cannot be constrained in our ability to function, collaborate and progress by the tools.

When will we see open-standards for social. I am not holding my breath. The open standards movements of the 90’s that led to HTML and other advances seems to have given way to closed-walled gardens. These closed-walled gardens are great for the powerful vendors as they lock you into their system. Apple does it with their AppStore ecosystem, Microsoft with their SharePoint/Yammer space, Salesforce with theirs.

I hope for change but suspect it won’t come from these big companies but instead from some small startup that works out the magic sauce to get these to interact. Of course we will then have to watch out for the lawyers and patent trolls. Hopefully the start up will survive.