You’ve earned a reputation as an acclaimed IAM leader and advocate for women within tech. How did you get here?

My career began in 1996 when a friend suggested I look into a career in IT, because that’s where the future would be. These were the days when we were still mostly using computers for word processing and the odd email. After graduating in computer science I found an engineering role at Cisco Systems. When I say engineering, I was going under floor tiles in banks and data centers, doing cabling and configuring routers and switches.

It was a male-dominated world and I felt pretty isolated. This was less because I was the only female in the team, and more due to the perception that “It can’t be possible that a woman is doing this kind of work.” It didn’t matter how many accreditations I gained. These kinds of views were a real barrier to my ability to win the confidence of the team through my abilities.

As a twenty-something wanting to be successful in my career, this created a strong need for encouragement and support, as well as a desire to change the perception that women can’t contribute in these kinds of roles. I started doing talks at schools, encouraging the younger generation to join this very exciting area of technology. I also fronted Cisco marketing campaigns, hoping to provide a positive role model for women. Later on, I started a peer group called Lean In Women in Technology. Initially the idea was to have a circle for women to get support and perhaps offer some mentoring and advice for younger women as well. And that grew into a 300-strong band of women who were also looking for support and guidance, and a place to talk about the challenges we often face in our careers.

Once I’d got over these hurdles myself, I moved into technology-focused management roles. I started delivering entire life cycles for routing and switching products, which led me in a systems and security direction. For example, I helped roll out CyberArk within Deutsche Bank. Following on from that, I moved into the identity nexus space, which is where I’ve been for the past ten years or so, leading large-scale transformation for banks and corporates. My focus has been on accelerating these transitions, including at NatWest where I’m currently working.

How have attitudes to women in technology changed during your career?

When I was at university there was never a question about whether I could do the work. Yet the culture within workplaces created a barrier to women like me earning the team’s confidence and trust. Perhaps in the last seven or eight years, I feel we have moved on from that era. Finally, we’re able to just get on with the work, because everybody has a valuable contribution to make.

Last year, my son was sitting next to me doing some homework while I was in a meeting on Teams. He pointed at the screen and said, “Mum, there’s only one guy in that meeting, everybody else is a woman.” I said, “Oh, yeah, that’s true.” And he gave me the thumbs up and said “Well done, Mum. That’s all your work.” Well, it was nice to hear that, even though it’s not the case!

How does this change in attitudes manifest within the programs that you lead?

The way in which projects are delivered within organisations tends to be very fear-driven. I try to neutralise that. I don’t want my team to be fearful, because that’s when people don’t speak, that’s when we don’t know what’s on others’ minds. If people suppress their concerns, that has a direct impact on the project.

I try to instill the values of being loving, creative, and inspirational. I want to build these values into everyone’s way of thinking when they work with me. Being loving means being passionate about your work and aspiring to do things with kindness. Being creative means bringing your ideas and your experience, your best offerings to solve problems. And being inspirational means your voice, your value matters. How are you inspiring your team, and the organisation, to think about things in a better way?

Once I get past these psychological barriers with team members, I find they are a lot more fluid, more relaxed. Their ideas and contributions really become evident. What I’ve seen throughout my career is that a directive approach often fails to achieve the desired result. My goal is to have skilled people who are passionate about what they do, and for them to say, “This is the right way to build this, based on my experience.” And then you get the outcomes you’re looking for.

I believe women’s voices are key to enabling this. We come from a loving, creative place. When you exercise those aspects, you see teams delivering because they want to, not because they’ve been told to. That’s probably where I’ve made the biggest contribution. Lots of people do program management, but to do it excellently, it’s about an attitude. It’s not something you can train, and it’s not specific to any particular technology.

It’s well-known that communication is a big part of change management. What you just described sounds more like culture, which can be even more powerful.

It is. What I’ve observed is that people often want to deliver. They want to bring their skills and experiences, but they’re kind of drowned out by this very mechanical approach to delivering a project. When you have one-to-ones with people, you understand that they don’t want to be told what to do. They want the dignity to do something in the way they know is the right way.

You talked about fear within organisations. There’s a lot of fear around AI right now. Do you see this in the teams you work with, and how should organisations respond?

The reality is that automation and headcount reduction are goals for many organisations. They will leverage AI where possible to do this. Five years ago, much of the work in IAM or IGA (Identity Governance and Administration) was still manual, such as submitting forms that are processed by a help desk or awarding team, who then have to provision this into the tool. We’re moving away from that. These processes are being automated, meaning the number of roles in these teams will be reduced.

Alongside this, we’ll see a shift in how teams deliver value. There will be a premium on lateral thinking, emotional intelligence and managing politics, which are not things AI can address. IAM and IGA are still very much about people, as well as process. Organizations need to think about how these roles should evolve, and tie in with new AI capabilities. Perhaps it will be more about managing digital workers to do things successfully. But the higher cognitive aspect of what we do will continue.

Presumably removing humans from the loop would lead to negative security outcomes. After all, AI is clearly far from infallible.

Absolutely. Whilst AI can provide better tooling, there’s still a human lens that always needs to be applied. We need to link these new capabilities back to the human element. We can’t lose that intelligent, lateral thinking aspect.

What do the new, AI-enhanced IAM and IGA workflows look like?

Running a discovery process using AI tools and workflows can get through a great deal of work, a lot faster. For example, you can probably scan 3,000 applications and determine what data is being collected within a few hours. But we still need a human to ask, “Okay, how can this be validated? What do I need to do to make sure the data is confirmed as accurate?”

Another area where AI is going to give us a great improvement in turnaround is on roles and application entitlements. It can take years to carry out a review and process all that data, going back to the business endlessly to collect more information and check it. AI is showing great ability to provide insight in these areas, for example identifying where role or entitlement groups can be simplified. Again, you always need a human to validate any proposed changes.

Technology is shaking up the London insurance market. The industry features a labyrinthine value chain and a patchwork of legacy systems. Working practices have been slow to evolve, particularly outside of retail markets like home, travel and auto insurance.

Last night’s discussions at Claims 2.0, hosted by InsTech, shed light on how platforms like Guidewire and Reserv are enabling the London market to become more agile and data driven, albeit still in pockets.

At the same time, face-to-face trading is very much in evidence, with many underwriters still passing paper slips across the floor of Lloyd’s of London.

Legacy IT, and the perceived risks of technology change, are ongoing barriers. I heard one story about a mission-critical server, through which billions of value flowed, that was (is still?) hosted ‘in a cupboard’, with staff resorting to spraying the building’s roof with water in a bid to prevent the server from igniting during a heatwave.

The risk of business disruption is becoming more manageable, with AI now enabling large data migrations to be completed within weeks rather than months. Everyone agrees AI will transform business processes, enabling providers to simultaneously deliver accuracy, efficiency and customer service, goals that previously needed to be traded off.

That said, the human element is still the decisive factor, highlighting the importance of effective change management. For example, how can providers inject data and AI into workflows in a way that loss adjusters are willing and able to use?

I took part in an interesting discussion about what it will take to leverage multi-agent systems to automate claims processing. Given the number of parties involved in the value chain, agents are going to need the ability to establish trust, and share trusted data, across organizational and geographic boundaries. I’m embarking on a new project investigating how this can be achieved - message me or connect if you’d like to explore use cases for AI agents operating across trust boundaries in insurance, financial services, healthcare, manufacturing or government.

Services like Nadia, an AI coach used by staff at companies like Costa Coffee, Coca-Cola, Prudential, Experian and WPP, are democratising access to workplace coaching, a benefit once reserved for senior leaders (if available at all).

A typical use case is a large retailer makes Nadia available to frontline managers. Store managers often feel isolated, with recent research highlighting the growing strain of dealing with customer conflict. With Nadia, managers can discuss their challenges and concerns with an AI trained in their workplace context.

What determines whether such tools win and retain our trust?

Privacy and control appear to be pre-requisites for adoption. As a frontline manager or team member, I want to know that the content of my coaching conversations and/or usage history are not available to the company, or my line manager.

User experience is another non-negotiable. “Using AI should feel like working with a teammate”, Tom Lawrence of MVPR told me recently (for context, we were discussing his agency’s in-house AI platform). In a coaching scenario, I’d expect any tool to understand my workplace setting and give me useful feedback and actionable advice, such as role playing and personalized development plans. An approachable / empathetic conversational style would encourage me to keep coming back.

Explainability is often discussed in relation to AI, yet it’s unclear whether employees care how a tool works. As long as it is seen as helpful and supportive, staff may be happy to accept that it’s a black box. However, explainability seems likely to come under the spotlight in the event a workplace dispute (or worse) is traced back to advice given by an AI coach.

Of course, the need to establish trust in workplace AI begins with employers, who must balance benefits like employee development, engagement and retention against potential risks such as privacy breaches. A high-profile slip-up could stem uptake or even cause reputational damage.

Commercial considerations are another factor. While staff want to know that personal information is not shared with their employer, employers need to justify the investment in AI tools and measure ROI. Providing aggregated and anonymized metrics (and potentially, organisations publicising these internally) should help build and maintain trust on both sides.

Linked to this, a degree of transparency about model training, performance monitoring and safety controls should also enhance trust.

The bigger picture of how AI is used across an organization is also likely to come into play. For example, use of AI in performance reviews (even if informal, for example via line managers using ChatGPT) could lead to a sense of loss of agency and undermine confidence in AI coaching.

My conversations with the Valence team revealed a thoughtful approach to these and other questions. Their impressive client roster and uptake metrics (plus a successful $50 million raise) suggest they’re on the right track.

AI coaching & workplace trust: key takeaways

Privacy guarantees are non-negotiable. Uptake of AI workplace coaching and similar tools is likely to depend on employees being offered explicit, strong data protections

Trust will be context-specific: People may trust AI in developmental or advisory roles, but are likely to be less keen about being evaluated by AI

Demand for transparency and explainability could grow, particularly in the event of disputes linked to AI generated advice, or if a debate emerges about the influence of AI coaching on performance and/or workplace relationships

Neil Fletcher has over 30 years of experience in M&A and international corporate tax advisory, including 16 years as a Tax Partner at PwC and latterly as a Managing Director at Alvarez and Marsal. His recent work includes advising on multi-billion dollar acquisitions and working in the Fintech and cryptocurrency sectors.

How is AI infiltrating the consulting sector today? Is this mainly via formal adoption of Enterprise AI tools, or through use of public models like ChatGPT?

In both ways. The public models are rapidly increasing in capability, and becoming seriously useful. Does that mean people are feeding confidential information into open source models? I don’t think so.

Personally, I’m using both public and custom models. When I upload the OECD Pillar 2 (the international global minimum corporate tax framework) documents into a general LLM, the accuracy of answer is usually less than fifty percent initially. I then increase the level of instruction until eventually it finds the answer. Even then I don’t necessarily trust it, as it does beg the question as to whether the model is only reflecting back the specificity I am entering into it.

Overall, it's very useful in research and generation, though human expertise is still vital.

Recently, people have come back to me suggesting this or that tool designed more specifically for what I want to do. You can see the benefits of these tools. It's beginning to demonstrate the investment case more clearly.

What does the business case for AI look like for a large scale consulting firm?

The easy answer is, as the accuracy goes up then costs go down. Any professional services firm is in danger of being on the outside looking in, always chasing the AI tail as the technology marches on. The current focus of Agentic AI seems to be on relatively routine workflows.

For consultants, it's probably a case of identifying use cases today to demonstrate where it can go in the future.

It may be worth reflecting on what professional services means in an AI context. A lot of people see the ‘secret sauce’ of professional services as being specialist knowledge. However, this may develop into leveraging enormous data sets to innovate new products and services. Pure knowledge management and data retrieval could be self-limiting.

How do you see AI impacting the professional services industry going forwards?

The number one question from junior people in professional services organisations is, Am I still going to have a job? I can see how A2A (Agent To Agent) workflows are going to have an impact. There are some products and services that will either no longer be needed, or will be performed by more relevant organisations.

As a professional of any level of experience you want to be there to shape what comes next. That will only happen if you’ve acquired the foundational knowledge on how these tools work.

Lawyers, accountants, engineers et cetera all need training on how to work with AI, but I don’t yet see a very structured way of building capability. There doesn't seem to be a better time to invest in acquiring these skills than today.

You recently commented on an article on LinkedIn describing AI as an existential threat to the Big 4 consulting giants including PwC, where you were a partner for 16 years. The article concludes “Do you still need a Big 4 firm to compete, or do you need better prompts?" Your response was “If there are no Big 4 Partners doing the Big 4 Partner job then how can “Act as a Big 4 partner” prompt work?” Can you expand on that?

What’s underpinning my comment is, they're perhaps diminishing what an experienced professional, such as a Big 4 partner does.

The linear process of how to do things faster or cheaper is not even debatable. The point is, how do you move forward so you can realise the value of your work for clients? This is a forward-looking aspect that a backward-looking prompt that says “Act as a Big 4 partner [has historically acted]” can never capture.

The delivery model doesn’t determine the outcome. Why would you just produce the same reports quicker, now you can do all these new things?

I’ve been reading about the concept of ‘operating rails’, whereby AI platforms offer to take over certain categories of decisions on customers’ behalf with bounded liability, based on their ability to predict outcomes using big data. Is that the sort of thing you have in mind?

Absolutely. For instance, risk transfer is fundamental to the financial services industry, and it can only be done if you can put numeric values on things. The pensions industry, to take one example, became widely accessible with the advent of data on morbidity, and so on.

Pattern recognition might have been done by a human super-expert in the past. Now it’s done with AI. Professional services firms have this huge flow of information going through their people and systems. The data itself is the real source of value, particularly when it’s curated by domain experts.

To take it to the next level, we need to be looking to create new, trusted tools using this data. You can only do that by bringing together people from different disciplines, which is why I find the current debate on LinkedIn so valuable.

What value do you personally get from LinkedIn?

I’m steeped in the international tax environment and I’ve written articles on LinkedIn that dive into the topic in some depth. I also participate in ongoing dialogue on international tax fora.

The responses I've had to technical articles on LinkedIn have predominantly been from other domain experts such as in-house tax, tax advisors and academics. It tends to be similar people that respond every time.

These days I’m more likely to participate in broader discussions, albeit from a taxation perspective. LinkedIn is doing a great job of enabling a public discourse about AI. It’s a huge topic that a lot of people have on their minds. People with very different backgrounds and agendas are coming together and having respectful, informed discussions in a way that goes beyond trying to sell your services. A lot of high quality content is being shared.

I’m in an ongoing dialogue with a variety of people on LinkedIn, including in the US and Asia. Where else would I be able to meet and have one-to-one conversations with former leaders at Google, for example?

You tend to engage on LinkedIn at least once a week. How much of this is premeditated, versus spontaneous engagement for the joy of joining the conversation?

It’s a mixture. I’m not generally thinking about impressing potential clients—my engagements tend to be bespoke and relationship driven.

In my experience, on LinkedIn you are able to get much broader inputs and a wider perspective. There’s a willingness to listen to people outside one’s own domain, with less emphasis on protecting a particular patch. AI is coming at a frightening rate of knots. Participating in the conversation builds your confidence, as many people are facing similar challenges, and are open to sharing them.

Which types of professional services firms do you think will win, or at least survive, the race for AI?

One route is about compliance, productivity and workflow automation. The other is about product and service innovation. It’s about moving forward with a mindset that’s open to both.

I believe the traditional professional services model still has a vital place, based on these firms’ proficiency with data. It’s not purely about defensive action. For example, AI will enable niche consultancies to invent new product categories and become the market leader, without needing to be the biggest firm on the planet.

What’s your advice for those working in the consulting industry today, or looking to enter it?

These new products and services will need people to build them. Learn the tools now, so you’re around to take it forward.

There will always be a need for the human element. Notwithstanding the speed of developments, human oversight of the models is essential as there will always be exceptions and imperfections in the data.

I recently spoke to Tom Lawrence, founder of MVPR, a team of senior PR practitioners with an agentic platform they've built in-house, called the PR Operating System, supporting execution. They're both a services and a product company, in that they deliver a full PR service to some clients while others license the PR Operating System.

What does the PR Operating System do, and how did it come about?

The platform provides a space for transparent collaboration and execution of all activities you’d traditionally expect to get from an in-house PR team or external PR agency, like tracking relevant journalists and managing email exchanges, using data to develop PR strategies and content, or speeding up execution of proactive and reactive PR work.

We started by looking for basic efficiencies, like how to turn a 30 minute task into a 15 minute one. You don’t necessarily need AI for that, it can often be done via a simple workflow automation. What you do need is a thorough understanding of how things are done currently.

We put a lot of effort into analysing our processes, which enabled us to spot opportunities for improvement. At the same time we realised it was vital to develop a clean data architecture.

Can you give us an idea of what that looks like?

Each major capability in our product now operates through a dedicated microservice which exposes APIs designed to excel at a singular function. This could be finding relevant journalists, analysing campaign objectives, or orchestrating email delivery.

What makes this architecture powerful is the protocol we’ve built on top of it. Our system of AI agents uses this to decide which API to call based on the workflow it’s been assigned. This gives our operating system highly granular control, with each step in an agentic workflow requesting exactly the data it needs from the right microservice.

Designing this kind of system isn’t easy, but now that it’s in place it provides huge flexibility and means we develop new products and features faster, because each part of the system is modular and reusable. It also enables full transparency with our clients, which is a huge unlock. For example, having clean data enables us to attribute all the way down the funnel.

Was selling the platform as a product part of the plan from Day One?

As users ourselves, we immediately know if a new feature saves us time. We always knew if we built something that works for us, there would be potential to offer it to clients.

How does owning your own platform impact your business model?

When we started MVPR we used to sell clients a set number of hours per month. Using the PR Operating System, we found we were meeting the client’s brief in a third of the allotted time.

We’re fully transparent about how services are delivered. Early on, a client challenged us that we hadn’t used their full allocation of hours, even though we’d fully delivered on the brief. On that occasion we gave the client a refund. Then we thought, let’s move to output based billing.

How is adoption progressing?

As with any new way of working, you need to motivate people to adopt it. PR people are problem solvers, we're great at coming up with solutions under pressure. We’ve found the most effective approach is to impose time constraints, then provide the tools that enable people to adapt.

In-house teams love working with the platform, however agencies have been slower to adopt. MVPR’s business model is different to the hourly rate model, and we’ve found that agencies can struggle to adapt their model.

Educating the market is an ongoing challenge. Most people think AI-assisted PR is a next year thing but if you can do it today, why wouldn’t you use this?

Trust in AI is in flux right now. How do you contend with that?

No one will use an agentic system they don’t trust.

User interactions are what builds trust. Using the AI should feel like working with a teammate.

The ability to deliver this experience comes down to the quality of the discovery work and the constraints built into the system. The team designing it needs to know “At this point in the workflow, client approval is needed” or “More context is needed here”.

What advice can you offer other professional services firms looking to augment human workflows with AI?

The best applications of AI are bottom-up. You need people who are close enough to the process to know it in intricate detail. However, they don’t necessarily have the experience to articulate the real problem or opportunity.

So you start with the leaders in a business and go deeper with the why - why do you want this?

The data transformation part is critical. It’s painstaking work, but if you don’t have it, applying AI to your data won’t work. Most companies probably need to bring in external expertise to get this done.

Any final thoughts?

We’re four years into building MVPR and two years into the journey of implementing AI. It’s a bit like driving into the fog. You know what’s behind you, and you can see what’s immediately ahead, but not further into the distance.

The biggest problem for most of the incumbents in our industry is that they didn’t get started two years ago. Now the advantages of applying AI to clean data are compounding. The gap between us and incumbents is significantly larger than it was two years ago.

What I’ve Learnt About (B2B) Trust

To answer this, I looked back over some projects I supported B2B clients on over the past few years.

As I went through this exercise (summarised below), it became clear that trust was often at the heart of the client’s challenge.

Retailer

Objective: Deliver a consistent customer experience at partner-owned sites.

Challenge: Little power to dictate standards due to fear of partners defecting.

Solution: Invest in education and incentives for partners and staff.

Parts manufacturer

Objective: Better (digital) management of the distributor sales channel.

Challenge: Distributors were suspicious, seeing digitisation as an attempt to gain visibility of their customers.

Solution: Create a mutual win by enabling distributors and outlets to easily track orders, earn points and redeem rewards.

Equipment manufacturer

Objective: Capitalise on explosive growth in data centres.

Challenge: The data centre ecosystem saw them as a telecoms specialist.

Solution: Thought leadership to ‘own’ an emerging trend at the intersection of both sectors.

Logistics company 1

Objective: Drive shipping volume without diluting brand positioning.

Challenge: E-commerce platforms control the customer experience, and were unwilling to share data.

Solution: Thought leadership targeting enterprise-grade platforms, showing how tight integration enables a premium customer experience.

Logistics company 2

Objective: Shift from selling services to selling subscriptions to a new SaaS product.

Challenge: Customers were unaware of the product and sceptical about AI-enabled inventory planning.

Solution: Build awareness and credibility by putting the company’s domain experts in the spotlight.

Logistics company 3

Objective: Offer specialised services and move up the value chain.

Challenge: Building trust with customers in regulated sectors is painstaking and slow.

Solution: Use existing tools and warehouse equipment to enable customers to verify process adherence without the need for video surveillance of staff.

Standards body

Objective: Drive uptake of industry standards for materials provenance and sustainability reporting.

Challenge: Suppliers were wary of trade secrets leaking.

Solution: Design a proof-of-concept enabling industry participants to share facts and proofs rather than raw data using open technology standards.

Trade association

Objective: Increase adoption of new technology standards.

Challenge: Scepticism and misconceptions surrounding the underlying technologies.

Solution: Focus on myth-busting, analyst relations and highlighting the value for specific industries.

IT services provider

Objective: Establish a compelling digital presence and drive lead generation.

Challenge: New company with low market awareness.

Solution: Position the CEO and communicate their expertise through consistent, visible thought leadership.

Looking down this list of projects some recurring themes and trust-building patterns jump out:

• Lack of trust is the single biggest barrier to digital transformation

• Education and incentives are needed to drive adoption of digital solutions

• This starts with engaging internal and external stakeholders, and making the business case

• Open technologies including verifiable credentials and Privacy Enhancing Technologies (PETs) have an important role to play in accelerating supply chain digitisation

• Creative use of existing tools can also enhance trust

Some of these projects identified technical requirements such as identity verification or sharing cryptographic proof of origin or compliance, to enable process automation or boost trust in existing processes.

Nevertheless, success ultimately hinged on building human trust - whether with staff, customers, partners or the wider industry.

My key takeaway is that trust building is a multi-faceted, ongoing endeavour encompassing stakeholder engagement, technology enablement, aligned incentives and visible leadership.

The payoff is faster, more successful digital transformation.

The mosquito has been buzzing around my head half the night. Sometime before dawn a storm passes overhead. We’re in the mountains, and it's a biggie. Peals of lightning split the night. Thunderclaps continue to ring in my ears after the storm moves on. Combined with the low-level itch from a crop of recent bites, sleep is elusive. Finally I whip the sheet off and turn on the light. Time to get this newsletter started.

Trust in digital services (a.k.a. digital trust) probably isn't a topic that keeps you awake at night. But worries about whether that drop-shipped product you ordered will ever arrive, or whether you should listen to the tai chi guru who keeps popping up in your feed? Tick.

It's becoming ever harder to know who or what is real online, whether that's a proposal you suspect was generated in seconds by feeding your LinkedIn profile to ChatGPT, an SMS message purportedly about a missed delivery, or an image depicting a world leader in an uncompromising situation.

Almost everyone I ask recently has been scammed online, or only escaped by a hair's breadth. Meanwhile an unrelenting wave of data breaches and identity fraud has woken us up to the fact that once our data is in the hands of less-than-trustworthy actors, it’s out there for good.

I’m struck by the parallels with last night’s experience. Worrisome digital experiences are an irritant, like a mosquito buzzing around your head, until a fraudulent transaction or malicious account takeover opens your eyes to the dangers that were present all along, like lightning revealing a darkened landscape.

The stakes are escalating. Personal AI agents promise remarkable and useful experiences, but only if we’re willing to entrust them with an unprecedented amount of personal information, and the power to (trans)act on our behalf. In the business arena, data collaboration is vital for process automation, supply chain resilience, sustainability and compliance, yet business partners won’t play ball if they worry about trade secrets leaking. Meanwhile the spectre of massive job cuts hangs over corporate AI experiments.

Digital trust has always been an existential issue for technology vendors. Your product or service might enable businesses to solve a problem 10x faster but if your pitch comes across as more hype than substance, or lacks clarity on key topics like the use and retention of customer data, you won’t get the chance to prove it.

The speed at which AI is advancing makes efforts to improve digital trust ever more urgent. My intention for this newsletter is to find out what digital trust success (and failure) looks like, by speaking with those on the frontline of tackling the trust gap.

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